RT Journal Article
SR Electronic
T1 Classification of clear cell renal cell carcinoma based on <em>PKM</em> alternative splicing
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 823336
DO 10.1101/823336
A1 Xiangyu Li
A1 Beste Turanli
A1 Kajetan Juszczak
A1 Woonghee Kim
A1 Muhammad Arif
A1 Yusuke Sato
A1 Seishi Ogawa
A1 Hasan Turkez
A1 Jens Nielsen
A1 Jan Boren
A1 Mathias Uhlen
A1 Cheng Zhang
A1 Adil Mardinoglu
YR 2019
UL http://biorxiv.org/content/early/2019/11/12/823336.abstract
AB Clear cell renal cell carcinoma (ccRCC) accounts for 70–80% of kidney cancer diagnoses and displays high molecular and histologic heterogeneity. Hence, it is necessary to reveal the underlying molecular mechanisms involved in progression of ccRCC to better stratify the patients and design effective treatment strategies. Here, we analyzed the survival outcome of ccRCC patients as a consequence of the differential expression of four transcript isoforms of the pyruvate kinase muscle type (PKM). We first extracted a classification biomarker consisting of eight gene pairs whose within-sample relative expression orderings (REOs) could be used to robustly classify the patients into two groups with distinct molecular characteristics and survival outcomes. Next, we validated our findings in a validation cohort and an independent Japanese ccRCC cohort. We finally performed drug repositioning analysis based on transcriptomic expression profiles of drug-perturbed cancer cell lines and proposed that paracetamol, nizatidine, dimethadione and conessine can be repurposed to treat the patients in one of the subtype of ccRCC whereas chenodeoxycholic acid, fenoterol and hexylcaine can be repurposed to treat the patients in the other subtype.