New Results

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

Basavaraj Vastrad, Chanabasayya Vastrad
doi: https://doi.org/10.1101/2022.04.29.489994

Abstract

Bipolar disorder (BD), also known as psychiatric disorder, affects millions of people all over the world. The aim of this investigation was to screen and verify hub genes involved in BD as well as to explore potential molecular mechanisms. The next generation sequencing (NGS) dataset GSE124326 was downloaded from the Gene Expression Omnibus (GEO) database, which contained 480 samples, including 240 BD and 240 normal controls. Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. A protein–protein interaction (PPI) network and module analysis were used to identify hub genes. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were also constructed. Receiver operating characteristic curve (ROC) analysis was used to validate hub genes. In this work, 957 DEGs, including 477 up regulated and 480 down regulated, were obtained from NGS data. The GO and pathway enrichment analyses of the DEGs showed that the up regulated genes were enriched in the neutrophil degranulation, immune system, transport, cytoplasm and enzyme regulator activity, and the down regulated genes were enriched in extracellular matrix organization, diseases of metabolism, multicellular organismal process, cell periphery and metal ion binding. Finally, through analyzing the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, we screened hub genes UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 by the Cytoscape software. The current investigation illustrates a characteristic NGS data in BD, which might contribute to the interpretation of the progression of BD and provide novel biomarkers and therapeutic targets for BD.

Introduction

Bipolar disorder (BD) is a psychiatric disorder characterized by recurrent manic or hypomanic and depressive episodes [1]. According to the report of World Health Organization (WHO), BD is the sixth cause of disability-adjusted life years among all psychiatric diseases [2]. The numbers of cases of BD are rising worldwide and it has become an important mental health concern. It is estimated that the incidence of BD was 30% to 69% in Europe and in the United States [3]. The risk factors associated with BD such as obesity [4], anxiety [5], depression [6], cognitive dysfunction [7], pregnancy [8], hypertension [9], cardiovascular diseases [10], diabetes mellitus [11] and genetic factor [12]. A numerous tests are available for screening and detecting BD; all, however, have their disadvantages [13]. Therefore, an in depth understanding of molecular pathogenesis of BD is great significance.

Studies have shown the role of genes and signaling pathway in the pathogenesis of BD. In recent years, several biomarkers were widely used to identify BD patients, such as CACNA1C [14], BDNF (brain derived neurotrophic factor) [15], GSK3 (glycogen synthetase kinase-3) [16], DUSP6 [17] and SYNE1 [18]. Several signaling pathways were involved in pathogenesis of BD such as kynurenine signaling pathway [19], Wnt and GSK3 signaling pathways [20], cAMP--CREB signaling pathways [21] PI3K/AKT/HIF1-a signaling pathway [22], MAP kinase and phosphoinositide signaling pathway [23], and Notch signaling pathway [24] were responsible for advancement of BD. Therefore, we aimed to further explore the pathogenesis of BD and identify specific molecular targets.

Next generation sequencing (NGS) [25] and bioinformatics analysis [26] are widely used in the investigation of the molecular mechanism of various diseases. NGS data are usually deposited and available in free public websites, such as the NCBI-Gene Expression Omnibus database (NCBI-GEO) (https://www.ncbi.nlm.nih.gov/geo) [27]. Integrated bioinformatics analyses of NGS data derived from investigation of BD could help identify the hub genes and further demonstrate their related functions and potential therapeutic targets in BD.

In the current investigation, NGS data of GSE124326 [28] from NCBI-GEO was downloaded. A total of 240 BD samples and 240 normal control samples were available. Differentially expressed genes (DEGs) between BD and normal control samples were filtered and obtained using the online tool DESeq2. Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed using the g:Profiler. The functions of the DEGs were further assessed by PPI network and modular analyses to identify the hub genes in BD. Moreover, miRNA-hub gene regulatory network and TF-hub gene regulatory network of the hub genes were established, miRNAs and TFs were selected. The diagnostic roles of hub genes were analyzed using receiver operating characteristic curve (ROC) analysis. These add a basis for enhanced understanding of molecular basis of BD pathology and effective pharmaceutical targets.

Materials and methods

Data resources

The NGS dataset GSE124326 [28] based on GPL16791 Illumina HiSeq 2500 (Homo sapiens) was acquired from the Gene Expression Ominibus (GEO) database. The GSE124326 dataset contained 240 BD samples and 240 normal control samples.

Identification of DEGs

DESeq2 package of R software [29] was used to analyze the DEGs between PD and normal control in the NGS data of GSE124326. The adjusted P-value and [log FC] were calculated. The Benjamini & Hochberg false discovery rate method was used as a correction factor for the adjusted P-value in DESeq2 [30]. The statistically significant DEGs were identified according to adjusted P-value < 0.05, [logFC] > 0.235 for up regulated genes and [logFC] < −0.52 for down regulated genes. The DEGs are presented as volcano plot and heat map generated using ggplot2 and gplot in R Bioconductor.

GO and pathway enrichment analyses of DEGs

g:Profiler (http://biit.cs.ut.ee/gprofiler/) [31] was used to perform GO functional and REACTOME pathway enrichment analyses. GO (http://www.geneontology.org) [32] annotation was applied to define gene functions in three terms: biological process (BP), cellular component (CC) and molecular function (MF). REACTOME (https://reactome.org/) [33] is a pathway database resource for understanding high-level biological functions and utilities. P < 0.05 was considered as statistically significant.

Construction of the protein-protein interaction (PPI) and module analysis

In order to obtain interacting proteins related to DEGs, the STRING version 11.5 database (https://string-db.org/) was used [34]. Cytoscape (http://www.cytoscape.org/) (version 3.9.1) [35] was used to visualize the PPI network of DEGs. The Network Analyzer plug-in was used to explore hub genes, and the hub genrs were generated using node degree [36], betweenness [37], stress [38] and closeness [39] methods. The intersect function was used to identify the hub genes. The PEWCC1 [40] was used to search modules of the PPI network and the default parameters (Degree cutoff ≥10, node score cutoff ≥0.4, K-core ≥4, and max depth=100.) were set in the functional interface of Cytoscape.

miRNA-hub gene regulatory network construction

MiRNA regulates gene expression under defined disease conditions through interaction with hub genes during the post transcriptional stage was analyzed. We applied miRNet database (https://www.mirnet.ca/) [41] to integrate miRNA databases (TarBase, miRTarBase, miRecords, miRanda (S mansoni only), miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, ADmiRE, and TAM 2.0.). We visualized miRNA-hub gene regulatory network by employing Cytoscape software [35].

TF-hub gene regulatory network construction

TF regulates gene expression under defined disease conditions through interaction with hub genes during the post transcriptional stage was analyzed. We applied NetworkAnalyst database (https://www.networkanalyst.ca/) [42] to integrate TF database (ENCODE). We visualized TF-hub gene regulatory network by employing Cytoscape software [35].

Receiver operating characteristic curve (ROC) analysis

The hub genes were used to identify biomarkers with high sensitivity and specificity for BD diagnosis. The ROC curves were plotted and area under curve (AUC) was calculated separately to evaluate the performance of each model using the R packages “pROC” [43]. A AUC > 0.8 indicated that the model had a good fitting effect.

Results

Identification of DEGs

GSE124326 was selected and underwent DEGs analysis using “DESeq2” package in R software. There was a total of 957 DEGs between BD and normal control samples, including 477 up regulated DEGs and 480 down regulated genes (Table 1). A volcano plot was constructed for the DEGs and is presented in Fig. 1. The DEGs are presented by a heat map in Fig. 2.

Fig. 1.
Fig. 1.

Volcano plot of differentially expressed genes. Genes with a significant change of more than two-fold were selected. Green dot represented up regulated significant genes and red dot represented down regulated significant genes.

Fig. 2.
Fig. 2.

Heat map of differentially expressed genes. Legend on the top left indicate log fold change of genes. (A1 – A240 = normal control samples; B1 – B240 = BD samples)

View this table:
Table 1

The statistical metrics for key differentially expressed genes (DEGs)

GO and pathway enrichment analyses of DEGs

To obtain a deeper insight into the biological functions of DEGs, GO annotation and REACTOME pathway enrichment analyses were performed. The enriched GO terms were shown in Table 2. In the present investigation, up regulated genes were mainly enriched in transport (BP), localization (BP), cytoplasm (CC),intrinsic component (CC),enzyme regulator activity (MF) and protein binding (MF). Down regulated genes were mainly involved in multicellular organismal process (BP), developmental process (BP), cell periphery (CC), plasma membrane (CC), metal ion binding (MF) and cation binding (MF). The up regulated and down regulated genes from the REACTOME pathway enrichment analysis are shown in Table 3. The up regulated genes from the REACTOME pathway enrichment analysis were those for the neutrophil degranulation and immune system. The most down regulated genes from the REACTOME pathway enrichment were for extracellular matrix organization and diseases of metabolism.

View this table:
Table 2

The enriched GO terms of the up and down regulated differentially expressed genes

View this table:
Table 3

The enriched pathway terms of the up and down regulated differentially expressed genes

Construction of the PPI and module analysis

Based on the information in the STRING database, the hub nodes with higher node degree, betweenness, stress and closeness were screened (Table 4). The PPI network contained 4360 nodes and 9761 edges (Fig.3). UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 were the hub genes with the highest values of topological parameters (node degree, betweenness, stress and closeness). Furthermore, the 2 significant modules were extracted from the PPI network. Module 1 contained 71 gene nodes, including RPL39, RPL31, RPL23, RPL35A, RPL11, RPS24, UBB and RPL34 with 434 edges (Fig. 4A). Functional enrichment analysis of the hub genes in this module was mainly related to cellular responses to stress, axon guidance, metabolism, transport, localization, cytoplasm, immune system and protein binding. Module 2 contained 70 gene nodes, including FZD4, MSTN, EGR3, NOTCH3, PAX8, SFRP5 and WNT4 with 132 edges (Fig. 4B). Functional enrichment analysis of the hub genes in this module was mainly related to multicellular organismal process, developmental process, signaling by receptor tyrosine, diseases of metabolism and cell periphery.

Fig. 3.
Fig. 3.

PPI network of DEGs. Up regulated genes are marked in green; down regulated genes are marked in red

Fig. 4.
Fig. 4.

Modules selected from the DEG PPI between patients with BD and normal controls. (A) The most significant module was obtained from PPI network with 71 nodes and 434 edges for up regulated genes (B) The most significant module was obtained from PPI network with 70 nodes and 132 edges for down regulated genes. Up regulated genes are marked in green; down regulated genes are marked in red

View this table:
Table 4

Topology table for up and down regulated genes

miRNA-hub gene regulatory network construction

To predict the hub genes for the miRNAs, we used independent online analytical tool (miRNet). The miRNA-hub gene regulatory network contained 2378 nodes, including 2090 miRNAs and 288 hub genes, and 11478 edges (Fig. 5). TUBB2A that was modulated by 206 miRNAs (ex; hsa-mir-8085), BCL2L1 that was modulated by 179 miRNAs (ex; hsa-mir-6735-5p), UBE2D1 that was modulated by 84 miRNAs (ex; hsa-mir-548ap-5p), UBB that was modulated by 81 miRNAs (ex; hsa-mir-132-3p), RPS24 that was modulated by 79 miRNAs (ex; hsa-mir-27a-3p), CAV1 that was modulated by 115 miRNAs (ex; hsa-mir-4514), EPHA2 that was modulated by 90 miRNAs (ex; hsa-mir-3133), ERBB3 that was modulated by 53 miRNAs (ex; hsa-mir-4328), MYH11 that was modulated by 50 miRNAs (ex; hsa-mir-643) and WNT4 that was modulated by 42 miRNAs (ex; hsa-mir-6749-3p) and are listed in Table 5.

Fig. 5.
Fig. 5.

Target gene -miRNA regulatory network between target genes. The purple color diamond nodes represent the key miRNAs; up regulated genes are marked in green; down regulated genes are marked in red.

View this table:
Table 5

miRNA -target gene and TF -target gene interaction

TF-hub gene regulatory network construction

To predict the hub genes for the TFs, we used independent online analytical tool (NetworkAnalyst). The TF-hub gene regulatory network contained 582 nodes, including 336 TFs and 246 hub genes, and 6606 edges (Fig. 6). BCL2L1 that was modulated by 115 TFs (ex; HMG20B), RPL23 that was modulated by 108 TFs (ex; ATF3), TUBB2A that was modulated by 59 TFs (ex; SMARCE1), UBB that was modulated by 59 TFs (ex; NCOR1), RPL26 that was modulated by 49 TFs (ex; POLR2A), EPHA2 that was modulated by 91 TFs (ex; STAT3), FOXA1 that was modulated by 79 TFs (ex; RARA), ERBB3 that was modulated by 54 TFs (ex; KLF4), FLNC that was modulated by 53 TFs (ex; HIC1) and CAV1 that was modulated by 24 TFs (ex; ESRRA) and are listed in Table 5.

Fig. 6.
Fig. 6.

Target gene -TF regulatory network between target genes. The yellow color triangle nodes represent the key TFs; up regulated genes are marked in green; down regulated genes are marked in red.

Receiver operating characteristic curve (ROC) analysis

ROC curve analyses were performed to verify the hub genes, and area under the curve (AUC) values were calculated. The area under the curve (AUC) values of UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 were 0.902, 0.907, 0.920, 0.948, 0.940, 0.942, 0.917, 0.922, 0.915 and 0.905, respectively (Fig.7). These result indicated that hub genes could be candidate biomarkers for BD diagnosis.

Fig. 7.
Fig. 7.

ROC curve analyses of hub genes. A) UBB B) UBE2D1 C) TUBA1A D) RPL11 E) RPS24 F) NOTCH3 G) CAV1 H) CNBD2 I) CCNA1 J) MYH11

Discussion

The BD is the most common psychiatric disorder worldwide. Although numerous advances have been made in the treatment of BD, the prognosis has remained poor. Therefore, it is crucial to elucidate the molecular mechanism of BD for understanding of the disease progression to develop novel therapeutic targets. Due to the rapid advancement of NGS technology, bioinformatics analysis might contribute to identifying the DEGs and functional pathways involved in the BD.

In this investigation, NGS dataset was selected to identify the DEGs between BD and normal control samples. As a result, 957 DEGs including 477 up regulated and 480 down regulated genes were identified. Wang et al. [44] and Joshi et al. [45] showed altered expression of HBG1 and S100A8 in cardiovascular diseases. Lu et al [46] reported that S100A8 promoted cognitive dysfunction. S100A8 [47] was altered expression in obesity and might serve as a potential prognostic biomarker of obesity. S100A8 [48] and KLHL40 [49] plays an emerging role in pregnancy. The expression of S100A8 [50] is altered in diabetes mellitus.

