New Results

Identification of biomarkers, pathways and potential therapeutic targets for heart failure using bioinformatics analysis

Basavaraj Vastrad, Chanabasayya Vastrad
doi: https://doi.org/10.1101/2021.08.05.455244

Abstract

Heart failure (HF) is a complex cardiovascular diseases associated with high mortality. To discover key molecular changes in HF, we analyzed next-generation sequencing (NGS) data of HF. In this investigation, differentially expressed genes (DEGs) were analyzed using limma in R package from GSE161472 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network, miRNA-hub gene regulatory network and TF-hub gene regulatory network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), REACTOME pathway, STRING, HiPPIE, miRNet, NetworkAnalyst and Cytoscape. Finally, we performed receiver operating characteristic curve (ROC) analysis of hub genes. A total of 930 DEGs 9464 up regulated genes and 466 down regulated genes) were identified in HF. GO and REACTOME pathway enrichment results showed that DEGs mainly enriched in localization, small molecule metabolic process, SARS-CoV infections and the citric acid (TCA) cycle and respiratory electron transport. Subsequently, the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed, and 10 hub genes in these network were focused on by centrality analysis and module analysis. Furthermore, data showed that HSP90AA1, ARRB2, MYH9, HSP90AB1, FLNA, EGFR, PIK3R1, CUL4A, YEATS4 and KAT2B were good diagnostic values. In summary, this study suggests that HSP90AA1, ARRB2, MYH9, HSP90AB1, FLNA, EGFR, PIK3R1, CUL4A, YEATS4 and KAT2B may act as the key genes in HF.

Introduction

Heart failure (HF) is one of the chronic cardiovascular diseases, affecting 1% to 2% of the adult population worldwide [1]. HF is said to be inefficiency of the heart to supply the peripheral tissues with the appropriate amount of blood and oxygen to meet their metabolic requirement and is linked with a high risk for subsequent mortality and morbidity [2]. Multiple risk factors might cause HF, including diabetics [3], hypertension [4], obesity [5], genetics [6], environmental triggers [7], and immunity, inflammation, and oxidative stress [8]. Although there are extensive investigation available regarding the etiologies and mechanisms underlying HF, the precise molecular mechanisms remain unclear [9–10]. Therefore, essential molecular markers of HF that are identifiable with more powerful technologies are urgently required.

Understanding the status of various genes and signaling pathway in early diagnosis of HF could improve the effect of initial treatment. COL1A1 [11], CXCL14 [12], MECP2 [13], RBM20 [14], PGC-1 [15], Wnt signaling pathway [16], TGF□β1/Smad3 signaling pathway [17], AT1-CARP signaling pathway [18], Akt signaling pathway [19] and neuregulin-1/ErbB signaling [20] were responsible for progression of HF. Therefore, we aimed to further explore the molecular pathogenesis of HF and identify specific molecular targets. However, these data still demand further clinical interpretation.

Next-generation sequencing (NGS) technology plays a crucial role in the analysis of gene expression, which served as important tools in cardiovascular research with great clinical application [21]. Recently, a large number of gene expression profiling studies have been reported with the use of NGS technology. The integrated bioinformatics analysis will be more positive and provide valuable novel molecular targets to foster the advancement of specific diagnosis and new therapeutic strategies.

In this investigation, NGS dataset (GSE161472) was downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo/) [22], and crucial genes identified by combining bioinformatics analyses in HF. Gene ontology (GO) terms and REACTOME pathways associated with HF were investigated, and the hub genes associated with HF were identified by protein–protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network construction and analysis. Subsequently, we validated the hub genes by receiver operating characteristic curve (ROC) analysis. Furthermore, we investigated the potential candidate molecular markers for their utility in diagnosis, prognosis, and drug targeting in HF.

