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Classification of clear cell renal cell carcinoma based on PKM alternative splicing
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
Istanbul Medeniyet Univ, Dept Bingn, Istanbul, Turkey..
KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0003-0354-0822
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
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2020 (English)In: Heliyon, ISSN 2405-8440, Vol. 6, no 2, article id e03440Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 6, no 2, article id e03440
Keywords [en]
Bioinformatics, Cancer research, Systems biology, PKM, Alternative splicing, Transcriptomics, Biomarker, Drug repositioning
National Category
Clinical Medicine
Identifiers
URN: urn:nbn:se:kth:diva-271527DOI: 10.1016/j.heliyon.2020.e03440ISI: 000518367800131PubMedID: 32095654Scopus ID: 2-s2.0-85079659277OAI: oai:DiVA.org:kth-271527DiVA, id: diva2:1426561
Note

QC 20200427

Available from: 2020-04-27 Created: 2020-04-27 Last updated: 2020-04-27Bibliographically approved

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Kim, WoongheeUhlén, Mathias

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