GO and REACTOME pathway enrichment analysis were conducted to demonstrate interactions of the DEGs. Immune system [51], axon guidance [52], metabolism [53], diseases of metabolism [54] and neuronal system [55] were lined with advancement of BD. Altered expression of SLC6A9 [56] and RHD (Rh blood group D antigen) [57], CHRFAM7A [58], ANXA3 [59], SLC1A5 [60], KCNH2 [61], HP (haptoglobin) [62], SELENBP1 [63], SNCA (synuclein alpha) [64], TGM2 [65], PINK1 [66], B2M [67], QPCT (glutaminyl-peptide cyclotransferase) [68], CBS (cystathionine beta-synthase) [69], NQO2 [70], GLRX5 [71], BASP1 [72], GAS7 [73], GPX1 [74], OLIG2 [75], RPTN (repetin) [76], IL33 [77], SOX10 [78], GRIK1 [79], ZFPM2 [80], SHANK3 [81], ERBB3 [82], ARC (activity regulated cytoskeleton associated protein) [83], GFAP (glial fibrillary acidic protein) [84], PLAT (plasminogen activator, tissue type) [85], GRIK5 [86], CACNB2 [87], NRXN2 [88], TERT (telomerase reverse transcriptase) [89], GNB3 [90], L1CAM [91], EGR3 [92], CAV1 [93], CACNA1B [94], MAGI1 [95], KIR2DL1 [96], PAH (phenylalanine hydroxylase) [97] and CYP3A5 [98] have been shown in schizophrenia. Recent studies showed that SLC6A9 [99], FKBP1B [100], S100A12 [101], SLC6A19 [102], TXN (thioredoxin) [103], TLR9 [104], HP (haptoglobin) [105], ARG1 [106], PINK1 [107], B2M [108], C5AR1 [109], MYADM (myeloid associated differentiation marker) [110], CBS (cystathionine beta-synthase) [111], GPX1 [112], SIAH2 [113], PRDX2 [114], RDH8 [115], CYP11B2 [116], RARRES2 [117], NOX1 [118], IL33 [119], OTC (ornithine transcarbamylase) [120], CYP1A1 [121], NFATC4 [122], TSLP (thymic stromal lymphopoietin) [123], WNT4 [124], MGP (matrix Gla protein) [125], FGFBP1 [126], GHR (growth hormone receptor) [127], ERBB3 [128], GFAP (glial fibrillary acidic protein) [129], CCDC40 [130], CACNB2 [131], CD34 [132], NOTCH3 [133], TERT (telomerase reverse transcriptase) [134], GNB3 [135], TP73 [136], RYR2 [137], ENPEP (glutamyl aminopeptidase) [138], SCN7A [139], WNK4 [140], SFRP5 [141], GDF15 [142], CAV1 [143], KCNA5 [144], FOXC1 [145], ASIC1 [146], VASH2 [147], CXCL8 [148], PAPPA2 [149], KCNMA1 [150], LOX (lysyl oxidase) [151], SPARCL1 [152] and CYP3A5 [153] might have the potential to be used as diagnostic biomarkers of hypertension. Previous studies have reported that RHD (Rh blood group D antigen) [154], S100A12 [155], TXN (thioredoxin) [156], TLR9 [157], S100P [158], TAGLN2 [159], S100A9 [160], CA1 [161], HP (haptoglobin) [162], RPL39 [163], F5 [164], PINK1 [165], B2M [166], S100A11 [167], SLC4A1 [168], CBS (cystathionine beta-synthase) [169], AHSP (alpha hemoglobin stabilizing protein) [170], F12 [171], EPHB4 [172], NFE2 [173], VRK1 [174], GPX1 [175], FOXA1 [176], CYP11B2 [177], NOX1 [178], IL33 [179], NPHS1 [180], OTC (ornithine transcarbamylase) [181], SULF1 [182], CYP1A1 [183], DCN (decorin) [184], ADAMTS7 [185], WNT4 [186], LAMA4 [187], SCN4A [188], CACNB2 [189], GNB3 [190], ENPEP (glutamyl aminopeptidase) [191], WNK4 [192], FOXC1 [193], PAX8 [194], ROBO1 [195], CXCL8 [196], PAPPA2 [197], LOX (lysyl oxidase) [198], NOSTRIN (nitric oxide synthase trafficking) [199], MUC16 [200], MIOX (myo-inositol oxygenase) [201], CYP11A1 [202] and CYP3A5 [203] are involved in the pregnancy complications. Modification in the activity and expression of RHD (Rh blood group D antigen) [204], S100A12 [205], TLR9 [206], ANXA3 [207], S100P [208], TAGLN2 [209], S100A9 [210], KCNH2 [211], HP (haptoglobin) [212], SELENBP1 [213], TANGO2 [214], PINK1 [215], TFR2 [216], B2M [217], LTBP2 [218], PGLYRP1 [219], HRH2 [220], CLEC5A [221], PLSCR4 [222], S100A11 [223], PPBP (pro-platelet basic protein) [224], RAP1GAP [225], CBS (cystathionine beta-synthase) [226], RBM38 [227], MYL4 [228], EPHB4 [229], TNNT1 [230], KBTBD7 [231], GPX1 [232], SIAH2 [113], FOXO4 [233], PRDX2 [234], MAF1 [235], KANK2 [236], SFRP2 [237], BGN (biglycan) [238], HSPB7 [239], ABCG8 [240], CYP11B2 [241], RARRES2 [242], NOX1 [243], CPE (carboxypeptidase E) [244], IL33 [245], OTC (ornithine transcarbamylase) [120], CYP1A1 [246], LRIG3 [247], GJA1 [248], NFATC4 [122], SEMA3F [249], CDH11 [250], DCN (decorin) [251], TSLP (thymic stromal lymphopoietin) [252], ADAMTS7 [253], C1QTNF1 [254], SFTPB (surfactant protein B) [255], WNT4 [256], MGP (matrix Gla protein) [257], SHANK3 [258], ALOX12B [259], MYBPHL (myosin binding protein H like) [260], GHR (growth hormone receptor) [261], ERBB3 [262], PLAT (plasminogen activator, tissue type) [263], ADAMTS2 [264], CACNB2 [265], DAB2IP [266], CD34 [267], COL15A1 [268], MSTN (myostatin) [269], NOTCH3 [270], HSPG2 [271], TERT (telomerase reverse transcriptase) [272], GNB3 [273], MMP15 [274], COL4A2 [275], EGR3 [276], RBM20 [277], RYR2 [278], EPHA2 [279], NDRG4 [280], UNC5B [281], CTNNA3 [282], SFRP5 [283], CUX2 [284], GDF15 [285], AXL (AXL receptor tyrosine kinase) [286], CAV1 [287], KCNA5 [288], FOXC1 [289], RYR1 [290], ROBO1 [291], XIRP2 [292], FZD4 [293], VASH2 [294], CXCL8 [295], KCNMA1 [150], LOX (lysyl oxidase) [296], DNAH11 [297], FMOD (fibromodulin) [298], MFAP4 [299], FLNC (filamin C) [300], LIFR (LIF receptor subunit alpha) [301], MAGI1 [302], SLC39A2 [303] and CYP3A5 [304] are linked to cardiovascular diseases. Studies have also reported that CHRFAM7A [58], ACHE (acetylcholinesterase (Cartwright blood group) [305], HP (haptoglobin) [306], SELENBP1 [63], BASP1 [72], CHRNA1 [307], RPTN (repetin) [76], IL33 [308], TACR1[309], SHANK3 [310], ALOX12B [311], HPN (hepsin) [312], GFAP (glial fibrillary acidic protein) [84], CACNB2 [313], MYT1 [314], NOTCH3 [315], TERT (telomerase reverse transcriptase) [316], GNB3 [317], EGR3 [318], BSN (bassoon presynaptic cytomatrix protein) [319], CUX2 [320], GDF15 [321], CXCL8 [322], MAGI1 [95] and PAH (phenylalanine hydroxylase) [323] are necessary for BD development. S100A12 [324], CDH1 [325], S100A9 [326], ANK1 [327], KCNH2 [328], HP (haptoglobin) [329], FRMD4A [330], SNCA (synuclein alpha) [331], FBXO7 [332], PINK1 [333], B2M [334], KCNH3 [335], CA2 [336], FUZ (fuzzy planar cell polarity protein) [337], UBB (ubiquitin B) [338], C5AR1 [339], CBS (cystathionine beta-synthase) [340], F12 [341], TSPAN5 [342], NQO2 [343], NDUFA1 [344], SRXN1 [345], BASP1 [346], GPX1 [347], OLIG2 [348], EFHC2 [349], FOXA1 [350], PAX4 [351], BGN (biglycan) [352], IL33 [353], LRIG3 [354], GJA1 [355], SHANK3 [356], ARC (activity regulated cytoskeleton associated protein) [357], GFAP (glial fibrillary acidic protein) [358], UNC13A [359], MSTN (myostatin) [360], HSPG2 [361], TERT (telomerase reverse transcriptase) [362], GNB3 [363], L1CAM [364], CALB1 [365], RYR2 [366], MYO15A [367], MYH11 [368], GDF15 [369], CAV1 [370], KIRREL3 [371], COBL (cordon-bleu WH2 repeat protein) [372], LOX (lysyl oxidase) [373], SPARCL1 [374], FLNC (filamin C) [375], TMPRSS4 [376], VWA2 [377], OGDHL (oxoglutarate dehydrogenase L) [378], CYP3A5 [379] and FBXO40 [380] were revealed to be associated with cognitive dysfunction. S100A12 [381], SLC6A19 [382], TXN (thioredoxin) [383], TLR9 [384], S100P [385], S100A9 [386], ANK1 [387], CA1 [388], HP (haptoglobin) [389], ARG1 [390], SNCA (synuclein alpha) [391], STARD10 [392], PINK1 [393], TFR2 [394], B2M [395], PHOSPHO1 [396], CBS (cystathionine beta-synthase) [397], WNT6 [398], NFE2 [399], RNF10 [400], CARM1 [401], GLRX5 [402], PPP2R5B [403], GPX1 [404], FOXO4 [405], ARHGEF12 [406], FOXA1 [407], PAX4 [351], ABCG8 [408], RARRES2 [409], NOX1 [410], CPE (carboxypeptidase E) [411], IL33 [412], ADAMTS7 [413], SFTPB (surfactant protein B) [255], MGP (matrix Gla protein) [257], VTN (vitronectin) [414], PRSS1 [415], GHR (growth hormone receptor) [416], ERBB3 [417], GFAP (glial fibrillary acidic protein) [418], CD34 [419], MSTN (myostatin) [420], NOTCH3 [421], NR5A2 [422], SMOC1 [423], GNB3 [424], CTNNA3 [425], SFRP5 [283], GDF15 [285], AXL (AXL receptor tyrosine kinase) [426], CAV1 [427], LCT (lactase) [428], FZD4 [429], KCNMA1 [430], FMOD (fibromodulin) [431] and CPA6 [432] were revealed to be correlated with disease outcome in patients with diabetes mellitus. Previous studies have demonstrated that SLC6A19 [433], DNAJC6 [434], TXN (thioredoxin) [383], S100A9 [435], HP (haptoglobin) [436], ARG1 [437], SNCA (synuclein alpha) [391], CEP19 [438], CAMP (cathelicidin antimicrobial peptide) [439], PINK1 [440], UBB (ubiquitin B) [441], PHOSPHO1 [396], CBS (cystathionine beta-synthase) [397], PPP2R5B [403], GPX1 [442], PRDX2 [443], LXN (latexin) [444], RGS6 [445], MAF1 [446], HSPB7 [447], PNLIP (pancreatic lipase) [448], NOX1 [410], CPE (carboxypeptidase E) [411], IL33 [449], SP7 [450], ZFPM2 [451], TSLP (thymic stromal lymphopoietin) [123], TACR1 [452], WNT4 [453], MFAP5 [454], GHR (growth hormone receptor) [455], GFAP (glial fibrillary acidic protein) [456], ACTN3 [457], MSTN (myostatin) [420], GNB3 [458], MMP15 [459], KCP (kielin cysteine rich BMP regulator) [460], WNK4 [461], SFRP5 [283], GDF15 [462], CAV1 [427], LCT (lactase) [428], CXCL8 [463], LOX (lysyl oxidase) [464], AIF1L [465] and CYP3A5 [466] are linked with the development mechanisms of obesity. Recent studies have proposed that the DNAJC6 [467], DNAJB2 [468], GPR4 [469] and ZFPM2 [80] are associated with Parkinson Disease. Vauthier et al [434], Cushion et al [470], Barone et al [471], Marcoli et al [472], Qin et al [473], de Nijs et al [474], Bergareche et al [475], Hu et al [476], Liu et al [477], Zhang et al [478], Gorman et al [479], Cherian et al [480], Gururaj et al [481], Belhedi et al [482] and Park et al [483] demonstrated that DNAJC6, TUBB2A, DPM2, SMOX (spermine oxidase), CDYL (chromodomain Y like), EFHC1, SCN4A, DOC2A, ADGRV1, SCAMP5, CACNA1B, KCNT1, KCNT2, CPA6 and CYP3A5 could induce epilepsy. These enriched genes might play essential roles in the advancement of BD and act as novel diagnosis biomarkers or treatment targets of BD.

Construction of PPI network and its modules of DEGs might be helpful for understanding the relationship of developmental BD. Thus, we speculated that UBE2D1, TUBA1A, RPL11, RPS24, CNBD2, CCNA1, RPL31, RPL23, RPL35A and RPL34 might be important hub genes in BD development.

miRNA-hub gene regulatory network and TF-hub gene regulatory network analyses predicted hub genes, miRNAs and TFs. hsa-mir-27a-3p [484], STAT3 [485], KLF4 [486] and ESRRA (estrogen related receptor alpha) [487] were reported to be associated with the prognosis of cognitive dysfunction. Altered expression of ATF3 [488], STAT3 [489] and KLF4 [490] are observed in hypertension, indicating that these biomarkers play a role in hypertension. ATF3 [491], NCOR1 [492], STAT3 [493] and KLF4 [494] have been positively correlated with cardiovascular diseases ATF3 [495], STAT3 [493] and ESRRA (estrogen related receptor alpha) [496] were previously reported to be critical for the development of diabetes mellitus. Ku et al. [497], Su et al. [498], Redonnet et al. [499], Deng et al. [500] and Larsen et al [496] concluded that ATF3, STAT3, retinoic acid receptor, alpha (RARA), KLF4 and ESRRA (estrogen related receptor alpha) were an important participant in obesity. Previous studies have reported that STAT3 [501] and retinoic acid receptor, alpha (RARA) [502] are related to schizophrenia. STAT3 [503] levels are correlated with pregnancy complications. Novel biomarkers include BCL2L1, RPL26, hsa-mir-8085, hsa-mir-6735-5p, hsa-mir-548ap-5p, hsa-mir-132-3p, hsa-mir-4514, hsa-mir-3133, hsa-mir-4328, hsa-mir-6749-3p, HMG20B, SMARCE1, POLR2A and HIC1 might play important roles in the development of BD and act as early diagnosis biomarkers or treatment targets of BD. Therefore, our further study will investigate the interactions of hub genes, miRNA and TFs to shed new light on the molecular mechanisms involved in BD pathology.

In conclusion, the present study provides significant information that might help in understanding the molecular mechanisms of BD. We identified several essential genes that are potentially linked with the development of BD using bioinformatics analyses of DEGs between patients with BD and normal controls. These genes and their pathways will further our understanding of BD etiology, and help improve diagnosis, prevention, and treatment. Our findings suggest that the up regulated expression of UBB, UBE2D1, TUBA1A, RPL11 and RPS24, and the down regulatied of expression of NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 can be considered candidate biomarkers or therapeutic targets for BD. Further studies are needed to confirm our putative finding.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

No informed consent because this study does not contain human or animals participants.

Availability of data and materials

The datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE124326) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE124326)]

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author Contributions

B. V. - Writing original draft, and review and editing

C. V. - Software and investigation

Acknowledgement

I thank Roel A Ophoff, UCLA, Center for Neurobehavioral Genetics, Los Angeles, CA, USA, very much, the author who deposited their NGS dataset GSE124326, into the public GEO database.