Material and methods

Data resources

This study investigated DEGs in HF versus normal samples by analyzing GSE161472 GEO expression profiling by high throughput sequencing data downloaded from the GEO database. GEO serves as a public repository for experimental high-throughput raw NGS data. Expression profiling by high throughput sequencing profile was generated with the GPL11154 Illumina HiSeq 2000 (Homo sapiens). The GSE161472 dataset included 84 samples, containing 47 HF and 37 normal control samples.

Identification of DEGs

The analysis of screening DEGs between HF and normal control samples was analyzed by limma in R package [23]. Moreover, the threshold for the DEGs was set as P-value <0 .05, and |log2foldchange (FC)| > 0.22 for up regulated genes and|log2foldchange (FC)| < -0.18 for down regulated genes. The heat map and volcano plot of the DEGs were plotted using gplots and ggplot2, respectively.

GO and REACTOME pathway enrichment analysis of DEGs

The GO terms (http://www.geneontology.org) database primarily adds three categories: biological process (BP), cellular component (CC), and molecular function (MF) [24]. The REACTOME pathway (https://reactome.org/) [25] database compiles genomic, chemical, and systematic functional information. The g:Profiler (http://biit.cs.ut.ee/gprofiler/) [26] online tool implements methods to analyze and anticipate functional profiles of gene and gene clusters. In this investigation, GO terms and REACTOME pathways were analyzed using the g:Profiler with the enrichment threshold of P < 0.05.

Construction of the PPI network and module analysis

The Human Integrated Protein-Protein Interaction rEference (HiPPIE) interactome (http://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/) [27] database provides a significant association of protein□protein interaction (PPI). Cytoscape 3.8.2 (http://www.cytoscape.org/) [28] is used for the visual exploration of interaction networks. In this investigation, DEGs PPI networks were analyzed by the HiPPIE database and subsequently visualized by using Cytoscape. In addition, the node degree [29], betweenness centrality [30], stress centrality [31] and closeness centrality [32] of each protein node in the PPI network was calculated using plug-in Network Analyzer of the Cytoscape software. PEWCC1 (http://apps.cytoscape.org/apps/PEWCC1) [33] plug-in of the Cytoscape software was then used to screen out modules of PPI networks, and the degree cutoff = 2, node score cutoff = 0.2, k core = 2, and max depth = 100.

MiRNA-hub gene regulatory network construction

The miRNet database (https://www.mirnet.ca/) [34], a web biological database for prediction of known and unknown miRNA and hub genes relationships, was used to construct the miRNA-hub gene regulatory network, which was visualized in Cytoscape 3.8.2 [28].

TF-hub gene regulatory network construction

TF-hub gene regulatory network analysis is useful to analyze the interactions between hub genes and TF which might provide insights into the mechanisms of generation or development of diseases. NetworkAnalyst database (https://www.networkanalyst.ca/) [35] and Cytoscape 3.8.2 [28] software were used to build the TF-hub gene regulatory network.

Validation of hub genes by receiver operating characteristic curve (ROC) analysis

Then ROC curve analysis was implemented to calculate the sensitivity (true positive rate) and specificity (true negative rate) of the hub gens for HF diagnosis and we investigated how large the area under the curve (AUC) was by using pROC package in R statistical software [36]. The diagnostic values of the hub genes were predicted based on the ROC curve analysis.

Results

Identification of DEGs

Dataset GSE161472 was downloaded from the GEO database and analyzed using R packages (limma). Volcano plot was constructed to visualize fold changes of the DEGs (Fig. 1). A total of 930 DEGs were identified in GSE161472, among which a total of 464 were up regulated genes and 466 were down regulated genes and are listed in Table 1. The DEGs in the NSG data are shown as volcano plots in Fig.1. The 464 up regulated genes and 466 down regulated genes are shown in heatmap and were shown 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 – A37 = normal control samples; B1 – B47 = HF samples)

View this table:
Table 1

The statistical metrics for key differentially expressed genes (DEGs)