References

  1. 1.↵
    Duan J, Yang R, Lu W, Zhao L, Hu S, Hu C. Comorbid Bipolar Disorder and Migraine: From Mechanisms to Treatment. Front Psychiatry. 2021;11:560138. doi:10.3389/fpsyt.2020.560138
    OpenUrlCrossRef
  2. 2.↵
    Leboyer M, Kupfer DJ. Bipolar disorder: new perspectives in health care and prevention. J Clin Psychiatry. 2010;71(12):1689–1695. doi:10.4088/JCP.10m06347yel
    OpenUrlCrossRefPubMedWeb of Science
  3. 3.↵
    Baca-Garcia E, Perez-Rodriguez MM, Basurte-Villamor I, López-Castromán J, Fernandez del Moral AL, Jimenez-Arriero MA, Gronzalez de Rivera JL, Saiz-Ruiz J, Leiva-Murillo JM, de Prado-Cumplido M, et al. Diagnostic stability and evolution of bipolar disorder in clinical practice: a prospective cohort study. Acta Psychiatr Scand. 2007;115(6):473–480. doi:10.1111/j.1600-0447.2006.00984
    OpenUrlCrossRef
  4. 4.↵
    Prillo J, Soh JF, Park H, Beaulieu S, Linnaranta O, Rej S. Obesity and metabolic comorbidity in bipolar disorder: do patients on lithium comprise a subgroup? A naturalistic study. BMC Psychiatry. 2021;21(1):558. doi:10.1186/s12888-021-03572-w
    OpenUrlCrossRef
  5. 5.↵
    Saccaro LF, Schilliger Z, Dayer A, Perroud N, Piguet C. Inflammation, anxiety, and stress in bipolar disorder and borderline personality disorder: A narrative review. Neurosci Biobehav Rev. 2021;127:184–192. doi:10.1016/j.neubiorev.2021.04.017
    OpenUrlCrossRef
  6. 6.↵
    Lazary J, Pogany L, De Las Cuevas C, Villasante-Tezanos GA, De Leon J. Adherence to psychiatric medications: Comparing patients with schizophrenia, bipolar disorder and major depression. Neuropsychopharmacol Hung. 2021;23(4):363–373.
    OpenUrl
  7. 7.↵
    Vanderplow AM, Eagle AL, Kermath BA, Bjornson KJ, Robison AJ, Cahill ME. Akt-mTOR hypoactivity in bipolar disorder gives rise to cognitive impairments associated with altered neuronal structure and function. Neuron. 2021;109(9):1479–1496.e6. doi:10.1016/j.neuron.2021.03.008
    OpenUrlCrossRef
  8. 8.↵
    Sharma V, Sharma P, Sharma S. Managing bipolar disorder during pregnancy and the postpartum period: a critical review of current practice. Expert Rev Neurother. 2020;20(4):373–383. doi:10.1080/14737175.2020.1743684
    OpenUrlCrossRef
  9. 9.↵
    Ayerbe L, Forgnone I, Addo J, Siguero A, Gelati S, Ayis S. Hypertension risk and clinical care in patients with bipolar disorder or schizophrenia; a systematic review and meta-analysis. J Affect Disord. 2018;225:665–670. doi:10.1016/j.jad.2017.09.002
    OpenUrlCrossRefPubMed
  10. 10.↵
    Van Rheenen TE, McIntyre RS, Balanzá-Martínez V, Berk M, Rossell SL. Cumulative Cardiovascular Disease Risk and Triglycerides Differentially Relate to Subdomains of Executive Function in Bipolar Disorder; preliminary findings. J Affect Disord. 2021;278:556–562. doi:10.1016/j.jad.2020.09.104
    OpenUrlCrossRef
  11. 11.↵
    McIntyre RS, Konarski JZ, Misener VL, Kennedy SH. Bipolar disorder and diabetes mellitus: epidemiology, etiology, and treatment implications. Ann Clin Psychiatry. 2005;17(2):83–93. doi:10.1080/10401230590932380
    OpenUrlCrossRefPubMed
  12. 12.↵
    Sanches M, Quevedo J, Soares JC. Antidepressants, genetic risk, and the prevention of bipolar disorder. Braz J Psychiatry. 2020;42(5):461–462. doi:10.1590/1516-4446-2020-0016
    OpenUrlCrossRef
  13. 13.↵
    Bayes A, Parker G, Paris J. Differential Diagnosis of Bipolar II Disorder and Borderline Personality Disorder. Curr Psychiatry Rep. 2019;21(12):125. doi:10.1007/s11920-019-1120-2
    OpenUrlCrossRef
  14. 14.↵
    Shonibare DO, Patel RR, Islam AH, Metcalfe AWS, Fiksenbaum L, Freeman N, MacIntosh BJ, Kennedy JL, Goldstein BI. Neurostructural phenotypes of CACNA1C rs1006737 in adolescents with bipolar disorder and healthy controls. Prog Neuropsychopharmacol Biol Psychiatry. 2021;104:110071. doi:10.1016/j.pnpbp.2020.110071
    OpenUrlCrossRef
  15. 15.↵
    Schröter K, Brum M, Brunkhorst-Kanaan N, Tole F, Ziegler C, Domschke K, Reif A, Kittel-Schneider S. Longitudinal multi-level biomarker analysis of BDNF in major depression and bipolar disorder. Eur Arch Psychiatry Clin Neurosci. 2020;270(2):169–181. doi:10.1007/s00406-019-01007-y
    OpenUrlCrossRef
  16. 16.↵
    Jiménez E, Arias B, Mitjans M, Goikolea JM, Roda E, Ruíz V, Pérez A, Sáiz PA, García-Portilla MP, Burón P, et al. Association between GSK3β gene and increased impulsivity in bipolar disorder. Eur Neuropsychopharmacol. 2014;24(4):510–518. doi:10.1016/j.euroneuro.2014.01.005
    OpenUrlCrossRef
  17. 17.↵
    Kim SH, Shin SY, Lee KY, Joo EJ, Song JY, Ahn YM, Lee YH, Kim YS. The genetic association of DUSP6 with bipolar disorder and its effect on ERK activity. Prog Neuropsychopharmacol Biol Psychiatry. 2012;37(1):41–49. doi:10.1016/j.pnpbp.2011.11.014
    OpenUrlCrossRefPubMed
  18. 18.↵
    Li W, Yang Y, Luo B, Zhang Y, Song X, Li M, Lv L.Association of SYNE1 locus with bipolar disorder in Chinese population. Hereditas. 2019;156:19. doi:10.1186/s41065-019-0095-7
    OpenUrlCrossRef
  19. 19.↵
    Poletti S, Myint AM, Schüetze G, Bollettini I, Mazza E, Grillitsch D, Locatelli C, Schwarz M, Colombo C, Benedetti F.Kynurenine pathway and white matter microstructure in bipolar disorder. Eur Arch Psychiatry Clin Neurosci. 2018;268(2):157–168. doi:10.1007/s00406-016-0731-4
    OpenUrlCrossRef
  20. 20.↵
    Muneer A. Wnt and GSK3 Signaling Pathways in Bipolar Disorder: Clinical and Therapeutic Implications. Clin Psychopharmacol Neurosci. 2017;15(2):100–114. doi:10.9758/cpn.2017.15.2.100
    OpenUrlCrossRef
  21. 21.↵
    Gaspar L, van de Werken M, Johansson AS, Moriggi E, Owe-Larsson B, Kocks JW, Lundkvist GB, Gordijn MC, Brown SA. Human cellular differences in cAMP--CREB signaling correlate with light-dependent melatonin suppression and bipolar disorder. Eur J Neurosci. 2014;40(1):2206–2215. doi:10.1111/ejn.12602
    OpenUrlCrossRef
  22. 22.↵
    Campbell I, Campbell H. Mechanisms of insulin resistance, mitochondrial dysfunction and the action of the ketogenic diet in bipolar disorder. Focus on the PI3K/AKT/HIF1-a pathway. Med Hypotheses. 2020;145:110299. doi:10.1016/j.mehy.2020.110299
    OpenUrlCrossRef
  23. 23.↵
    Ludtmann MH, Boeckeler K, Williams RS. Molecular pharmacology in a simple model system: implicating MAP kinase and phosphoinositide signalling in bipolar disorder. Semin Cell Dev Biol. 2011;22(1):105–113. doi:10.1016/j.semcdb.2010.11.002
    OpenUrlCrossRefPubMed
  24. 24.↵
    Hoseth EZ, Krull F, Dieset I, Mørch RH, Hope S, Gardsjord ES, Steen NE, Melle I, Brattbakk HR, Steen VM, et al. Attenuated Notch signaling in schizophrenia and bipolar disorder. Sci Rep. 2018;8(1):5349. doi:10.1038/s41598-018-23703-w
    OpenUrlCrossRefPubMed
  25. 25.↵
    Qin D. Next-generation sequencing and its clinical application. Cancer Biol Med. 2019;16(1):4–10. doi:10.20892/j.issn.2095-3941.2018.0055
    OpenUrlFREE Full Text
  26. 26.↵
    van Driel MA, Brunner HG. Bioinformatics methods for identifying candidate disease genes. Hum Genomics. 2006;2(6):429–432. doi:10.1186/1479-7364-2-6-429
    OpenUrlCrossRefPubMed
  27. 27.↵
    Clough E, Barrett T. The Gene Expression Omnibus Database. Methods Mol Biol. 2016;1418:93–110. doi:10.1007/978-1-4939-3578-9_5
    OpenUrlCrossRefPubMed
  28. 28.↵
    Krebs CE, Ori APS, Vreeker A, Wu T, Cantor RM, Boks MPM, Kahn RS, Olde Loohuis LM, Ophoff RA. Whole blood transcriptome analysis in bipolar disorder reveals strong lithium effect. Psychol Med. 2020;50(15):2575–2586. doi:10.1017/S0033291719002745
    OpenUrlCrossRef
  29. 29.↵
    Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. doi:10.1186/s13059-014-0550-8
    OpenUrlCrossRefPubMed
  30. 30.↵
    Solari A, Goeman JJ. Minimally adaptive BH: A tiny but uniform improvement of the procedure of Benjamini and Hochberg. Biom J. 2017;59(4):776–780. doi:10.1002/bimj.201500253
    OpenUrlCrossRef
  31. 31.↵
    Reimand J, Kull M, Peterson H, Hansen J, Vilo J. g:Profiler--a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 2007;35(Web Server issue):W193–W200. doi:10.1093/nar/gkm226
    OpenUrlCrossRefPubMedWeb of Science
  32. 32.↵
    Thomas PD. The Gene Ontology and the Meaning of Biological Function. Methods Mol Biol. 2017;1446:15 doi:10.1007/978-1-4939-3743-1_2
    OpenUrlCrossRef
  33. 33.↵
    Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, Haw R, Jassal B, Korninger F, May B et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46(D1):D649–D655. doi:10.1093/nar/gkx1132
    OpenUrlCrossRefPubMed
  34. 34.↵
    Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605–D612. doi:10.1093/nar/gkaa1074
    OpenUrlCrossRefPubMed
  35. 35.↵
    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13(11):2498–2504. doi:10.1101/gr.1239303
    OpenUrlAbstract/FREE Full Text
  36. 36.↵
    Luo X, Guo L, Dai XJ, Wang Q, Zhu W, Miao X, Gong H. Abnormal intrinsic functional hubs in alcohol dependence: evidence from a voxelwise degree centrality analysis. Neuropsychiatr Dis Treat. 2017;13:2011–2020. doi:10.2147/NDT.S142742
    OpenUrlCrossRef
  37. 37.↵
    Li Y, Li W, Tan Y, Liu F, Cao Y, Lee KY. Hierarchical Decomposition for Betweenness Centrality Measure of Complex Networks. Sci Rep. 2017;7:46491.. doi:10.1038/srep46491
    OpenUrlCrossRef
  38. 38.↵
    Gilbert M, Li Z, Wu XN, Rohr L, Gombos S, Harter K, Schulze WX. Comparison of path-based centrality measures in protein-protein interaction networks revealed proteins with phenotypic relevance during adaptation to changing nitrogen environments. J Proteomics. 2021;235:104114. doi:10.1016/j.jprot.2021.104114
    OpenUrlCrossRef
  39. 39.↵
    Li G, Li M, Wang J, Li Y, Pan Y. United Neighborhood Closeness Centrality and Orthology for Predicting Essential Proteins. IEEE/ACM Trans Comput Biol Bioinform. 2020;17(4):1451–1458. doi:10.1109/TCBB.2018.2889978
    OpenUrlCrossRef
  40. 40.↵
    Zaki N, Efimov D, Berengueres J. Protein complex detection using interaction reliability assessment and weighted clustering coefficient. BMC Bioinformatics. 2013;14:163. doi:10.1186/1471-2105-14
    OpenUrlCrossRef
  41. 41.↵
    Fan Y, Xia J (2018) miRNet-Functional Analysis and Visual Exploration of miRNA-Target Interactions in a Network Context. Methods Mol Biol 1819:215–233. doi:10.1007/978-1-4939-8618-7_10
    OpenUrlCrossRef
  42. 42.↵
    Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J (2019) NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res 47:W234–W241. doi:10.1093/nar/gkz240
    OpenUrlCrossRefPubMed
  43. 43.↵
    Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011;12:77. doi:10.1186/1471-2105-12-77
    OpenUrlCrossRefPubMed
  44. 44.↵
    Wang X, Meng X, Meng L, Guo Y, Li Y, Yang C, Pei Z, Li J, Wang F. Joint efficacy of the three biomarkers SNCA, GYPB and HBG1 for atrial fibrillation and stroke: Analysis via the support vector machine neural network. J Cell Mol Med. 2022;10.1111/jcmm.17224. doi:10.1111/jcmm.17224
    OpenUrlCrossRef
  45. 45.↵
    Joshi A, Schmidt LE, Burnap SA, Lu R, Chan MV, Armstrong PC, Baig F, Gutmann C, Willeit P, Santer P, et al. Neutrophil-Derived Protein S100A8/A9 Alters the Platelet Proteome in Acute Myocardial Infarction and Is Associated With Changes in Platelet Reactivity. Arterioscler Thromb Vasc Biol. 2022;42(1):49–62. doi:10.1161/ATVBAHA.121.317113
    OpenUrlCrossRef
  46. 46.↵
    Lu SM, Yu CJ, Liu YH, Dong HQ, Zhang X, Zhang SS, Hu LQ, Zhang F, Qian YN, Gui B. S100A8 contributes to postoperative cognitive dysfunction in mice undergoing tibial fracture surgery by activating the TLR4/MyD88 pathway. Brain Behav Immun. 2015;44:221–234. doi:10.1016/j.bbi.2014.10.011
    OpenUrlCrossRefPubMed
  47. 47.↵
    Chen N, Miao L, Lin W, Zou D, Huang L, Huang J, Shi W, Li L, Luo Y, Liang H, Pan S, et al. Integrated DNA Methylation and Gene Expression Analysis Identified S100A8 and S100A9 in the Pathogenesis of Obesity. Front Cardiovasc Med. 2021;8:631650.doi:10.3389/fcvm.2021.631650
    OpenUrlCrossRef
  48. 48.↵
    Bouvier D, Giguère Y, Blanchon L, Bujold E, Pereira B, Bernard N, Gallot D, Sapin V, Forest JC. Study of sRAGE, HMGB1, AGE, and S100A8/A9 Concentrations in Plasma and in Serum-Extracted Extracellular Vesicles of Pregnant Women With Preterm Premature Rupture of Membranes. Front Physiol. 2020;11:609. doi:10.3389/fphys.2020.00609
    OpenUrlCrossRef
  49. 49.↵
    Chen TH, Tian X, Kuo PL, Pan HP, Wong LC, Jong YJ. Identification of KLHL40 mutations by targeted next-generation sequencing facilitated a prenatal diagnosis in a family with three consecutive affected fetuses with fetal akinesia deformation sequence. Prenat Diagn. 2016;36(12):1135–1138. doi:10.1002/pd.4949
    OpenUrlCrossRef
  50. 50.↵
    Lim RR, Vaidya T, Gadde SG, Yadav NK, Sethu S, Hainsworth DP, Mohan RR, Ghosh A, Chaurasia SS. Correlation between systemic S100A8 and S100A9 levels and severity of diabetic retinopathy in patients with type 2 diabetes mellitus. Diabetes Metab Syndr. 2019;13(2):1581–1589. doi:10.1016/j.dsx.2019.03.014
    OpenUrlCrossRef
  51. 51.↵
    Rosenblat JD. Targeting the immune system in the treatment of bipolar disorder. Psychopharmacology (Berl). 2019;236(10):2909–2921. doi:10.1007/s00213-019-5175-x
    OpenUrlCrossRef
  52. 52.↵
    Bame M, McInnis MG, O’Shea KS. MicroRNA Alterations in Induced Pluripotent Stem Cell-Derived Neurons from Bipolar Disorder Patients: Pathways Involved in Neuronal Differentiation, Axon Guidance, and Plasticity. Stem Cells Dev. 2020;29(17):1145–1159. doi:10.1089/scd.2020.0046
    OpenUrlCrossRef
  53. 53.↵
    Steen NE, Methlie P, Lorentzen S, Dieset I, Aas M, Nerhus M, Haram M, Agartz I, Melle I, Berg JP, et al. Altered systemic cortisol metabolism in bipolar disorder and schizophrenia spectrum disorders. J Psychiatr Res. 2014;52:57–62. doi:10.1016/j.jpsychires.2014.01.017
    OpenUrlCrossRef
  54. 54.↵
    Vancampfort D, Stubbs B, Mitchell AJ, De Hert M, Wampers M, Ward PB, Rosenbaum S, Correll CU.Risk of metabolic syndrome and its components in people with schizophrenia and related psychotic disorders, bipolar disorder and major depressive disorder: a systematic review and meta-analysis. World Psychiatry. 2015;14(3):339–347. doi:10.1002/wps.20252
    OpenUrlCrossRefPubMed
  55. 55.↵
    Blond BN, Fredericks CA, Blumberg HP. Functional neuroanatomy of bipolar disorder: structure, function, and connectivity in an amygdala-anterior paralimbic neural system. Bipolar Disord. 2012;14(4):340–355. doi:10.1111/j.1399-5618.2012.01015.x
    OpenUrlCrossRefPubMed
  56. 56.↵
    Curtis D. Do damaging variants of SLC6A9, the gene for the glycine transporter 1 (GlyT-1), protect against schizophrenia?. Psychiatr Genet. 2020;30(5):150–152. doi:10.1097/YPG.0000000000000260
    OpenUrlCrossRef
  57. 57.↵
    Palmer CG, Mallery E, Turunen JA, Hsieh HJ, Peltonen L, Lonnqvist J, Woodward JA, Sinsheimer JS. Effect of Rhesus D incompatibility on schizophrenia depends on offspring sex. Schizophr Res. 2008;104(1-3):135–145. doi:10.1016/j.schres.2008.06.022
    OpenUrlCrossRefPubMed
  58. 58.↵
    Flomen RH, Collier DA, Osborne S, Munro J, Breen G, St Clair D, Makoff AJ. Association study of CHRFAM7A copy number and 2 bp deletion polymorphisms with schizophrenia and bipolar affective disorder. Am J Med Genet B Neuropsychiatr Genet. 2006;141B(6):571–575. doi:10.1002/ajmg.b.30306
    OpenUrlCrossRef
  59. 59.↵
    Joaquim HPG, Costa AC, Serpa MH, Talib LL, Gattaz WF. Reduced Annexin A3 in schizophrenia. Eur Arch Psychiatry Clin Neurosci. 2020;270(4):489–494. doi:10.1007/s00406-019-01048-3
    OpenUrlCrossRef
  60. 60.↵
    Deng X, Sagata N, Takeuchi N, Tanaka M, Ninomiya H, Iwata N, Ozaki N, Shibata H, Fukumaki Y. Association study of polymorphisms in the neutral amino acid transporter genes SLC1A4, SLC1A5 and the glycine transporter genes SLC6A5, SLC6A9 with schizophrenia. BMC Psychiatry. 2008;8:58. doi:10.1186/1471-244X-8-58
    OpenUrlCrossRefPubMed
  61. 61.↵
    Lubeiro A, Fatjó-Vilas M, Guardiola M, Almodóvar C, Gomez-Pilar J, Cea-Cañas B, Poza J, Palomino A, Gómez-García M, Zugasti J, et al. Analysis of KCNH2 and CACNA1C schizophrenia risk genes on EEG functional network modulation during an auditory odd-ball task. Eur Arch Psychiatry Clin Neurosci. 2020;270(4):433–442. doi:10.1007/s00406-018-0977-0
    OpenUrlCrossRef
  62. 62.↵
    Wan C, La Y, Zhu H, Yang Y, Jiang L, Chen Y, Feng G, Li H, Sang H, Hao X, et al. Abnormal changes of plasma acute phase proteins in schizophrenia and the relation between schizophrenia and haptoglobin (Hp) gene. Amino Acids. 2007;32(1):101–108. doi:10.1007/s00726-005-0292-8
    OpenUrlCrossRefPubMedWeb of Science
  63. 63.↵
    Kanazawa T, Chana G, Glatt SJ, Mizuno H, Masliah E, Yoneda H, Tsuang MT, Everall IP. The utility of SELENBP1 gene expression as a biomarker for major psychotic disorders: replication in schizophrenia and extension to bipolar disorder with psychosis. Am J Med Genet B Neuropsychiatr Genet. 2008;147B(6):686–689. doi:10.1002/ajmg.b.30664
    OpenUrlCrossRef
  64. 64.↵
    Takamura S, Ikeda A, Nishioka K, Furuya H, Tashiro M, Matsushima T, Li Y, Yoshino H, Funayama M, Morinobu S, et al. Schizophrenia as a prodromal symptom in a patient harboring SNCA duplication. Parkinsonism Relat Disord. 2016;25:108–109. doi:10.1016/j.parkreldis.2016.01.028
    OpenUrlCrossRef
  65. 65.↵
    Wang J, Liu Y, Wang Z, Du W, Hui L, Zhao X, Zhao X, Zhang X, Wei J. Lack of genetic association of the TGM2 gene with schizophrenia in a Chinese population. Psychiatr Genet. 2015;25(6):259–262. doi:10.1097/YPG.0000000000000103
    OpenUrlCrossRef
  66. 66.↵
    Li X, Zhang W, Zhang C, Yi Z, Zhang DF, Gong W, Tang J, Wang D, Lu W, Chen X, et al. Common variants of the PINK1 and PARL genes do not confer genetic susceptibility to schizophrenia in Han Chinese. Mol Genet Genomics. 2015;290(2):585–592. doi:10.1007/s00438-014-0942-1
    OpenUrlCrossRef
  67. 67.↵
    Chittiprol S, Venkatasubramanian G, Neelakantachar N, Reddy NA, Shetty KT, Gangadhar BN. Longitudinal study of beta2-microglobulin abnormalities in schizophrenia. Int Immunopharmacol. 2009;9(10):1215–1217. doi:10.1016/j.intimp.2009.07.002
    OpenUrlCrossRefPubMed
  68. 68.↵
    Zhang QQ, Jiang T, Gu LZ, Zhu XC, Zhao HD, Gao Q, Zhu HQ, Zhou JS, Zhang YD. Common Polymorphisms Within QPCT Gene Are Associated with the Susceptibility of Schizophrenia in a Han Chinese Population. Mol Neurobiol. 2016;53(9):6362–6366. doi:10.1007/s12035-015-9541-3
    OpenUrlCrossRef
  69. 69.↵
    Golimbet V, Korovaitseva G, Abramova L, Kaleda V. The 844ins68 polymorphism of the cystathionine beta-synthase gene is associated with schizophrenia. Psychiatry Res. 2009;170(2-3):168–171. doi:10.1016/j.psychres.2008.07.007
    OpenUrlCrossRefPubMed
  70. 70.↵
    Harada S, Tachikawa H, Kawanishi Y. A possible association between an insertion/deletion polymorphism of the NQO2 gene and schizophrenia. Psychiatr Genet. 2003;13(4):205–209. doi:10.1097/00041444-200312000-00003
    OpenUrlCrossRefPubMedWeb of Science
  71. 71.↵
    Yang B, Niu W, Chen S, Xu F, Li X, Wu X, Cao Y, Zhang R, Yang F, Wang L, et al. Association study of the GLRX5 rs1007814 polymorphism with schizophrenia in the Han Chinese population. Psychiatr Genet. 2017;27(2):76–77. doi:10.1097/YPG.0000000000000160
    OpenUrlCrossRef
  72. 72.↵
    Behan AT, Byrne C, Dunn MJ, Cagney G, Cotter DR. Proteomic analysis of membrane microdomain-associated proteins in the dorsolateral prefrontal cortex in schizophrenia and bipolar disorder reveals alterations in LAMP, STXBP1 and BASP1 protein expression. Mol Psychiatry. 2009;14(6):601-613. doi:10.1038/mp.2008.7
    OpenUrlCrossRefPubMedWeb of Science
  73. 73.↵
    Meyer MA. Neuronal localization of GAS7 within human brain tissue: Implications for schizophrenia research. Neurol Int. 2018;10(4):7563. doi:10.4081/ni.2018.7563
    OpenUrlCrossRef
  74. 74.↵
    Shao X, Yan C, Sun D, Fu C, Tian C, Duan L, Zhu G. Association Between Glutathione Peroxidase-1 (GPx-1) Polymorphisms and Schizophrenia in the Chinese Han Population. Neuropsychiatr Dis Treat. 2020;16:2297–2305. doi:10.2147/NDT.S272278
    OpenUrlCrossRef
  75. 75.↵
    Komatsu H, Takeuchi H, Kikuchi Y, Ono C, Yu Z, Iizuka K, Takano Y, Kakuto Y, Funakoshi S, Ono T, et al. Ethnicity-Dependent Effects of Schizophrenia Risk Variants of the OLIG2 Gene on OLIG2 Transcription and White Matter Integrity. Schizophr Bull. 2020;46(6):1619–1628. doi:10.1093/schbul/sbaa049
    OpenUrlCrossRef
  76. 76.↵
    Wang S, Ren H, Xu J, Yu Y, Han S, Qiao H, Cheng S, Xu C, An S, Ju B, et al. Diminished serum repetin levels in patients with schizophrenia and bipolar disorder. Sci Rep. 2015;5:7977. doi:10.1038/srep07977
    OpenUrlCrossRef
  77. 77.↵
    Kozłowska E, Brzezińska-Błaszczyk E, Agier J, Wysokiński A, Żelechowska P. Alarmins (IL-33, sST2, HMGB1, and S100B) as potential biomarkers for schizophrenia. J Psychiatr Res. 2021;138:380-387. doi:10.1016/j.jpsychires.2021.04.019
    OpenUrlCrossRef
  78. 78.↵
    Yuan A, Li W, Yu T, Zhang C, Wang D, Liu D, Xu Y, Li H, Yu S. SOX10 rs139883 polymorphism is associated with the age of onset in schizophrenia. J Mol Neurosci. 2013;50(2):333–338. doi:10.1007/s12031-013-9982-y
    OpenUrlCrossRef
  79. 79.↵
    Hirata Y, Zai CC, Souza RP, Lieberman JA, Meltzer HY, Kennedy JL. Association study of GRIK1 gene polymorphisms in schizophrenia: case-control and family-based studies. Hum Psychopharmacol. 2012;27(4):345–351. doi:10.1002/hup.2233
    OpenUrlCrossRefPubMed
  80. 80.↵
    Greenbaum L, Smith RC, Lorberboym M, Alkelai A, Zozulinsky P, Lifschytz T, Kohn Y, Djaldetti R, Lerer B. Association of the ZFPM2 gene with antipsychotic-induced parkinsonism in schizophrenia patients. Psychopharmacology (Berl). 2012;220(3):519–528. doi:10.1007/s00213-011-2499-6
    OpenUrlCrossRef
  81. 81.↵
    de Sena Cortabitarte A, Degenhardt F, Strohmaier J, Lang M, Weiss B, Roeth R, Giegling I, Heilmann-Heimbach S, Hofmann A, Rujescu D, et al. Investigation of SHANK3 in schizophrenia. Am J Med Genet B Neuropsychiatr Genet. 2017;174(4):390–398. doi:10.1002/ajmg.b.32528
    OpenUrlCrossRefPubMed
  82. 82.↵
    Li D, Feng G, He L. Case-control study of association between the functional candidate gene ERBB3 and schizophrenia in Caucasian population. World J Biol Psychiatry. 2009;10(4 Pt 2):595-598. doi:10.1080/15622970903304442
    OpenUrlCrossRefPubMed
  83. 83.↵
    Managò F, Mereu M, Mastwal S, Mastrogiacomo R, Scheggia D, Emanuele M, De Luca MA, Weinberger DR, Wang KH, Papaleo F.Genetic Disruption of Arc/Arg3.1 in Mice Causes Alterations in Dopamine and Neurobehavioral Phenotypes Related to Schizophrenia. Cell Rep. 2016;16(8):2116–2128. doi:10.1016/j.celrep.2016.07.044
    OpenUrlCrossRefPubMed
  84. 84.↵
    Webster MJ, O’Grady J, Kleinman JE, Weickert CS. Glial fibrillary acidic protein mRNA levels in the cingulate cortex of individuals with depression, bipolar disorder and schizophrenia. Neuroscience. 2005;133(2):453–461. doi:10.1016/j.neuroscience.2005.02.037
    OpenUrlCrossRefPubMedWeb of Science
  85. 85.↵
    Deng X, Takaki H, Wang L, Kuroki T, Nakahara T, Hashimoto K, Ninomiya H, Arinami T, Inada T, Ujike H, et al. Positive association of phencyclidine-responsive genes, PDE4A and PLAT, with schizophrenia. Am J Med Genet B Neuropsychiatr Genet. 2011;156B(7):850-858. doi:10.1002/ajmg.b.31233
    OpenUrlCrossRef
  86. 86.↵
    Shibata H, Aramaki T, Sakai M, Ninomiya H, Tashiro N, Iwata N, Ozaki N, Fukumaki Y. Association study of polymorphisms in the GluR7, KA1 and KA2 kainate receptor genes (GRIK3, GRIK4, GRIK5) with schizophrenia. Psychiatry Res. 2006;141(1):39-51. doi:10.1016/j.psychres.2005.07.015
    OpenUrlCrossRefPubMed
  87. 87.↵
    Juraeva D, Haenisch B, Zapatka M, Frank J; GROUP Investigators; PSYCH-GEMS SCZ Working Group, Witt SH, Mühleisen TW, Treutlein J, Strohmaier J, Meier S, Degenhardt F, et al. Integrated pathway-based approach identifies association between genomic regions at CTCF and CACNB2 and schizophrenia. PLoS Genet. 2014;10(6):e1004345. doi:10.1371/journal.pgen.1004345
    OpenUrlCrossRefPubMed
  88. 88.↵
    Gauthier J, Siddiqui TJ, Huashan P, Yokomaku D, Hamdan FF, Champagne N, Lapointe M, Spiegelman D, Noreau A, Lafrenière RG, et al. Truncating mutations in NRXN2 and NRXN1 in autism spectrum disorders and schizophrenia. Hum Genet. 2011;130(4):563–573. doi:10.1007/s00439-011-0975-z
    OpenUrlCrossRefPubMed
  89. 89.↵
    Rao S, Ye N, Hu H, Shen Y, Xu Q. Variants in TERT influencing telomere length are associated with paranoid schizophrenia risk. Am J Med Genet B Neuropsychiatr Genet. 2016;171B(3):317–324. doi:10.1002/ajmg.b.32403
    OpenUrlCrossRefPubMed
  90. 90.↵
    Lee HJ, Kang SG, Paik JW, Lee MS, Cho BH, Park YM, Kim W, Choi JE, Jung IK, Kim L, et al. No evidence for an association between G protein beta3 subunit gene C825T polymorphism and tardive dyskinesia in schizophrenia. Hum Psychopharmacol. 2007;22(8):501–504. doi:10.1002/hup.875
    OpenUrlCrossRefPubMed
  91. 91.↵
    Kurumaji A, Nomoto H, Okano T, Toru M. An association study between polymorphism of L1CAM gene and schizophrenia in a Japanese sample. Am J Med Genet. 2001;105(1):99–104.
    OpenUrlCrossRefPubMed
  92. 92.↵
    Huentelman MJ, Muppana L, Corneveaux JJ, Dinu V, Pruzin JJ, Reiman R, Borish CN, De Both M, Ahmed A, Todorov A, et al. Association of SNPs in EGR3 and ARC with Schizophrenia Supports a Biological Pathway for Schizophrenia Risk. PLoS One. 2015;10(10):e0135076. doi:10.1371/journal.pone.0135076
    OpenUrlCrossRefPubMed
  93. 93.↵
    Kassan A, Egawa J, Zhang Z, Almenar-Queralt A, Nguyen QM, Lajevardi Y, Kim K, Posadas E, Jeste DV, Roth DM, et al. Caveolin-1 regulation of disrupted-in-schizophrenia-1 as a potential therapeutic target for schizophrenia. J Neurophysiol. 2017;117(1):436–444. doi:10.1152/jn.00481.2016
    OpenUrlCrossRefPubMed
  94. 94.↵
    Hidalgo S, Campusano JM, Hodge JJL. The Drosophila ortholog of the schizophrenia-associated CACNA1A and CACNA1B voltage-gated calcium channels regulate memory, sleep and circadian rhythms. Neurobiol Dis. 2021;155:105394. doi:10.1016/j.nbd.2021.105394
    OpenUrlCrossRef
  95. 95.↵
    Karlsson R, Graae L, Lekman M, Wang D, Favis R, Axelsson T, Galter D, Belin AC, Paddock S. MAGI1 copy number variation in bipolar affective disorder and schizophrenia. Biol Psychiatry. 2012;71(10):922–930. doi:10.1016/j.biopsych.2012.01.020
    OpenUrlCrossRefPubMedWeb of Science
  96. 96.↵
    Hathy E, Szabó E, Vincze K, Haltrich I, Kiss E, Varga N, Erdei Z, Várady G, Homolya L, Apáti Á, et al. Generation of multiple iPSC clones from a male schizophrenia patient carrying de novo mutations in genes KHSRP, LRRC7, and KIR2DL1, and his parents. Stem Cell Res. 2021;51:102140. doi:10.1016/j.scr.2020.102140
    OpenUrlCrossRef
  97. 97.↵
    Talkowski ME, McClain L, Allen T, Bradford LD, Calkins M, Edwards N, Georgieva L, Go R, Gur R, Gur R, et al. Convergent patterns of association between phenylalanine hydroxylase variants and schizophrenia in four independent samples. Am J Med Genet B Neuropsychiatr Genet. 2009;150B(4):560–569. doi:10.1002/ajmg.b.30862
    OpenUrlCrossRef
  98. 98.↵
    Kang RH, Jung SM, Kim KA, Lee DK, Cho HK, Jung BJ, Kim YK, Kim SH, Han C, Lee MS, et al. Effects of CYP2D6 and CYP3A5 genotypes on the plasma concentrations of risperidone and 9-hydroxyrisperidone in Korean schizophrenic patients. J Clin Psychopharmacol. 2009;29(3):272–277. doi:10.1097/JCP.0b013e3181a289e0
    OpenUrlCrossRefPubMed
  99. 99.↵
    Ueno T, Tabara Y, Fukuda N, Tahira K, Matsumoto T, Kosuge K, Haketa A, Matsumoto K, Sato Y, Nakayama T, et al. Association of SLC6A9 gene variants with human essential hypertension. J Atheroscler Thromb. 2009;16(3):201–206. doi:10.5551/jat.e125
    OpenUrlCrossRefPubMed
  100. 100.↵
    Benjafield AV, Katyk K, Morris BJ. Association of EDNRA, but not WNK4 or FKBP1B, polymorphisms with essential hypertension. Clin Genet. 2003;64(5):433–438. doi:10.1034/j.1399-0004.2003.00148.x
    OpenUrlCrossRefPubMedWeb of Science
  101. 101.↵
    Tzouvelekis A, Herazo-Maya JD, Ryu C, Chu JH, Zhang Y, Gibson KF, Adonteng-Boateng PK, Li Q, Pan H, Cherry B, et al.S100A12 as a marker of worse cardiac output and mortality in pulmonary hypertension. Respirology. 2018;23(8):771–779. doi:10.1111/resp.13302
    OpenUrlCrossRef
  102. 102.↵
    Seol SY, Lee SY, Kim YD, Do EJ, Kwon JA, Kim SI, Chu IS, Leem SH. Minisatellite polymorphisms of the SLC6A19: susceptibility in hypertension. Biochem Biophys Res Commun. 2008;374(4):714–719. doi:10.1016/j.bbrc.2008.07.094
    OpenUrlCrossRefPubMed
  103. 103.↵
    Ferreira NE, Omae S, Pereira A, Rodrigues MV, Miyakawa AA, Campos LC, Santos PC, Dallan LA, Martinez TL, Santos RD, et al. Thioredoxin interacting protein genetic variation is associated with diabetes and hypertension in the Brazilian general population. Atherosclerosis. 2012;221(1):131–136. doi:10.1016/j.atherosclerosis.2011.12.009
    OpenUrlCrossRefPubMed
  104. 104.↵
    McCarthy CG, Wenceslau CF, Goulopoulou S, Ogbi S, Baban B, Sullivan JC, Matsumoto T, Webb RC.Circulating mitochondrial DNA and Toll-like receptor 9 are associated with vascular dysfunction in spontaneously hypertensive rats. Cardiovasc Res. 2015;107(1):119–130. doi:10.1093/cvr/cvv137