GO and REACTOME pathway enrichment analysis of DEGs

The top 930 DEGs were chosen to perform GO term and REACTOME pathway analyses. We detected enrichment in several BP GO terms such as localization, organic substance transport, small molecule metabolic process and cellular metabolic process and are listed in Table 2. In terms of CC, cytoplasm, membrane, intracellular anatomical structure and organelle lumen and are listed in Table 2. What’s more, some MF GO terms, such as protein binding, enzyme binding, catalytic activity and nucleoside phosphate binding, and are listed in Table 2. As to REACTOME pathway enrichment analysis, SARS-CoV infections, asparagine N-linked glycosylation, the citric acid (TCA) cycle and respiratory electron transport, and respiratory electron transport were mostly associated with these genes and are listed in Table 3.

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

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Table 5

Topology table for up and down regulated genes.

View this table:
Table 6

miRNA - target gene and TF - target gene interaction

Construction of the PPI network and module analysis

Using Cytoscape, a HIPPIE interactome database was used to establish a PPI network of these DEGs, with 4194 nodes and 8352 edges (Fig. 3). Based on the HIPPIE database, the DEGs with the highest PPI scores identified by the 4 centrality methods are shown in Table 4. The hub genes were obtained using the 4 centrality methods, including HSP90AA1, ARRB2, MYH9, HSP90AB1, FLNA, EGFR, PIK3R1, CUL4A, YEATS4 and KAT2B. A significant module was constructed from the PPI network of the DEGs using PEWCC1, including module 1 had 35 nodes and 124 edges (Fig.4A) and module 2 had 14 nodes and 34 edges (Fig.4B). GO and REACTOME pathway enrichment analysis showed that genes in these modules were markedly enriched in disease, immune system, cytoplasm, neutrophil degranulation, protein binding, infectious disease, SARS-CoV infections, localization, organic substance transport, the citric acid (TCA) cycle and respiratory electron transport, respiratory electron transport and metabolism.

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 of isolated form PPI of DEGs. (A) The most significant module was obtained from PPI network with 35 nodes and 124 edges for up regulated genes (B) The most significant module was obtained from PPI network with 14 nodes and 34 edges for down regulated genes. Up regulated genes are marked in green; down regulated genes are marked in red

MiRNA-hub gene regulatory network construction

According to the information in miRNet database and Cytoscape databases, the miRNA-hub gene regulatory network relationships of miRNA and hub genes were obtained (Fig. 5). After comparing the targets with hub genes, we found that MYH9 was the potential target of 226 miRNAs (ex; hsa-mir-520e); TUBB was the potential target of 202 miRNAs (ex; hsa-mir-8084); XPO1 was the potential target of 198 miRNAs (ex; hsa-mir-125a-5p); HSP90AA1 was the potential target of 188 miRNAs (ex; hsa-mir-133a-3p); HSP90AB1 was the potential target of 162 miRNAs (ex; hsa-mir-4801); PIK3R1 was the potential target of 131 miRNAs (ex; hsa-mir-138-5p); NCOA2 was the potential target of 114 miRNAs (ex; hsa-mir-539-5p); EGFR was the potential target of 83 miRNAs (ex; hsa-mir-132-3p); IFIT3 was the potential target of 78 miRNAs (ex; hsa-mir-449a); PSMB9 was the potential target of 68 miRNAs (ex; hsa-mir-200c-5p).

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.

TF-hub gene regulatory network construction

According to the information in NetworkAnalyst database and Cytoscape databases, the TF-hub gene regulatory network relationships of TF and hub genes were obtained (Fig. 6). After comparing the targets with hub genes, we found that HSP90AA1 was the potential target of 35 TFs (ex; RUNX1T1); XPO1 was the potential target of 33 TFs (ex; STAT1); SMARCA4 was the potential target of 33 TFs (ex; EGR1); HSPA5 was the potential target of 22 TFs (ex; FOSB); ARRB2 was the potential target of 20 TFs (ex; ARNT); KAT2B was the potential target of 47 TFs (ex; TWIST1); ZBTB16 was the potential target of 47 TFs (ex; GATA2); ZBTB16 was the potential target of 39 TFs (ex; GATA2); NCOA2 was the potential target of 34 TFs (ex; AHR); PIK3R1 was the potential target of 34 TFs (ex; GTF2H1); EGFR was the potential target of 27 TFs (ex; STAT5B).