    OpenUrlCrossRefPubMed
  105. 105.↵
    Delanghe JR, Duprez DA, De Buyzere ML, Bergez BM, Callens BY, Leroux-Roels GG, Clement DL.Haptoglobin polymorphism and complications in established essential arterial hypertension. J Hypertens. 1993;11(8):861–867. doi:10.1097/00004872-199308000-00013
    OpenUrlCrossRefPubMedWeb of Science
  106. 106.↵
    Shah SFA, Iqbal T, Qamar R, Rafiq MA, Hussain S. ARG1 Gene Polymorphisms and Their Association in Individuals with Essential Hypertension: A Case-Control Study. DNA Cell Biol. 2018;37(7):609–616. doi:10.1089/dna.2018.4222
    OpenUrlCrossRef
  107. 107.↵
    Saraji A, Sydykov A, Schäfer K, Garcia-Castro CF, Henneke I, Alebrahimdehkordi N, Kosanovic D, Hadzic S, Guenther A, Hecker M, et al. PINK1-mediated Mitophagy Contributes to Pulmonary Vascular Remodeling in Pulmonary Hypertension. Am J Respir Cell Mol Biol. 2021;65(2):226–228. doi:10.1165/rcmb.2021-0082LE
    OpenUrlCrossRef
  108. 108.↵
    Wang HJ, Si QJ, Shi Y, Guo Y, Li Y, Wang YT. The prognostic values of beta-2 microglobulin for risks of cardiovascular events and mortality in the elderly patients with isolated systolic hypertension. J Res Med Sci. 2018;23:82. doi:10.4103/jrms.JRMS_135_17
    OpenUrlCrossRef
  109. 109.↵
    Weiss S, Rosendahl A, Czesla D, Meyer-Schwesinger C, Stahl RA, Ehmke H, Kurts C, Zipfel PF, Köhl J, Wenzel UO.The complement receptor C5aR1 contributes to renal damage but protects the heart in angiotensin II-induced hypertension. Am J Physiol Renal Physiol. 2016;310(11):F1356–F1365. doi:10.1152/ajprenal.00040.2016
    OpenUrlCrossRefPubMed
  110. 110.↵
    Bai Y, Wang J, Chen Y, Lv T, Wang X, Liu C, Xue H, He K, Sun L. The miR-182/Myadm axis regulates hypoxia-induced pulmonary hypertension by balancing the BMP-and TGF-β-signalling pathways in an SMC/EC-crosstalk-associated manner. Basic Res Cardiol. 2021;116(1):53. doi:10.1007/s00395-021-00892-6
    OpenUrlCrossRef
  111. 111.↵
    Wang C, Xu G, Wen Q, Peng X, Chen H, Zhang J, Xu S, Zhang C, Zhang M, Ma J, et al. CBS promoter hypermethylation increases the risk of hypertension and stroke. Clinics (Sao Paulo). 2019;74:e630. doi:10.6061/clinics/2019/e630
    OpenUrlCrossRef
  112. 112.↵
    Ardanaz N, Yang XP, Cifuentes ME, Haurani MJ, Jackson KW, Liao TD, Carretero OA, Pagano PJ. Lack of glutathione peroxidase 1 accelerates cardiac-specific hypertrophy and dysfunction in angiotensin II hypertension. Hypertension. 2010;55(1):116–123. doi:10.1161/HYPERTENSIONAHA.109.135715
    OpenUrlCrossRef
  113. 113.↵
    Wang Q, Shi W, Zhang Q, Feng W, Wang J, Zhai C, Yan X, Li M. Inhibition of Siah2 ubiquitin ligase ameliorates monocrotaline-induced pulmonary arterial remodeling through inactivation of YAP. Life Sci. 2020;242:117159. doi:10.1016/j.lfs.2019.117159
    OpenUrlCrossRef
  114. 114.↵
    Mahal Z, Fujikawa K, Matsuo H, Zahid HM, Koike M, Misumi M, Kaneko T, Mashimo T, Ohara H, Nabika T. Effects of the Prdx2 depletion on blood pressure and life span in spontaneously hypertensive rats. Hypertens Res. 2019;42(5):610–617. doi:10.1038/s41440-019-0207-9
    OpenUrlCrossRefPubMed
  115. 115.↵
    Jiang W, Wang X, Li R, Wang P, Shan G, Jia X, Gu M. Targeted capture sequencing identifies genetic variations of GRK4 and RDH8 in Han Chinese with essential hypertension in Xinjiang. PLoS One. 2021;16(7):e0255311. doi:10.1371/journal.pone.0255311
    OpenUrlCrossRef
  116. 116.↵
    Li W, Liu C. The -344C/T polymorphism in the CYP11B2 gene is associated with essential hypertension in the Chinese. J Renin Angiotensin Aldosterone Syst. 2014;15(2):150–155. doi:10.1177/1470320312466928
    OpenUrlCrossRefPubMed
  117. 117.↵
    Batista AP, Barbosa KF, de Azevedo RJ, Vianna VN, de Queiroz EM, Marinho CC, Machado-Coelho GLL. Hypertension is associated with a variant in the RARRES2 gene in populations of Ouro Preto, Minas Gerais, Brazil: a cross-sectional study. Int J Mol Epidemiol Genet. 2021;12(3):40–51.
    OpenUrl
  118. 118.↵
    Nosalski R, Mikolajczyk T, Siedlinski M, Saju B, Koziol J, Maffia P, Guzik TJ. Nox1/4 inhibition exacerbates age dependent perivascular inflammation and fibrosis in a model of spontaneous hypertension. Pharmacol Res. 2020;161:105235. doi:10.1016/j.phrs.2020.105235
    OpenUrlCrossRef
  119. 119.↵
    Cannon DT, Nogueira L, Gutierrez-Gonzalez AK, Gilmore NK, Bigby TD, Breen EC. Role of IL-33 receptor (ST2) deletion in diaphragm contractile and mitochondrial function in the Sugen5416/hypoxia model of pulmonary hypertension. Respir Physiol Neurobiol. 2022;295:103783. doi:10.1016/j.resp.2021.103783
    OpenUrlCrossRef
  120. 120.↵
    Dumont J, Meroufel D, Bauters C, Hansmannel F, Bensemain F, Cottel D, Hamon M, Lambert JC, Ducimetière P, Amouyel P, et al. Association of ornithine transcarbamylase gene polymorphisms with hypertension and coronary artery vasomotion. Am J Hypertens. 2009;22(9):993–1000. doi:10.1038/ajh.2009.110
    OpenUrlCrossRefPubMed
  121. 121.↵
    Prieto MC, Williams DE, Liu L, Kavanagh KL, Mullins JJ, Mitchell KD. Enhancement of renin and prorenin receptor in collecting duct of Cyp1a1-Ren2 rats may contribute to development and progression of malignant hypertension. Am J Physiol Renal Physiol. 2011;300(2):F581–F588. doi:10.1152/ajprenal.00433.2010
    OpenUrlCrossRefPubMedWeb of Science
  122. 122.↵
    Quintana-Villamandos B, Goukassian DA, Sasi SP, Delgado-Baeza E. Short-Term Treatment with Esmolol Reverses Left Ventricular Hypertrophy in Adult Spontaneously Hypertensive Rats via Inhibition of Akt/NF-B and κ NFATc4. Biomed Res Int. 2018;2018:2691014. doi:10.1155/2018/2691014
    OpenUrlCrossRef
  123. 123.↵
    Turcot V, Bouchard L, Faucher G, Garneau V, Tchernof A, Deshaies Y, Pérusse L, Marceau S, Biron S, Lescelleur O, et al. Thymic stromal lymphopoietin: an immune cytokine gene associated with the metabolic syndrome and blood pressure in severe obesity. Clin Sci (Lond). 2012;123(2):99–109. doi:10.1042/CS20110584
    OpenUrlAbstract/FREE Full Text
  124. 124.↵
    Niu L, Sun N, Kong L, Xu Y, Kang Y. miR-634 inhibits human vascular smooth muscle cell proliferation and migration in hypertension through Wnt4/β-catenin pathway. Front Biosci (Landmark Ed). 2021;26(8):395–404. doi:10.52586/4953
    OpenUrlCrossRef
  125. 125.↵
    Chirinos JA, Sardana M, Syed AA, Koppula MR, Varakantam S, Vasim I, Oldland HG, Phan TS, Drummen NEA, Vermeer C, et al. Aldosterone, inactive matrix gla-protein, and large artery stiffness in hypertension. J Am Soc Hypertens. 2018;12(9):681–689. doi:10.1016/j.jash.2018.06.018
    OpenUrlCrossRef
  126. 126.↵
    Tomaszewski M, Charchar FJ, Nelson CP, Barnes T, Denniff M, Kaiser M, Debiec R, Christofidou P, Rafelt S, van der Harst P, et al. Pathway analysis shows association between FGFBP1 and hypertension. J Am Soc Nephrol. 2011;22(5):947–955. doi:10.1681/ASN.2010080829
    OpenUrlAbstract/FREE Full Text
  127. 127.↵
    Donlon TA, Chen R, Masaki KH, Willcox DC, Allsopp RC, Willcox BJ, Morris BJ. Association of growth hormone receptor gene variant with longevity in men is due to amelioration of increased mortality risk from hypertension. Aging (Albany NY). 2021;13(11):14745–14767. doi:10.18632/aging.203133
    OpenUrlCrossRef
  128. 128.↵
    Zhu L, Fang Z, Jin Y, Chang W, Huang M, Chen Y, Yao Y. Circulating ERBB3 levels are inversely associated with the risk of overweight-related hypertension: a cross-sectional study. BMC Endocr Disord. 2021;21(1):130. doi:10.1186/s12902-021-00793-8
    OpenUrlCrossRef
  129. 129.↵
    Ma J, Shen L, Bao L, Yuan H, Wang Y, Liu H, Wang Q. A novel prognosis prediction model, including cytotoxic T lymphocyte-associated antigen-4, ischemia-modified albumin, lipoprotein-associated phospholipase A2, glial fibrillary acidic protein, and homocysteine, for ischemic stroke in the Chinese hypertensive population. J Clin Lab Anal. 2021;35(5):e23756. doi:10.1002/jcla.23756
    OpenUrlCrossRef
  130. 130.↵
    Dai HL, Wang D, Guang XF, Zhang WH. Pulmonary Hypertension in a Patient With Kartagener’s Syndrome and a Novel Homozygous Nonsense Mutation in CCDC40 Gene: A Case Report. Front Med (Lausanne). 2022;9:860684. doi:10.3389/fmed.2022.860684
    OpenUrlCrossRef
  131. 131.↵
    Soldatov NM. CACNB2: An Emerging Pharmacological Target for Hypertension, Heart Failure, Arrhythmia and Mental Disorders. Curr Mol Pharmacol. 2015;8(1):32–42. doi:10.2174/1874467208666150507093258
    OpenUrlCrossRefPubMed
  132. 132.↵
    Shimizu Y, Sato S, Koyamatsu J, Yamanashi H, Nagayoshi M, Kadota K, Kawashiri SY, Inoue K, Nagata Y, Maeda T. Platelets and circulating CD34-positive cells as an indicator of the activity of the vicious cycle between hypertension and endothelial dysfunction in elderly Japanese men. Atherosclerosis. 2017;259:26–31. doi:10.1016/j.atherosclerosis.2017.02.016
    OpenUrlCrossRef
  133. 133.↵
    Morris HE, Neves KB, Montezano AC, MacLean MR, Touyz RM. Notch3 signalling and vascular remodelling in pulmonary arterial hypertension. Clin Sci (Lond). 2019;133(24):2481–2498. doi:10.1042/CS20190835
    OpenUrlCrossRef
  134. 134.↵
    Martynowicz H, Gać P, Kornafel-Flak O, Filipów S, Łaczmański Ł, Sobieszczańska M, Mazur G, Porba R. The Relationship Between the Effectiveness of Blood Pressure Control and Telomerase Reverse Transcriptase Concentration, Adipose Tissue Hormone Concentration and Transcriptase Concentration, Adipose Tissue Hormone Concentration and Endothelium Function in Hypertensives. Heart Lung Circ. 2020;29(8):e200–e209. doi:10.1016/j.hlc.2019.12.012
    OpenUrlCrossRef
  135. 135.↵
    Kumar R, Kohli S, Alam P, Barkotoky R, Gupta M, Tyagi S, Jain SK, Pasha MA. Interactions between the FTO and GNB3 genes contribute to varied clinical phenotypes in hypertension. PLoS One. 2013;8(5):e63934. doi:10.1371/journal.pone.0063934
    OpenUrlCrossRefPubMed
  136. 136.↵
    Carmona-Calero EM, Castañeyra-Ruiz L, González-Toledo JM, de Paz-Carmona H, Brage C, Castañeyra-Ruiz A, Rancel-Torres MN, González-Marrero I, Castañeyra-Perdomo A. The expression of p73 in the organum vasculosum of the lamina terminalis and choroid plexus of spontaneously hypertensive rats. Histol Histopathol. 2013;28(7):925–932. doi:10.14670/HH-28.925
    OpenUrlCrossRef
  137. 137.↵
    Tanaka S, Yamamoto T, Mikawa M, Nawata J, Fujii S, Nakamura Y, Kato T, Fukuda M, Suetomi T, Uchinoumi H, et al. Stabilization of RyR2 maintains right ventricular function, reduces the development of ventricular arrhythmias, and improves prognosis in pulmonary hypertension. Heart Rhythm. 2022;S1547-5271(22)00119-9. doi:10.1016/j.hrthm.2022.02.003
    OpenUrlCrossRef
  138. 138.↵
    Holmes RS, Spradling-Reeves KD, Cox LA. Mammalian Glutamyl Aminopeptidase Genes (ENPEP) and Proteins: Comparative Studies of a Major Contributor to Arterial Hypertension. J Data Mining Genomics Proteomics. 2017;8(2):2. doi:10.4172/2153-0602.1000211
    OpenUrlCrossRef
  139. 139.↵
    Zhang B, Li M, Wang L, Li C, Lou Y, Liu J, Liu Y, Wang Z, Wen S. The association between the polymorphisms in a sodium channel gene SCN7A and essential hypertension: a case-control study in the Northern Han Chinese. Ann Hum Genet. 2015;79(1):28–36. doi:10.1111/ahg.12085
    OpenUrlCrossRef
  140. 140.↵
    Wakabayashi M, Mori T, Isobe K, Sohara E, Susa K, Araki Y, Chiga M, Kikuchi E, Nomura N, Mori Y, et al. Impaired KLHL3-mediated ubiquitination of WNK4 causes human hypertension. Cell Rep. 2013;3(3):858–868. doi:10.1016/j.celrep.2013.02.024
    OpenUrlCrossRefPubMedWeb of Science
  141. 141.↵
    Li B, Yao Q, Guo S, Ma S, Dong Y, Xin H, Wang H, Liu L, Chang W, Zhang Y.Type 2 diabetes with hypertensive patients results in changes to features of adipocytokines: Leptin, Irisin, LGR4, and Sfrp5. Clin Exp Hypertens. 2019;41(7):645-650. doi:10.1080/10641963.2018.1529779
    OpenUrlCrossRef
  142. 142.↵
    Barma M, Khan F, Price RJG, Donnan PT, Messow CM, Ford I, McConnachie A, Struthers AD, McMurdo MET, Witham MD.Association between GDF-15 levels and changes in vascular and physical function in older patients with hypertension. Aging Clin Exp Res. 2017;29(5):1055–1059. doi:10.1007/s40520-016-0636-0
    OpenUrlCrossRef
  143. 143.↵
    Oliveira SDS, Chen J, Castellon M, Mao M, Raj JU, Comhair S, Erzurum S, Silva CLM, Machado RF, Bonini MG, et al. Injury-Induced Shedding of Extracellular Vesicles Depletes Endothelial Cells of Cav-1 (Caveolin-1) and Enables TGF-β (Transforming Growth Factor-β Dependent Pulmonary Arterial Hypertension. Arterioscler Thromb Vasc Biol. 2019;39(6):1191–1202. doi:10.1161/ATVBAHA.118.312038
    OpenUrlCrossRef
  144. 144.↵
    Jiao YR, Wang W, Lei PC, Jia HP, Dong J, Gou YQ, Chen CL, Cao J, Wang YF, Zhu YK. 5-HTT, BMPR2, EDN1, ENG, KCNA5 gene polymorphisms and susceptibility to pulmonary arterial hypertension: A meta-analysis. Gene. 2019;680:34-42. doi:10.1016/j.gene.2018.09.020
    OpenUrlCrossRef
  145. 145.↵
    Yang L, Liang H, Shen L, Guan Z, Meng X. LncRNA Tug1 involves in the pulmonary vascular remodeling in mice with hypoxic pulmonary hypertension via the microRNA-374c-mediated Foxc1. Life Sci. 2019;237:116769. doi:10.1016/j.lfs.2019.116769
    OpenUrlCrossRef
  146. 146.↵
    Nitta CH, Osmond DA, Herbert LM, Beasley BF, Resta TC, Walker BR, Jernigan NL. Role of ASIC1 in the development of chronic hypoxia-induced pulmonary hypertension. Am J Physiol Heart Circ Physiol. 2014;306(1):H41–H52. doi:10.1152/ajpheart.00269.2013
    OpenUrlCrossRefPubMedWeb of Science
  147. 147.↵
    Kang Z, Ji Y, Zhang G, Qu Y, Zhang L, Jiang W. Ponatinib attenuates experimental pulmonary arterial hypertension by modulating Wnt signaling and vasohibin-2/vasohibin-1. Life Sci. 2016;148:1–8. doi:10.1016/j.lfs.2016.02.017
    OpenUrlCrossRef
  148. 148.↵
    Kim HY, Jeong DW, Kim HS. Sulfatase 2 mediates, partially, the expression of endothelin-1 and the additive effect of Ang II-induced endothelin-1 expression by CXCL8 in vascular smooth muscle cells from spontaneously hypertensive rats. Cytokine. 2019;114:98–105. doi:10.1016/j.cyto.2018.11.007
    OpenUrlCrossRef
  149. 149.↵
    Cowley AW Jr, Yang C, Kumar V, Lazar J, Jacob H, Geurts AM, Liu P, Dayton A, Kurth T, Liang M. Pappa2 is linked to salt-sensitive hypertension in Dahl S rats. Physiol Genomics. 2016;48(1):62–72. doi:10.1152/physiolgenomics.00097.2015
    OpenUrlCrossRefPubMed
  150. 150.↵
    Tomás M, Vázquez E, Fernández-Fernández JM, Subirana I, Plata C, Heras M, Vila J, Marrugat J, Valverde MA, Sentí M. Genetic variation in the KCNMA1 potassium channel alpha subunit as risk factor for severe essential hypertension and myocardial infarction. J Hypertens. 2008;26(11):2147–2153. doi:10.1097/HJH.0b013e32831103d8