Fig. 6.
Fig. 6.

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

Validation of hub genes by receiver operating characteristic curve (ROC) analysis

A ROC curve was plotted to evaluate the diagnostic value of HSP90AA1, ARRB2, MYH9, HSP90AB1, FLNA, EGFR, PIK3R1, CUL4A, YEATS4 and KAT2B (Fig. 7). The AUCs for the 10 hub genes were 0.953, 0.941, 0.976, 0.948, 0.931, 0.969, 0,958, 0.906, 0.912 and 0.950, respectively. These hub genes show good diagnostics values.

Fig. 7.
Fig. 7.

ROC curve analyses of hub genes. A) HSP90AA1 B) ARRB2 C) MYH9 D) HSP90AB1 E) FLNA F) EGFR G) PIK3R1 H) CUL4A I) YEATS4 J) KAT2B

Discussion

Although many relevant investigation of HF have been operated, early diagnoses, adequacy of treatment and prognosis for HF remain poorly concluded. For diagnosis and treatment, it is vital to more interpret the molecular mechanisms resulting in occurrence and advancement. Bioinformatics analysis is progressively adopted to screen out biomarkers have a guiding role in the diagnosis and treatment of HF [37].

In this investigation, we performed a series of bioinformatics analysis to screen key genes and pathways. The expression profiling by high throughput sequencing data found that 464 up regulated genes and 466 down regulated genes were identified in HF samples compared to normal control samples. ZFP57 [38] and ANK1 [39] contributes to the progression of diabetics, but these genes might be novel target for HF. TNC (tenascin C) has been shown to be activated in cardiac hypertrophy [40]. CCL2 is mainly involved in the progression of myocardial infarction [41]. SPP1[42] and IGSF1 [43] plays an important role in obesity, but these genes might be novel target for HF. Kiczak et al [44] found that TIMP1 was highly expressed in the HF.