    OpenUrlCrossRefPubMedWeb of Science
  151. 151.↵
    Vadasz Z, Balbir Gurman A, Meroni P, Farge D, Levi Y, Ingegnoli F, Braun-Moscovici Y, Rosner I, Slobodin G, Rozenbaum M, et al. Lysyl oxidase-a possible role in systemic sclerosis-associated pulmonary hypertension: a multicentre study. Rheumatology (Oxford). 2019;58(9):1547–1555. doi:10.1093/rheumatology/kez035
    OpenUrlCrossRef
  152. 152.↵
    Keranov S, Dörr O, Jafari L, Liebetrau C, Keller T, Troidl C, Kriechbaum S, Voss S, Richter M, Tello K, et al. SPARCL1 as a biomarker of maladaptive right ventricular remodelling in pulmonary hypertension. Biomarkers. 2020;25(3):290–295. doi:10.1080/1354750X.2020.1745889
    OpenUrlCrossRef
  153. 153.↵
    Zhang YP, Zuo XC, Huang ZJ, Cai JJ, Wen J, Duan DD, Yuan H.CYP3A5 polymorphism, amlodipine and hypertension. J Hum Hypertens. 2014;28(3):145–149. doi:10.1038/jhh.2013.67
    OpenUrlCrossRefPubMed
  154. 154.↵
    Osaro E, Charles AT. Rh isoimmunization in Sub-Saharan Africa indicates need for universal access to anti-RhD immunoglobulin and effective management of D-negative pregnancies. Int J Womens Health. 2010;2:429–437. doi:10.2147/IJWH.S15165
    OpenUrlCrossRefPubMed
  155. 155.↵
    Bersani I, De Carolis S, Foell D, Weinhage T, Garufi C, De Carolis MP, Rossi ED, Casella G, Rubortone SA, Speer CP. Impact of chorioamnionitis on maternal and fetal levels of proinflammatory S100A12. Eur J Pediatr. 2021;180(1):39–45. doi:10.1007/s00431-020-03695-4
    OpenUrlCrossRef
  156. 156.↵
    Farladansky-Gershnabel S, Heusler I, Biron-Shental T, Shechter-Maor G, Amiel A, Kidron D, Weisz A, Einbinder Y, Cohen-Hagai K, Benchetrit S, et al. Elevated expression of galectin-3, thioredoxin and thioredoxin interacting protein in preeclampsia. Pregnancy Hypertens. 2021;26:95–101. doi:10.1016/j.preghy.2021.10.003
    OpenUrlCrossRef
  157. 157.↵
    Sánchez-Luquez K, Schadock IC, Gonçalves CV, Tornatore M, Finger-Jardim F, Avila EC, Soares MA, de Martínez AMB, Ellwanger JH, Chies JAB, et al. Impact of TLR7 and TLR9 polymorphisms on susceptibility to placental infections and pregnancy complications. J Reprod Immunol. 2021;146:103342. doi:10.1016/j.jri.2021.103342
    OpenUrlCrossRef
  158. 158.↵
    Zhu HY, Tong XM, Lin XN, Jiang LY, Wang JX, Zhang SY. Expression and Distribution of Calcium-Binding Protein S100P in Human Placenta during Pregnancy. Int J Fertil Steril. 2015;8(4):445–452. doi:10.22074/ijfs.2015.4189
    OpenUrlCrossRef
  159. 159.↵
    Wang H, Zhang X, Liu C, Chen S, Liu X, Fan S. TAGLN2-Regulated Trophoblast Migration, Invasion and Fusion are Impaired in Preeclampsia. Front Cell Dev Biol. 2022;10:810633. doi:10.3389/fcell.2022.810633
    OpenUrlCrossRef
  160. 160.↵
    Ozeki A, Oogaki Y, Henmi Y, Karasawa T, Takahashi M, Takahashi H, Ohkuchi A, Shirasuna K. Elevated S100A9 in preeclampsia induces soluble endoglin and IL-1β secretion and hypertension via the NLRP3 inflammasome. J Hypertens. 2022;40(1):84–93. doi:10.1097/HJH.0000000000002981
    OpenUrlCrossRef
  161. 161.↵
    Shepherd JN, Spencer N. Human carbonic anhydrase I concentration in erythrocytes during pregnancy and the menstrual cycle. Clin Biochem. 1985;18(6):369–372. doi:10.1016/s0009-9120(85)80077-1
    OpenUrlCrossRefPubMed
  162. 162.↵
    Matos A, Pereira da Silva A, Clara Bicho M, Afonso C, Areias MJ, Rebelo I, Bicho M. In women with previous pregnancy hypertension, levels of cardiovascular risk biomarkers may be modulated by haptoglobin polymorphism. Obstet Gynecol Int. 2014;2014:361727. doi:10.1155/2014/361727
    OpenUrlCrossRef
  163. 163.↵
    Jie Q, Sun F, Li Q, Zhu J, Wei Y, Yang H, Long P, Wang Z, Yang X, Li D, et al. Downregulated ribosomal protein L39 inhibits trophoblast cell migration and invasion by targeting E-cadherin in the placenta of patients with preeclampsia. FASEB J. 2021;35(4):e21322. doi:10.1096/fj.202002061R
    OpenUrlCrossRef
  164. 164.↵
    Ridker PM, Miletich JP, Buring JE, Ariyo AA, Price DT, Manson JE, Hill JA. Factor V Leiden mutation as a risk factor for recurrent pregnancy loss. Ann Intern Med. 1998;128(12 Pt 1):1000-1003. doi:10.7326/0003-4819-128-12_part_1-199806150-00007
    OpenUrlCrossRefPubMedWeb of Science
  165. 165.↵
    Li N, Xu H, Liu X, Gao R, He J, Ding Y, Li F, Geng Y, Mu X, Chen X.Exposure to benzo(a)pyrene suppresses mitophagy via ANT1-PINK1-Parkin pathway in ovarian corpus luteum during early pregnancy. Sci Total Environ. 2022;814:152759. doi:10.1016/j.scitotenv.2021.152759
    OpenUrlCrossRef
  166. 166.↵
    Kristensen K, Wide-Swensson D, Schmidt C, Blirup-Jensen S, Lindström V, Strevens H, Grubb A. Cystatin C, beta-2-microglobulin and beta-trace protein in pre-eclampsia. Acta Obstet Gynecol Scand. 2007;86(8):921–926. doi:10.1080/00016340701318133
    OpenUrlCrossRefPubMedWeb of Science
  167. 167.↵
    Gao Y, Tang Y, Sun Q, Guan G, Wu X, Shi F, Zhou Z, Yang W. Circular RNA FOXP1 relieves trophoblastic cell dysfunction in recurrent pregnancy loss via the miR-143-3p/S100A11 cascade. Bioengineered. 2021;12(1):9081–9093. doi:10.1080/21655979.2021.1988374
    OpenUrlCrossRef
  168. 168.↵
    Huang Z, Du G, Huang X, Han L, Han X, Xu B, Zhang Y, Yu M, Qin Y, Xia Y, et al. The enhancer RNA lnc-SLC4A1-1 epigenetically regulates unexplained recurrent pregnancy loss (URPL) by activating CXCL8 and NF-kB pathway. EBioMedicine. 2018;38:162–170. doi:10.1016/j.ebiom.2018.11.015
    OpenUrlCrossRef
  169. 169.↵
    Sheibani L, Lechuga TJ, Zhang H, Hameed A, Wing DA, Kumar S, Rosenfeld CR, Chen DB. Augmented H2S production via cystathionine-beta-synthase upregulation plays a role in pregnancy-associated uterine vasodilation. Biol Reprod. 2017;96(3):664–672. doi:10.1095/biolreprod.116.143834
    OpenUrlCrossRef
  170. 170.↵
    Emanuelli M, Cecati M, Sartini D, Stortoni P, Corradetti A, Giannubilo SR, Turi A, Tranquilli AL.Placental Alpha Hemoglobin Stabilizing Protein (AHSP) and recurrent miscarriage. Cell Stress Chaperones. 2009;14(2):193–197. doi:10.1007/s12192-008-0072-y
    OpenUrlCrossRefPubMedWeb of Science
  171. 171.↵
    Ebina Y, Ieko M, Naito S, Kobashi G, Deguchi M, Minakami H, Atsumi T, Yamada H. Low levels of plasma protein S, protein C and coagulation factor XII during early pregnancy and adverse pregnancy outcome. Thromb Haemost. 2015;114(1):65–69. doi:10.1160/TH14-11-0928
    OpenUrlCrossRef
  172. 172.↵
    Liu X, Hu Y, Zheng Y, Liu X, Luo M, Liu W, Zhao Y, Zou L. EPHB4 Regulates Human Trophoblast Cell Line HTR-8/SVneo Function: Implications for the Role of EPHB4 in Preeclampsia. Biol Reprod. 2016;95(3):65. doi:10.1095/biolreprod.116.140939
    OpenUrlCrossRefPubMed
  173. 173.↵
    Kweider N, Huppertz B, Pecks U, Goecke T, Pufe T, Kadyrov M, Wruck CJ, Rath W. PP019. A new player in preeclampsia: The NF-E2-related factor 2 (NRF2). Pregnancy Hypertens. 2013;3(2):74. doi:10.1016/j.preghy.2013.04.047
    OpenUrlCrossRef
  174. 174.↵
    Li Z, Zhou X, Gao W, Sun M, Chen H, Meng T. Circular RNA VRK1 facilitates pre-eclampsia progression via sponging miR-221-3P to regulate PTEN/Akt. J Cell Mol Med. 2022;26(6):1826–1841. doi:10.1111/jcmm.16454
    OpenUrlCrossRef
  175. 175.↵
    Gao H, Liu C, Lin P, Xu L, Li X, Chen Y, Cheng B, Li A, Liu S. Effects of GSTP1 and GPX1 Polymorphisms on the Risk of Preeclampsia in Chinese Han Women. Cell Physiol Biochem. 2016;39(5):2025–2032. doi:10.1159/000447898
    OpenUrlCrossRef
  176. 176.↵
    Zhu J, Wei Y, Wang Z, Jie Q, Sun F, Li Q, Long P, Huang Y, Yu Y, Ma Y. Down-regulated FOXA1 in early-onset pre-eclampsia induces apoptosis, and inhibits migration and invasion of trophoblast cells. J Gene Med. 2020;22(12):e3273. doi:10.1002/jgm.3273
    OpenUrlCrossRef
  177. 177.↵
    Azimi-Nezhad M, Teymoori A, Ebrahimzadeh-Vesal R. Association of CYP11B2 gene polymorphism with preeclampsia in north east of Iran (Khorasan province). Gene. 2020;733:144358. doi:10.1016/j.gene.2020.144358
    OpenUrlCrossRef
  178. 178.↵
    Cui XL, Brockman D, Campos B, Myatt L. Expression of NADPH oxidase isoform 1 (Nox1) in human placenta: involvement in preeclampsia. Placenta. 2006;27(4-5):422–431. doi:10.1016/j.placenta.2005.04.004
    OpenUrlCrossRefPubMedWeb of Science
  179. 179.↵
    Sheng YR, Hu WT, Shen HH, Wei CY, Liu YK, Ma XQ, Li MQ, Zhu XY. An imbalance of the IL-33/ST2-AXL-efferocytosis axis induces pregnancy loss through metabolic reprogramming of decidual macrophages. Cell Mol Life Sci. 2022;79(3):173. doi:10.1007/s00018-022-04197-2
    OpenUrlCrossRef
  180. 180.↵
    Khaliq OP, Konoshita T, Moodley J, Naicker T. The association of NPHS1 and ACNT4 gene polymorphisms with pre-eclampsia. Eur J Obstet Gynecol Reprod Biol. 2021;266:9–14. doi:10.1016/j.ejogrb.2021.09.006
    OpenUrlCrossRef
  181. 181.↵
    Sysák R, Brennerová K, Krlín R, Štencl P, Rusňák I, Vargová M. Effect of Ornithine Transcarbamylase (OTC) Deficiency on Pregnancy and Puerperium. Diagnostics (Basel). 2022;12(2):415. doi:10.3390/diagnostics12020415
    OpenUrlCrossRef
  182. 182.↵
    Zahraei M, Sheikhha MH, Kalantar SM, Ghasemi N, Jahaninejad T, Rajabi S, Mohammadpour H. The association of arylendosulfatase 1 (SULF1) gene polymorphism with recurrent miscarriage. J Assist Reprod Genet. 2014;31(2):157–161. doi:10.1007/s10815-013-0150-7
    OpenUrlCrossRef
  183. 183.↵
    Suryanarayana V, Deenadayal M, Singh L. Association of CYP1A1 gene polymorphism with recurrent pregnancy loss in the South Indian population. Hum Reprod. 2004;19(11):2648–2652. doi:10.1093/humrep/deh463
    OpenUrlCrossRefPubMedWeb of Science
  184. 184.↵
    Halari CD, Zheng M, Lala PK. Roles of Two Small Leucine-Rich Proteoglycans Decorin and Biglycan in Pregnancy and Pregnancy-Associated Diseases. Int J Mol Sci. 2021;22(19):10584. doi:10.3390/ijms221910584
    OpenUrlCrossRef
  185. 185.↵
    Zhang L, Zhao F, Li C, Li H, Tang Q, Chen Y, Yao Y, Ding Z, Xu Y, Chen A, et al. Hypomethylation of DNA promoter upregulates ADAMTS7 and contributes to HTR-8/SVneo and JEG-3 cells abnormalities in pre-eclampsia. Placenta. 2020;93:26–33. doi:10.1016/j.placenta.2020.02.013
    OpenUrlCrossRef
  186. 186.↵
    Wang G, Zhang Z, Chen C, Zhang Y, Zhang C. Dysfunction of WNT4/WNT5A in deciduas: possible relevance to the pathogenesis of preeclampsia. J Hypertens. 2016;34(4):719–727. doi:10.1097/HJH.0000000000000851
    OpenUrlCrossRef
  187. 187.↵
    Ji Y, Zhou L, Wang G, Qiao Y, Tian Y, Feng Y. Role of LAMA4 Gene in Regulating Extravillous Trophoblasts in Pathogenesis of Preeclampsia. Med Sci Monit. 2019;25:9630–9636. doi:10.12659/MSM.917402
    OpenUrlCrossRef
  188. 188.↵
    Zaharieva IT, Thor MG, Oates EC, van Karnebeek C, Hendson G, Blom E, Witting N, Rasmussen M, Gabbett MT, Ravenscroft G, et al. Loss-of-function mutations in SCN4A cause severe foetal hypokinesia or ‘classical’ congenital myopathy. Brain. 2016;139(Pt 3):674–691. doi:10.1093/brain/awv352
    OpenUrlCrossRefPubMed
  189. 189.↵
    Xin Q, Xin G, Li L, Sun W, Jiang W, Wang J, Luan Y, Zhang Y, Cheng L, Duan S, et al. Association study of hypertension susceptibility genes ITGA9, MOV10, and CACNB2 with preeclampsia in Chinese Han population. J Matern Fetal Neonatal Med. 2021;1-9. doi:10.1080/14767058.2021.1876022
    OpenUrlCrossRef
  190. 190.↵
    Sheppard R, Hsich E, Damp J, Elkayam U, Kealey A, Ramani G, Zucker M, Alexis JD, Horne BD, Hanley-Yanez K, et al. GNB3 C825T Polymorphism and Myocardial Recovery in Peripartum Cardiomyopathy: Results of the Multicenter Investigations of Pregnancy-Associated Cardiomyopathy Study. Circ Heart Fail. 2016;9(3):e002683. doi:10.1161/CIRCHEARTFAILURE.115.002683
    OpenUrlAbstract/FREE Full Text
  191. 191.↵
    Aung M, Konoshita T, Moodley J, Naicker T, Connolly C, Khaliq OP, Gathiram P.Aminopeptidase A (ENPEP) gene polymorphisms and preeclampsia: Descriptive analysis. Eur J Obstet Gynecol Reprod Biol. 2021;258:70–74. doi:10.1016/j.ejogrb.2020.12.051
    OpenUrlCrossRef
  192. 192.↵
    Mayan H, Mouallem M, Shaharabany M, Pauzner R, Farfel Z. Resolution of hypertension during pregnancy in familial hyperkalemia and hypertension with the WNK4 Q565E mutation. Am J Obstet Gynecol. 2005;192(2):598–603. doi:10.1016/j.ajog.2004.07.020
    OpenUrlCrossRefPubMed
  193. 193.↵
    Cao S, Zhang S. Forkhead-box C1 attenuates high glucose-induced trophoblast cell injury during gestational diabetes mellitus via activating adenosine monophosphate-activated protein kinase through regulating fibroblast growth factor 19. Bioengineered. 2022;13(1):1174–1184. doi:10.1080/21655979.2021.2018094
    OpenUrlCrossRef
  194. 194.↵
    Martin-Montalvo A, López-Noriega L, Jiménez-Moreno C, Herranz A, Lorenzo PI, Cobo-Vuilleumier N, Tamayo A, González-Guerrero C, Hofsteede JSWR, Lebreton F, Bosco D, et al. Transient PAX8 Expression in Islets During Pregnancy Correlates With β Cell Survival, Revealing a Novel Candidate Gene in Gestational Diabetes Mellitus. Diabetes. 2019;68(1):109–118. doi:10.2337/db18-0285
    OpenUrlAbstract/FREE Full Text
  195. 195.↵
    Li P, Shi Y, Shuai H, Cai Y, Lu W, Wang G, Gao L, Wang L, Fan X, Yang X. Alterted SLIT2/ROBO1 signalling is linked to impaired placentation of missed and threatened miscarriage in early pregnancy. Histopathology. 2017;71(4):543–552. doi:10.1111/his.13250
    OpenUrlCrossRef
  196. 196.↵
    Huang Z, Du G, Huang X, Han L, Han X, Xu B, Zhang Y, Yu M, Qin Y, Xia Y, et al. The enhancer RNA lnc-SLC4A1-1 epigenetically regulates unexplained recurrent pregnancy loss (URPL) by activating CXCL8 and NF-kB pathway. EBioMedicine. 2018;38:162–170. doi:10.1016/j.ebiom.2018.11.015
    OpenUrlCrossRef
  197. 197.↵
    Macintire K, Tuohey L, Ye L, Palmer K, Gantier M, Tong S, Kaitu’u-Lino TJ. PAPPA2 is increased in severe early onset pre-eclampsia and upregulated with hypoxia. Reprod Fertil Dev. 2014;26(2):351–357. doi:10.1071/RD12384
    OpenUrlCrossRef
  198. 198.↵
    Mäki JM, Räsänen J, Tikkanen H, Sormunen R, Mäkikallio K, Kivirikko KI, Soininen R. Inactivation of the lysyl oxidase gene Lox leads to aortic aneurysms, cardiovascular dysfunction, and perinatal death in mice. Circulation. 2002;106(19):2503–2509. doi:10.1161/01.cir.0000038109.84500.1e
    OpenUrlAbstract/FREE Full Text
  199. 199.↵
    Laskowska M, Laskowska K, Oleszczuk J. The relation of maternal serum eNOS, NOSTRIN and ADMA levels with aetiopathogenesis of preeclampsia and/or intrauterine fetal growth restriction. J Matern Fetal Neonatal Med. 2015;28(1):26–32. doi:10.3109/14767058.2014.900036
    OpenUrlCrossRef
  200. 200.↵