GO and REACTOME pathway enrichment analyses were used to investigate the interactions of these DEGs. SARS-CoV infections [45], asparagine N-linked glycosylation [46], neutrophil degranulation [47], immune system [48], respiratory electron transport [49], metabolism [50], complex I biogenesis [51], neddylation [52], localization [53], membrane [54], protein binding [55], small molecule metabolic process [56] and were responsible for progression of HF. Han et al [57], Yamada et al [58], Wang et al [59], García-Manzanares et al [60], Raitoharju et al [61], King et al [62], Hirokawa et al [63], Kahali et al [64], Sun et al [65] and Kuhn et al [66] found expression of DHCR24, STXBP2, CLEC5A, XPO1, ADAM8, IRF3, DOT1L, PPP1R3B, IFIT3 and PCSK6 in myocardial infarction and indicated it as a potential gene markers. Expression of CYP1B1 [67], SLC7A1 [68], MYH9 [69], LAT2 [70], FXYD5 [71], CAMK1 [72], TGFBR1 [73], HSP90AB1 [74], PLEKHA7 [75], AGTRAP (angiotensin II receptor associated protein) [76], SLC16A9 [77], ACADSB (acyl-CoA dehydrogenase short/branched chain) [78], IMPA1 [79], CD300LG [80], CIRBP (cold inducible RNA binding protein) [81], PIK3R1 [82], YEATS4 [83], USP2 [84], NEDD9 [85], CHCHD5 [86] and ERAP1 [87] promotes hypertension. HSPB1 [88], CRYAB (crystallin alpha B) [89], ANXA5 [90], CCR2 [91], RGS4 [92], TNFRSF1A [93], XBP1 [94], NKX2-5 [95], NEU1 [96], GSTP1 [97], COMT (catechol-O-methyltransferase) [98], LIMK1 [99], CAMKK1 [100], CD276 [101], SMARCA4 [102], ADORA2B [103], ACOT1 [104], RGN (regucalcin) [105], PPA2 [106], KAT2B [107], PDK1 [108], CS (citrate synthase) [109], FGF12 [110], AQP4 [111], LMOD2 [112], SELENBP1 [113], MB (myoglobin) [114], S100A1 [115], RYR2 [116], GPC5 [117], JARID2 [118], EGFR (epidermal growth factor receptor) [119], FUNDC1 [120], S1PR1 [121], EPAS1 [122] and OSBPL11 [123] genes are a potential biomarkers for the detection and prognosis of HF at an early age. A previous study reported that CALR (calreticulin) [124], BSCL2 [125], PKD1 [126], TMBIM1 [127], CHST15 [128], NAA10 [129], TCF3 [130], CNN1 [131], TAF1A [132], ACAD9 [133], KLHL24 [134], MYOM2 [135], TRIM63 [136], CTNNA3 [137], NLRC5 [138]. KLF9 [139], MYLK3 [140], RBM20 [141], GSTK1 [142], UQCRFS1 [143], NDUFS2 [144] and COX6B1 [145] are expressed in cardiomyopathy. Other research have revealed that RTN4 [146], NCF2 [147], ARHGAP9 [148], LIPG (lipase G, endothelial type) [149], BCL3 [150], HSPG2 [151], APOBR (apolipoprotein B receptor) [152], ITGA2 [153], PPIA (peptidylprolylisomerase A) [154], IRAK1 [155], VKORC1 [156], RNLS (renalase, FAD dependent amine oxidase) [157], HLA-F [158], FBXL17 [159], COL11A2 [160] and NDUFC2 [161] are expressed in coronary heart disease, suggesting that it might also function in coronary heart disease transformation and development. CCR1 [162], MANF (mesencephalic astrocyte derived neurotrophic factor) [163], HSP90AA1 [164], ARRB2 [165], SLC39A13 [166], P4HA2 [167], HECTD3 [168], CNPY2 [169], ECHDC2 [170], NDUFS4 [171] and IMMT (inner membrane mitochondrial protein) [172] are a key initiators of ischemic cardiac diseases. Mounting evidence indicates that expression of PEA15 [173], CD48 [174], EDEM2 [175], CD55 [176], NCOR2 [177], EXT2 [178], SPRED1 [179], PDIA6 [180], CD300E [181], TCF19 [182], ABHD15 [183], OXSM (3-oxoacyl-ACP synthase, mitochondrial) [184], MGST3 [185], COQ7 [186], ACSL5 [187], ANK1 [188], PYGM (glycogen phosphorylase, muscle associated) [189], FBXO40 [190], SLC2A4 [191], HLA-DOA [192], TAP2 [193], HLA-DPA1 [194], NSMCE2 [195], NDUFA4 [196], HMG20A [197], AMY2B [198] and ACYP2 [199] might be involved in the pathogenesis of diabetics, but these genes might be novel target for HF. Recent evidence indicates that the SIRPA (signal regulatory protein alpha) [200], PFN1 [201], EIF6 [202], AHR (aryl hydrocarbon receptor) [203], RUNX1 [204], IDO1 [205], PDHB (pyruvate dehydrogenase E1 subunit beta) [206], NDUFS1 [207], MTUS1 [208], ZNF418 [209] and MTMR14 [210] are the key biomarkers in cardiac hypertrophy. Previous studies had shown that the expression of CLIC1 [211], ARPC1B [212], FLNA (filamin A) [213], HILPDA (hypoxia inducible lipid droplet associated) [214], RTN3 [215], G0S2 [216], CALU (calumenin) [217], MYDGF (myeloid derived growth factor) [218], LTBR (lymphotoxin beta receptor) [219], GGCX (gamma-glutamyl carboxylase) [220], SAMD1 [221], ACAT1 [222], NNT (nicotinamide nucleotide transhydrogenase) [223] and ATG14 [224] were closely related to the occurrence of atherosclerosis. HSPA5 [225], NMB (neuromedin B) [226], ELP5 [227], NLGN2 [228], RAB23 [229], MBOAT7 [230], CEP19 [231], PFKP (phosphofructokinase, platelet) [232], TKT (transketolase) [233], P4HB [234], CRYM (crystallin mu) [235], AMT (aminomethyltransferase) [236], MACROD2 [237], NUDT3 [238], DLD (dihydrolipoamide dehydrogenase) [239], NCOA2 [240], ZBTB16 [241], TFAM (transcription factor A, mitochondrial) [242], RASAL2 [243], NDUFB6 [244], HELZ2 [245] and CIITA (class II major histocompatibility complex transactivator) [246] were reported to be expressed in obesity, but these genes might be novel target for HF. TGFB1 [247], CD63 [248], DACT1 [249] and PSMB10 [250] have been reported to be crucial for the progression of atrial fibrillation. FAM20C [251] and CD74 [252] are important in the development of cardiac arrhythmia.