    Tyler C, Kapur A, Felder M, Belisle JA, Trautman C, Gubbels JA, Connor JP, Patankar MS. The mucin MUC16 (CA125) binds to NK cells and monocytes from peripheral blood of women with healthy pregnancy and preeclampsia. Am J Reprod Immunol. 2012;68(1):28–37. doi:10.1111/j.1600-0897.2012.01113.x
    OpenUrlCrossRefPubMed
  201. 201.↵
    Kısa B, Sert UY, Celik HK, Candar T, Erol Koç EM, Taşcı Y, Çağlar GS.Myo-inositol oxygenese activity in second trimester of pregnancy: altered myoinositol catabolism in gestational diabetes mellitus. Arch Physiol Biochem. 2020;1–4. doi:10.1080/13813455.2020.1739714
    OpenUrlCrossRef
  202. 202.↵
    Pan T, He G, Chen M, Bao C, Chen Y, Liu G, Zhou M, Li S, Xu W, Liu X. Abnormal CYP11A1 gene expression induces excessive autophagy, contributing to the pathogenesis of preeclampsia. Oncotarget. 2017;8(52):89824–89836. doi:10.18632/oncotarget.21158
    OpenUrlCrossRef
  203. 203.↵
    Coto E, Tavira B, Marín R, Ortega F, López-Larrea C, Ruiz-Ortega M, Ortiz A, Díaz M, Corao AI, Alonso B, et al. Functional polymorphisms in the CYP3A4, CYP3A5, and CYP21A2 genes in the risk for hypertension in pregnancy. Biochem Biophys Res Commun. 2010;397(3):576–579. doi:10.1016/j.bbrc.2010.06.003
    OpenUrlCrossRefPubMedWeb of Science
  204. 204.↵
    Gotsman I, Keren A, Zwas DR, Lotan C, Admon D. Clinical Impact of ABO and Rhesus D Blood Type Groups in Patients With Chronic Heart Failure. Am J Cardiol. 2018;122(3):413–419. doi:10.1016/j.amjcard.2018.04.018
    OpenUrlCrossRef
  205. 205.↵
    Buyukterzi Z, Can U, Alpaydin S, Guzelant A, Karaarslan S, Kocyigit D, Gurses KM. Enhanced S100A9 and S100A12 expression in acute coronary syndrome. Biomark Med. 2017;11(3):229–237. doi:10.2217/bmm-2016-0253
    OpenUrlCrossRef
  206. 206.↵
    Roshan MH, Tambo A, Pace NP. The Role of TLR2, TLR4, and TLR9 in the Pathogenesis of Atherosclerosis. Int J Inflam. 2016;2016:1532832. doi:10.1155/2016/1532832
    OpenUrlCrossRef
  207. 207.↵
    Meng H, Zhang Y, An ST, Chen Y. Annexin A3 gene silencing promotes myocardial cell repair through activation of the PI3K/Akt signaling pathway in rats with acute myocardial infarction. J Cell Physiol. 2019;234(7):10535–10546. doi:10.1002/jcp.27717
    OpenUrlCrossRef
  208. 208.↵
    Cai XY, Lu L, Wang YN, Jin C, Zhang RY, Zhang Q, Chen QJ, Shen WF.Association of increased S100B, S100A6 and S100P in serum levels with acute coronary syndrome and also with the severity of myocardial infarction in cardiac tissue of rat models with ischemia-reperfusion injury. Atherosclerosis. 2011;217(2):536–542. doi:10.1016/j.atherosclerosis.2011.05.023
    OpenUrlCrossRefPubMed
  209. 209.↵
    Li Z, Ye Z, Ma J, Gu Q, Teng J, Gong X. MicroRNA 133b alleviates doxorubicin_induced cardiomyocyte apoptosis and cardiac fibrosis by targeting PTBP1 and TAGLN2. Int J Mol Med. 2021;48(1):125. doi:10.3892/ijmm.2021.4958
    OpenUrlCrossRef
  210. 210.↵
    Marinković G, Koenis DS, de Camp L, Jablonowski R, Graber N, de Waard V, de Vries CJ, Goncalves I, Nilsson J, Jovinge S, et al. S100A9 Links Inflammation and Repair in Myocardial Infarction. Circ Res. 2020;127(5):664–676. doi:10.1161/CIRCRESAHA.120.315865
    OpenUrlCrossRef
  211. 211.↵
    Wang QS, Wang XF, Chen XD, Yu JF, Wang J, Sun J, Lu SB, Shen MY, Lu M, Li YG, et al. Genetic polymorphism of KCNH2 confers predisposition of acquired atrial fibrillation in Chinese. J Cardiovasc Electrophysiol. 2009;20(10):1158–1162. doi:10.1111/j.1540-8167.2009.01494.x
    OpenUrlCrossRefPubMed
  212. 212.↵
    Lee CW, Cheng TM, Lin CP, Pan JP. Plasma haptoglobin concentrations are elevated in patients with coronary artery disease. 2014;9(1). PLoS One. 2013;8(10):e76817. doi:10.1371/journal.pone.0076817
    OpenUrlCrossRefPubMed
  213. 213.↵
    Kühn EC, Slagman A, Kühn-Heid ECD, Seelig J, Schwiebert C, Minich WB, Stoppe C, Möckel M, Schomburg L. Circulating levels of selenium-binding protein 1 (SELENBP1) are associated with risk for major adverse cardiac events and death. J Trace Elem Med Biol. 2019;52:247–253. doi:10.1016/j.jtemb.2019.01.005
    OpenUrlCrossRef
  214. 214.↵
    Lalani SR, Liu P, Rosenfeld JA, Watkin LB, Chiang T, Leduc MS, Zhu W, Ding Y, Pan S, Vetrini F, et al. Recurrent Muscle Weakness with Rhabdomyolysis, Metabolic Crises, and Cardiac Arrhythmia Due to Bi-allelic TANGO2 Mutations. Am J Hum Genet. 2016;98(2):347-357. doi:10.1016/j.ajhg.2015.12.008
    OpenUrlCrossRefPubMed
  215. 215.↵
    Zhang CL, Long TY, Bi SS, Sheikh SA, Li F. CircPAN3 ameliorates myocardial ischaemia/reperfusion injury by targeting miR-421/Pink1 axis-mediated autophagy suppression. Lab Invest. 2021;101(1):89–103. doi:10.1038/s41374-020-00483-4
    OpenUrlCrossRef
  216. 216.↵
    Shi Y, Lian Y, Zhou L, Huang L, Zhang X, Guo H, Wu T, Cheng L, He M. Association between genetic variations in TFR2 gene and coronary heart disease in Chinese: a case-control study. J Cardiovasc Med (Hagerstown). 2014;15(5):397–401. doi:10.2459/JCM.0b013e32836206f3
    OpenUrlCrossRef
  217. 217.↵
    Shi F, Sun L, Kaptoge S. Association of beta-2-microglobulin and cardiovascular events and mortality: A systematic review and meta-analysis. Atherosclerosis. 2021;320:70–78. doi:10.1016/j.atherosclerosis.2021.01.018
    OpenUrlCrossRef
  218. 218.↵
    Chen HX, Yang ZY, Hou HT, Wang J, Wang XL, Yang Q, Liu L, He GW. Novel mutations of TCTN3/LTBP2 with cellular function changes in congenital heart disease associated with polydactyly. J Cell Mol Med. 2020;24(23):13751–13762. doi:10.1111/jcmm.15950
    OpenUrlCrossRef
  219. 219.↵
    Han Y, Hua S, Chen Y, Yang W, Zhao W, Huang F, Qiu Z, Yang C, Jiang J, Su X, et al. Circulating PGLYRP1 Levels as a Potential Biomarker for Coronary Artery Disease and Heart Failure. J Cardiovasc Pharmacol. 2021;77(5):578–585. doi:10.1097/FJC.0000000000000996
    OpenUrlCrossRef
  220. 220.↵
    He GH, Cai WK, Zhang JB, Ma CY, Yan F, Lu J, Xu GL.Associations of Polymorphisms in HRH2, HRH3, DAO, and HNMT Genes with Risk of Chronic Heart Failure. Biomed Res Int. 2016;2016:1208476. doi:10.1155/2016/1208476
    OpenUrlCrossRef
  221. 221.↵
    Wang X, Hu Y, Wang Y, Shen D, Tao G. CLEC5A knockdown protects against cardiac dysfunction after myocardial infarction by suppressing macrophage polarization, NLRP3 inflammasome activation, and pyroptosis. Biochem Cell Biol. 2021;99(5):655-665. doi:10.1139/bcb-2020-0672
    OpenUrlCrossRef
  222. 222.↵
    Lv L, Li T, Li X, Xu C, Liu Q, Jiang H, Li Y, Liu Y, Yan H, Huang Q, et al. The lncRNA Plscr4 Controls Cardiac Hypertrophy by Regulating miR-214. Mol Ther Nucleic Acids. 2018;10:387–397. doi:10.1016/j.omtn.2017.12.018
    OpenUrlCrossRef
  223. 223.↵
    Chen Y, Huang M, Yan Y, He D. Tranilast inhibits angiotensin II-induced myocardial fibrosis through S100A11/ transforming growth factor-β(TGF-β1)/Smad axis. Bioengineered. 2021;12(1):8447–8456. doi:10.1080/21655979.2021.1982322
    OpenUrlCrossRef
  224. 224.↵
    Maneerat Y, Prasongsukarn K, Benjathummarak S, Dechkhajorn W. PPBP and DEFA1/DEFA3 genes in hyperlipidaemia as feasible synergistic inflammatory biomarkers for coronary heart disease. Lipids Health Dis. 2017;16(1):80. doi:10.1186/s12944-017-0471-0
    OpenUrlCrossRef
  225. 225.↵
    Gao Y, Zhao D, Xie WZ, Meng T, Xu C, Liu Y, Zhang P, Bi X, Zhao Z. Rap1GAP Mediates Angiotensin II-Induced Cardiomyocyte Hypertrophy by Inhibiting Autophagy and Increasing Oxidative Stress. Oxid Med Cell Longev. 2021;2021:7848027. doi:10.1155/2021/7848027
    OpenUrlCrossRef
  226. 226.↵
    Nordström M, Kjellström T. Age dependency of cystathionine beta-synthase activity in human fibroblasts in homocyst(e)inemia and atherosclerotic vascular disease. Atherosclerosis. 1992;94(2-3):213–221. doi:10.1016/0021-9150(92)90246-d
    OpenUrlCrossRefPubMedWeb of Science
  227. 227.↵
    Sonnenschein K, Fiedler J, Pfanne A, Just A, Mitzka S, Geffers R, Pich A, Bauersachs J, Thum T. Therapeutic modulation of RNA-binding protein Rbm38 facilitates re-endothelialization after arterial injury. Cardiovasc Res. 2019;115(12):1804–1810. doi:10.1093/cvr/cvz063
    OpenUrlCrossRef
  228. 228.↵
    Ghazizadeh Z, Kiviniemi T, Olafsson S, Plotnick D, Beerens ME, Zhang K, Gillon L, Steinbaugh MJ, Barrera V, Sui SH, et al. Metastable Atrial State Underlies the Primary Genetic Substrate for MYL4 Mutation-Associated Atrial Fibrillation. Circulation. 2020;141(4):301–312. doi:10.1161/CIRCULATIONAHA.119.044268
    OpenUrlCrossRef
  229. 229.↵
    Zeng X, Hunt A, Jin SC, Duran D, Gaillard J, Kahle KT. EphrinB2-EphB4-RASA1 Signaling in Human Cerebrovascular Development and Disease. Trends Mol Med. 2019;25(4):265–286. doi:10.1016/j.molmed.2019.01.009
    OpenUrlCrossRefPubMed
  230. 230.↵
    Guay SP, Légaré C, Brisson D, Mathieu P, Bossé Y, Gaudet D, Bouchard L. Epigenetic and genetic variations at the TNNT1 gene locus are associated with HDL-C levels and coronary artery disease. Epigenomics. 2016;8(3):359–371. doi:10.2217/epi.15.120
    OpenUrlCrossRef
  231. 231.↵
    Yang L, Wang B, Zhou Q, Wang Y, Liu X, Liu Z, Zhan Z.MicroRNA-21 prevents excessive inflammation and cardiac dysfunction after myocardial infarction through targeting KBTBD7. Cell Death Dis. 2018;9(7):769. doi:10.1038/s41419-018-0805-5
    OpenUrlCrossRef
  232. 232.↵
    Yeh HL, Kuo LT, Sung FC, Yeh CC. Association between Polymorphisms of Antioxidant Gene (MnSOD, CAT, and GPx1) and Risk of Coronary Artery Disease. Biomed Res Int. 2018;2018:5086869. doi:10.1155/2018/5086869
    OpenUrlCrossRef
  233. 233.↵
    Wang J, Hao D, Zeng L, Zhang Q, Huang W. Neuropeptide Y mediates cardiac hypertrophy through microRNA-216b/FoxO4 signaling pathway. Int J Med Sci. 2021;18(1):18–28. doi:10.7150/ijms.51133
    OpenUrlCrossRef
  234. 234.↵
    Liu F, Su H, Liu B, Mei Y, Ke Q, Sun X, Tan W. STVNa Attenuates Isoproterenol-Induced Cardiac Hypertrophy Response through the HDAC4 and Prdx2/ROS/Trx1 Pathways. Int J Mol Sci. 2020;21(2):682. doi:10.3390/ijms21020682
    OpenUrlCrossRef
  235. 235.↵
    Sun Y, Chen C, Xue R, Wang Y, Dong B, Li J, Chen C, Jiang J, Fan W, Liang Z, et al. Maf1 ameliorates cardiac hypertrophy by inhibiting RNA polymerase III through ERK1/2. Theranostics. 2019;9(24):7268–7281. doi:10.7150/thno.33006
    OpenUrlCrossRef
  236. 236.↵
    Li L, Li ZB, Jia M, Chu HT. Therapeutic effects of KANK2 in myocardial infarction rats might be associated with the NF-κB p65 inhibition. Int Immunopharmacol. 2020;86:106687. doi:10.1016/j.intimp.2020.106687
    OpenUrlCrossRef
  237. 237.↵
    Wu Y, Liu X, Zheng H, Zhu H, Mai W, Huang X, Huang Y. Multiple Roles of sFRP2 in Cardiac Development and Cardiovascular Disease. Int J Biol Sci. 2020;16(5):730–738. doi:10.7150/ijbs.40923
    OpenUrlCrossRef
  238. 238.↵
    Scuruchi M, Potì F, Rodríguez-Carrio J, Campo GM, Mandraffino G. Biglycan and atherosclerosis: Lessons from high cardiovascular risk conditions. Biochim Biophys Acta Mol Cell Biol Lipids. 2020;1865(2):158545. doi:10.1016/j.bbalip.2019.158545
    OpenUrlCrossRef
  239. 239.↵
    Garnier S, Hengstenberg C, Lamblin N, Dubourg O, De Groote P, Fauchier L, Trochu JN, Arbustini E, Esslinger U, Barton PJ, et al. Involvement of BAG3 and HSPB7 loci in various etiologies of systolic heart failure: Results of a European collaboration assembling more than 2000 patients. Int J Cardiol. 2015;189:105–107. doi:10.1016/j.ijcard.2015.04.003
    OpenUrlCrossRefPubMed
  240. 240.↵
    Yu XH, Qian K, Jiang N, Zheng XL, Cayabyab FS, Tang CK. ABCG5/ABCG8 in cholesterol excretion and atherosclerosis. Clin Chim Acta. 2014;428:82–88. doi:10.1016/j.cca.2013.11.010
    OpenUrlCrossRef
  241. 241.↵
    Takeda Y, Demura M, Yoneda T, Takeda Y. DNA Methylation of the Angiotensinogen Gene, AGT, and the Aldosterone Synthase Gene, CYP11B2 in Cardiovascular Diseases. Int J Mol Sci. 2021;22(9):4587. doi:10.3390/ijms22094587
    OpenUrlCrossRef
  242. 242.↵
    Er LK, Hsu LA, Juang JJ, Chiang FT, Teng MS, Tzeng IS, Wu S, Lin JF, Ko YL. Circulating Chemerin Levels, but not the RARRES2 Polymorphisms, Predict the Long-Term Outcome of Angiographically Confirmed Coronary Artery Disease. Int J Mol Sci. 2019;20(5):1174. doi:10.3390/ijms20051174
    OpenUrlCrossRef
  243. 243.↵
    Gimenez M, Schickling BM, Lopes LR, Miller FJ Jr. Nox1 in cardiovascular diseases: regulation and pathophysiology. Clin Sci (Lond). 2016;130(3):151–165. doi:10.1042/CS20150404
    OpenUrlCrossRefPubMed
  244. 244.↵
    Jia EZ, Wang J, Yang ZJ, Zhu TB, Wang LS, Wang H, Li CJ, Chen B, Cao KJ, Huang J, et al. Association of the mutation for the human carboxypeptidase E gene exon 4 with the severity of coronary artery atherosclerosis. Mol Biol Rep. 2009;36(2):245–254. doi:10.1007/s11033-007-9173-4
    OpenUrlCrossRefPubMedWeb of Science
  245. 245.↵
    Sun Y, Pavey H, Wilkinson I, Fisk M. Role of the IL-33/ST2 axis in cardiovascular disease: A systematic review and meta-analysis. PLoS One. 2021;16(11):e0259026. doi:10.1371/journal.pone.0259026
    OpenUrlCrossRef
  246. 246.↵
    Eskandari M, Awsat Mellati A, Mahmoodi K, Kamali K, Soltanpour MS. Association of the CYP1A1 rs4646903 polymorphism with susceptibility and severity of coronary artery disease. Mol Biol Res Commun. 2021;10(2):22–61. doi:10.22099/mbrc.2021.39141.1574
    OpenUrlCrossRef
  247. 247.↵
    Hellström M, Ericsson M, Johansson B, Faraz M, Anderson F, Henriksson R, Nilsson SK, Hedman H.Cardiac hypertrophy and decreased high-density lipoprotein cholesterol in Lrig3-deficient mice. Am J Physiol Regul Integr Comp Physiol. 2016;310(11):R1045–R1052. doi:10.1152/ajpregu.00309.2015
    OpenUrlCrossRefPubMed
  248. 248.↵
    Wang P, Qin W, Wang P, Huang Y, Liu Y, Zhang R, Li S, Yang Q, Wang X, Chen F, Liu J, Yang B, et al. Genomic Variants in NEURL, GJA1 and CUX2 Significantly Increase Genetic Susceptibility to Atrial Fibrillation. Sci Rep. 2018;8(1):3297. doi:10.1038/s41598-018-21611-7
    OpenUrlCrossRef
  249. 249.↵
    Fujimaki T, Kato K, Yokoi K, Oguri M, Yoshida T, Watanabe S, Metoki N, Yoshida H, Satoh K, Aoyagi Y, et al. Association of genetic variants in SEMA3F, CLEC16A, LAMA3, and PCSK2 with myocardial infarction in Japanese individuals. Atherosclerosis. 2010;210(2):468-473. doi:10.1016/j.atherosclerosis.2009.11.050
    OpenUrlCrossRefPubMedWeb of Science
  250. 250.↵
    Schroer AK, Bersi MR, Clark CR, Zhang Q, Sanders LH, Hatzopoulos AK, Force TL, Majka SM, Lal H, Merryman WD. Cadherin-11 blockade reduces inflammation-driven fibrotic remodeling and improves outcomes after myocardial infarction. JCI Insight. 2019;4(18):e131545. doi:10.1172/jci.insight.131545
    OpenUrlCrossRef
  251. 251.↵