To explore the pathogenesis of HF, we constructed PPI network and isolated modules from PPI network for systematic analysis. The genes in the PPI network and modules with higher score were the hub genes that affected the progression of disease. CLTA (clathrin light chain A), RAI14, MPRIP (myosin phosphatase Rho interacting protein), AP2A1, CUL4A and NDUFA13 were the novel biomarkers for the progression of HF.

We further constructed a miRNA-hub gene regulatory network and TF-hub gene regulatory network for better understanding of the interaction between miRNA and hub genes, and TF and hub genes. Hsa-mir-125a-5p [253], hsa-mir-539-5p [254] and RUNX1T1 [255] were linked with progression of obesity, but these genes might be novel target for HF. Hsa-mir-138-5p [256] and STAT1 [257] were involved in the progression of HF. Hsa-mir-200c-5p [258] and STAT5B [259] were associated with development of diabetics, but these genes might be novel target for HF. EGR1 [260] and GATA2 [261] were liable for advancement of coronary heart disease. FOSB [262] and AHR [263] were associated with development of ischemic cardiac diseases. TWIST1 was responsible for progression of atherosclerosis [264]. TUBB (tubulin beta class I), hsa-mir-520e, hsa-mir-8084, hsa-mir-133a-3p, hsa-mir-4801, hsa-mir-132-3p, hsa-mir-449a, ARNT, and GTF2H1 were the novel biomarkers for the progression of HF.

In summary, the present data provide a comprehensive bioinformatics analysis of DEGs that might be related to the progression of HF. We have identified 930 candidate DEGs with NGS data and integrated bioinformatics analyses. A variety of novel genes and signaling pathways might be associated in the pathogenesis of HF. We also conclude that HSP90AA1, ARRB2, MYH9, HSP90AB1, FLNA, EGFR, PIK3R1, CUL4A, YEATS4 and KAT2B might be associated with progression of HF. These findings could lead to an increase in our understanding of the etiology and underlying molecular events of HF.

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. [(GSE161472) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE161472)]

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 Marina Stolina, Amgen Inc, Cardiometabolic Disorders, One Amgen Center Drive, Thousand Oaks, California, USA, very much, the author who deposited their profiling by high throughput sequencing dataset GSE161472, into the public GEO database.

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Posted August 06, 2021.
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Identification of biomarkers, pathways and potential therapeutic targets for heart failure using bioinformatics analysis
Basavaraj Vastrad, Chanabasayya Vastrad
bioRxiv 2021.08.05.455244; doi: https://doi.org/10.1101/2021.08.05.455244
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