    Vu TT, Marquez J, Le LT, Nguyen ATT, Kim HK, Han J. The role of decorin in cardiovascular diseases: more than just a decoration. Free Radic Res. 2018;52(11-12):1210–1219. doi:10.1080/10715762.2018.1516285
    OpenUrlCrossRef
  252. 252.↵
    Zhao Y, Zhang Y, Guo Z, Ma Z, Liu Y, Han C, Yang X, Zhao L. Elevated Plasma Thymic Stromal Lymphopoietin After Acute Myocardial Infarction. Front Cardiovasc Med. 2022;9:685677. doi:10.3389/fcvm.2022.685677
    OpenUrlCrossRef
  253. 253.↵
    MacDonald BT, Keshishian H, Mundorff CC, Arduini A, Lai D, Bendinelli K, Popp NR, Bhandary B, Clauser KR, Specht H, et al. TAILS Identifies Candidate Substrates and Biomarkers of ADAMTS7, a Therapeutic Protease Target in Coronary Artery Disease. Mol Cell Proteomics. 2022;21(4):100223. doi:10.1016/j.mcpro.2022.100223
    OpenUrlCrossRef
  254. 254.↵
    Wu L, Gao L, Zhang D, Yao R, Huang Z, Du B, Wang Z, Xiao L, Li P, Li Y, et al. C1QTNF1 attenuates angiotensin II-induced cardiac hypertrophy via activation of the AMPKa pathway. Free Radic Biol Med. 2018;121:215–230. doi:10.1016/j.freeradbiomed.2018.05.004
    OpenUrlCrossRef
  255. 255.↵
    Shao B, Snell-Bergeon JK, Pyle LL, Thomas KE, de Boer IH, Kothari V, Segrest J, Davidson WS, Bornfeldt KE, Heinecke JW. Pulmonary surfactant protein B carried by HDL predicts incident CVD in patients with type 1 diabetes. J Lipid Res. 2022;63(4):100196. doi:10.1016/j.jlr.2022.100196
    OpenUrlCrossRef
  256. 256.↵
    Yin C, Ye Z, Wu J, Huang C, Pan L, Ding H, Zhong L, Guo L, Zou Y, Wang X, et al. Elevated Wnt2 and Wnt4 activate NF-κB signaling to promote cardiac fibrosis by cooperation of Fzd4/2 and LRP6 following myocardial infarction. EBioMedicine. 2021;74:103745. doi:10.1016/j.ebiom.2021.103745
    OpenUrlCrossRef
  257. 257.↵
    Thomsen SB, Rathcke CN, Zerahn B, Vestergaard H. Increased levels of the calcification marker matrix Gla Protein and the inflammatory markers YKL-40 and CRP in patients with type 2 diabetes and ischemic heart disease. Cardiovasc Diabetol. 2010;9:86. doi:10.1186/1475-2840-9-86
    OpenUrlCrossRefPubMed
  258. 258.↵
    Man W, Gu J, Wang B, Zhang M, Hu J, Lin J, Sun D, Xiong Z, Gu X, Hao K, et al. SHANK3 Co-ordinately Regulates Autophagy and Apoptosis in Myocardial Infarction. Front Physiol. 2020;11:1082. doi:10.3389/fphys.2020.01082
    OpenUrlCrossRef
  259. 259.↵
    Kim JY, Choi BG, Jelinek J, Kim DH, Lee SH, Cho K, Rha SH, Lee YH, Jin HS, Choi DK, et al. Promoter methylation changes in ALOX12 and AIRE1: novel epigenetic markers for atherosclerosis. Clin Epigenetics. 2020;12(1):66. doi:10.1186/s13148-020-00846-0
    OpenUrlCrossRef
  260. 260.↵
    Lahm H, Dreßen M, Beck N, Doppler S, Deutsch MA, Matsushima S, Neb I, König KC, Sideris K, Voss S, et al. Myosin binding protein H-like (MYBPHL): a promising biomarker to predict atrial damage. Sci Rep. 2019;9(1):9986. doi:10.1038/s41598-019-46123-w
    OpenUrlCrossRef
  261. 261.↵
    Maitra A, Shanker J, Dash D, Sannappa PR, John S, Siwach P, Rao VS, Sridhara H, Kakkar VV. Polymorphisms in the pituitary growth hormone gene and its receptor associated with coronary artery disease in a predisposed cohort from India. J Genet. 2010;89(4):437–447. doi:10.1007/s12041-010-0062-6
    OpenUrlCrossRefPubMed
  262. 262.↵
    Maitusong B, Xie X, Ma YT, Fu ZY, Yang YN, Li XM, Liu F, Chen BD, Gai MT. Association between ErbB3 genetic polymorphisms and coronary artery disease in the Han and Uyghur populations of China. Int J Clin Exp Med. 2015;8(9):16520–16527
    OpenUrl
  263. 263.↵
    Rydzewski A, Sakata K, Kobayashi A, Yamazaki N, Urano T, Takada Y, Takada A. Changes in plasminogen activator inhibitor 1 and tissue-type plasminogen activator during exercise in patients with coronary artery disease. Haemostasis. 1990;20(5):305–312. doi:10.1159/000216142
    OpenUrlCrossRefPubMedWeb of Science
  264. 264.↵
    Wang X, Chen W, Zhang J, Khan A, Li L, Huang F, Qiu Z, Wang L, Chen X. Critical Role of ADAMTS2 (A Disintegrin and Metalloproteinase With Thrombospondin Motifs 2) in Cardiac Hypertrophy Induced by Pressure Overload. Hypertension. 2017;69(6):1060–1069. doi:10.1161/HYPERTENSIONAHA.116.08581
    OpenUrlAbstract/FREE Full Text
  265. 265.↵
    Ling TY, Wang XL, Chai Q, Lu T, Stulak JM, Joyce LD, Daly RC, Greason KL, Wu LQ, Shen WK, et al. Regulation of cardiac CACNB2 by microRNA-499: Potential role in atrial fibrillation. BBA Clin. 2017;7:78–84. doi:10.1016/j.bbacli.2017.02.002
    OpenUrlCrossRef
  266. 266.↵
    Bhanushali AA, Pradhan G, Contractor A, Das BR. Frequency and association of disabled homolog 2-interacting protein (DAB2IP) variant rs7025486 G>A with coronary artery disease risk in Indian population. Indian Heart J. 2018;70 Suppl 3(Suppl 3): S480–S482. doi:10.1016/j.ihj.2018.06.016
    OpenUrlCrossRef
  267. 267.↵
    Morishita T, Uzui H, Ikeda H, Amaya N, Kaseno K, Ishida K, Fukuoka Y, Lee JD, Tada H. Association of CD34/CD133/VEGFR2-Positive Cell Numbers with Eicosapentaenoic Acid and Postprandial Hyperglycemia in Patients with Coronary Artery Disease. Int J Cardiol. 2016;221:1039–1042. doi:10.1016/j.ijcard.2016.07.079
    OpenUrlCrossRef
  268. 268.↵
    Connelly JJ, Cherepanova OA, Doss JF, Karaoli T, Lillard TS, Markunas CA, Nelson S, Wang T, Ellis PD, Langford CF, et al. Epigenetic regulation of COL15A1 in smooth muscle cell replicative aging and atherosclerosis. Hum Mol Genet. 2013;22(25):5107–5120. doi:10.1093/hmg/ddt365
    OpenUrlCrossRefPubMed
  269. 269.↵
    Schafer MJ, Atkinson EJ, Vanderboom PM, Kotajarvi B, White TA, Moore MM, Bruce CJ, Greason KL, Suri RM, Khosla S, et al. Quantification of GDF11 and Myostatin in Human Aging and Cardiovascular Disease. Cell Metab. 2016;23(6):1207–1215. doi:10.1016/j.cmet.2016.05.023
    OpenUrlCrossRefPubMed
  270. 270.↵
    Xia Y, Bhattacharyya A, Roszell EE, Sandig M, Mequanint K. The role of endothelial cell-bound Jagged1 in Notch3-induced human coronary artery smooth muscle cell differentiation. Biomaterials. 2012;33(8):2462–2472. doi:10.1016/j.biomaterials.2011.12.001
    OpenUrlCrossRefPubMed
  271. 271.↵
    Cai H, Wang XL, Wilcken DE. Genetic polymorphism of heparan sulfate proteoglycan (perlecan, HSPG2), lipid profiles and coronary artery disease in the Australian population. Atherosclerosis. 2000;148(1):125–129. doi:10.1016/s0021-9150(99)00213-0
    OpenUrlCrossRef
  272. 272.↵
    Bressler J, Franceschini N, Demerath EW, Mosley TH, Folsom AR, Boerwinkle E. Sequence variation in telomerase reverse transcriptase (TERT) as a determinant of risk of cardiovascular disease: the Atherosclerosis Risk in Communities (ARIC) study. BMC Med Genet. 2015;16:52. doi:10.1186/s12881-015-0194-x
    OpenUrlCrossRef
  273. 273.↵
    Renner W, Hoffmann MM, Grünbacher G, Winkelmann BR, Boehm BO, März W. G-protein beta3 subunit (GNB3) gene polymorphisms and cardiovascular disease: the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. Atherosclerosis. 2007;192(1):108–112. doi:10.1016/j.atherosclerosis.2006.07.001
    OpenUrlCrossRefPubMedWeb of Science
  274. 274.↵
    Abdelrahman HA, Akawi N, Al-Shamsi AM, Ali A, Al-Jasmi F, John A, Hertecant J, Al-Gazali L, Ali BR. Bi-allelic null variant in matrix metalloproteinase-15, causes congenital cardiac defect, cholestasis jaundice, and failure to thrive. Clin Genet. 2022;101(4):403–410. doi:10.1111/cge.14107
    OpenUrlCrossRef
  275. 275.↵
    Yari A, Saleh-Gohari N, Mirzaee M, Hashemi F, Saeidi K. A Study of Associations Between rs9349379 (PHACTR1), rs2891168 (CDKN2B-AS), rs11838776 (COL4A2) and rs4880 (SOD2) Polymorphic Variants and Coronary Artery Disease in Iranian Population. Biochem Genet. 2022;60(1):106-126. doi:10.1007/s10528-021-10089-0
    OpenUrlCrossRef
  276. 276.↵
    Li X, Ma YT, Xie X, Yang YN, Ma X, Zheng YY, Pan S, Liu F, Chen BD. Association of Egr3 genetic polymorphisms and coronary artery disease in the Uygur and Han of China. Lipids Health Dis. 2014;13:84. doi:10.1186/1476-511X-13-84
    OpenUrlCrossRef
  277. 277.↵
    Fochi S, Lorenzi P, Galasso M, Stefani C, Trabetti E, Zipeto D, Romanelli MG. The Emerging Role of the RBM20 and PTBP1 Ribonucleoproteins in Heart Development and Cardiovascular Diseases. Genes (Basel). 2020;11(4):402. doi:10.3390/genes11040402
    OpenUrlCrossRef
  278. 278.↵
    Dobrev D, Wehrens XH. Role of RyR2 phosphorylation in heart failure and arrhythmias: Controversies around ryanodine receptor phosphorylation in cardiac disease. Circ Res. 2014;114(8):1311–1319. doi:10.1161/CIRCRESAHA.114.300568
    OpenUrlAbstract/FREE Full Text
  279. 279.↵
    Tian D, Qin Q, Liu R, Wang Z, Li X, Xu Q, Lv Q. Diagnostic Value of Circulating Progranulin and Its Receptor EphA2 in Predicting the Atheroma Burden in Patients with Coronary Artery Disease. Dis Markers. 2021;2021:6653501. doi:10.1155/2021/6653501
    OpenUrlCrossRef
  280. 280.↵
    Zhao C, Ren Y, Zhang Y. NDRG4 Alleviates Myocardial Infarction-Induced Apoptosis through the JAK2/STAT3 Pathway. Comput Math Methods Med. 2022;2022:4869470. doi:10.1155/2022/4869470
    OpenUrlCrossRef
  281. 281.↵
    Oksala N, Pärssinen J, Seppälä I, Raitoharju E, Kholova I, Hernesniemi J, Lyytikäinen LP, Levula M, Mäkelä KM, Sioris T, et al. Association of neuroimmune guidance cue netrin-1 and its chemorepulsive receptor UNC5B with atherosclerotic plaque expression signatures and stability in human(s): Tampere Vascular Study (TVS). Circ Cardiovasc Genet. 2013;6(6):579–587. doi:10.1161/CIRCGENETICS.113.000141
    OpenUrlAbstract/FREE Full Text
  282. 282.↵
    Janssens B, Mohapatra B, Vatta M, Goossens S, Vanpoucke G, Kools P, Montoye T, van Hengel J, Bowles NE, van Roy F, et al. Assessment of the CTNNA3 gene encoding human alpha T-catenin regarding its involvement in dilated cardiomyopathy. Hum Genet. 2003;112(3):227–236. doi:10.1007/s00439-002-0857-5
    OpenUrlCrossRefPubMedWeb of Science
  283. 283.↵
    Wang D, Zhang Y, Shen C. Research update on the association between SFRP5, an anti-inflammatory adipokine, with obesity, type 2 diabetes mellitus and coronary heart disease. J Cell Mol Med. 2020;24(5):2730–2735. doi:10.1111/jcmm.15023
    OpenUrlCrossRef
  284. 284.↵
    Wang P, Qin W, Wang P, Huang Y, Liu Y, Zhang R, Li S, Yang Q, Wang X, Chen F, et al. Genomic Variants in NEURL, GJA1 and CUX2 Significantly Increase Genetic Susceptibility to Atrial Fibrillation. Sci Rep. 2018;8(1):3297. doi:10.1038/s41598-018-21611-7
    OpenUrlCrossRef
  285. 285.↵
    Adela R, Banerjee SK. GDF-15 as a Target and Biomarker for Diabetes and Cardiovascular Diseases: A Translational Prospective. J Diabetes Res. 2015;2015:490842. doi:10.1155/2015/490842
    OpenUrlCrossRef
  286. 286.↵
    DeBerge M, Glinton K, Subramanian M, Wilsbacher LD, Rothlin CV, Tabas I, Thorp EB. Macrophage AXL receptor tyrosine kinase inflames the heart after reperfused myocardial infarction. J Clin Invest. 2021;131(6):e139576. doi:10.1172/JCI139576
    OpenUrlCrossRef
  287. 287.↵
    Chen S, Wang X, Wang J, Zhao Y, Wang D, Tan C, Fa J, Zhang R, Wang F, Xu C, et al. Genomic variant in CAV1 increases susceptibility to coronary artery disease and myocardial infarction. Atherosclerosis. 2016;246:148–156. doi:10.1016/j.atherosclerosis.2016.01.008
    OpenUrlCrossRefPubMed
  288. 288.↵
    Christophersen IE, Olesen MS, Liang B, Andersen MN, Larsen AP, Nielsen JB, Haunsø S, Olesen SP, Tveit A, Svendsen JH, et al. Genetic variation in KCNA5: impact on the atrial-specific potassium current IKur in patients with lone atrial fibrillation. Eur Heart J. 2013;34(20):1517–1525. doi:10.1093/eurheartj/ehs442
    OpenUrlCrossRefPubMedWeb of Science
  289. 289.↵
    Chen HY, Xiao ZZ, Ling X, Xu RN, Zhu P, Zheng SY. ELAVL1 is transcriptionally activated by FOXC1 and promotes ferroptosis in myocardial ischemia/reperfusion injury by regulating autophagy. Mol Med. 2021;27(1):14. doi:10.1186/s10020-021-00271-w
    OpenUrlCrossRef
  290. 290.↵
    Hong KW, Shin DJ, Lee SH, Son NH, Go MJ, Lim JE, Shin C, Jang Y, Oh B. Common variants in RYR1 are associated with left ventricular hypertrophy assessed by electrocardiogram. Eur Heart J. 2012;33(10):1250–1256. doi:10.1093/eurheartj/ehr267
    OpenUrlCrossRefPubMed
  291. 291.↵
    Liu Y, Yin Z, Xu X, Liu C, Duan X, Song Q, Tuo Y, Wang C, Yang J, Yin S. Crosstalk between the activated Slit2-Robo1 pathway and TGF-β signalling promotes cardiac fibrosis. ESC Heart Fail. 2021;8(1):447–460. doi:10.1002/ehf2.13095
    OpenUrlCrossRef
  292. 292.↵
    McCalmon SA, Desjardins DM, Ahmad S, Davidoff KS, Snyder CM, Sato K, Ohashi K, Kielbasa OM, Mathew M, Ewen EP, et al. Modulation of angiotensin II-mediated cardiac remodeling by the MEF2A target gene Xirp2. Circ Res. 2010;106(5):952–960. doi:10.1161/CIRCRESAHA.109.209007
    OpenUrlAbstract/FREE Full Text
  293. 293.↵
    Yin C, Ye Z, Wu J, Huang C, Pan L, Ding H, Zhong L, Guo L, Zou Y, Wang X, et al. Elevated Wnt2 and Wnt4 activate NF-κB signaling to promote cardiac fibrosis by cooperation of Fzd4/2 and LRP6 following myocardial infarction. EBioMedicine. 2021;74:103745. doi:10.1016/j.ebiom.2021.103745
    OpenUrlCrossRef
  294. 294.↵
    Kuroda R, Eguchi S. The Mysterious Role of Vasohibin-2 in Ascending Aorta Pathology. Am J Hypertens. 2021;34(5):453–455. doi:10.1093/ajh/hpab025
    OpenUrlCrossRef
  295. 295.↵
    Hedayati-Moghadam M, Hosseinian S, Paseban M, Shabgah AG, Gholizadeh J, Jamialahmadi T, Sathyapalan T, Sahebkar A.The Role of Chemokines in Cardiovascular Diseases and the Therapeutic Effect of Curcumin on CXCL8 and CCL2 as Pathological Chemokines in Atherosclerosis. Adv Exp Med Biol. 2021;1328:155–170. doi:10.1007/978-3-030-73234-9_11
    OpenUrlCrossRef
  296. 296.↵
    Rodríguez C, Martínez-González J, Raposo B, Alcudia JF, Guadall A, Badimon L. Regulation of lysyl oxidase in vascular cells: lysyl oxidase as a new player in cardiovascular diseases. Cardiovasc Res. 2008;79(1):7–13. doi:10.1093/cvr/cvn102
    OpenUrlCrossRefPubMedWeb of Science
  297. 297.↵
    Zhou YG, Yin RX, Wu J, Zhang QH, Chen WX, Cao XL. The association between the DNAH11 rs10248618 SNP and serum lipid traits, the risk of coronary artery disease, and ischemic stroke. Int J Clin Exp Pathol. 2018;11(9):4585–4594.
    OpenUrl
  298. 298.↵
    Andenæs K, Lunde IG, Mohammadzadeh N, Dahl CP, Aronsen JM, Strand ME, Palmero S, Sjaastad I, Christensen G, Engebretsen KVT, et al. The extracellular matrix proteoglycan fibromodulin is upregulated in clinical and experimental heart failure and affects cardiac remodeling. PLoS One. 2018;13(7):e0201422. doi:10.1371/journal.pone.0201422
    OpenUrlCrossRef
  299. 299.↵
    Wang H, Liu M, Wang X, Shuai W, Fu