Open AccessIdentification of prognosis‐related alternative splicing events in kidney renal clear cell carcinoma
Author(s) -
Zuo Yongdi,
Zhang Liang,
Tang Weihao,
Tang Wanxin
Publication year - 2019
Publication title -
journal of cellular and molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.44
H-Index - 130
eISSN - 1582-4934
pISSN - 1582-1838
DOI - 10.1111/jcmm.14651
Subject(s) - alternative splicing , rna splicing , biology , computational biology , multivariate statistics , renal cell carcinoma , gene , kidney cancer , oncology , bioinformatics , medicine , genetics , computer science , messenger rna , machine learning , rna
Alternative splicing (AS) contributes to protein diversity by modifying most gene transcriptions. Cancer generation and progression are associated with specific splicing events. However, AS signature in kidney renal clear cell carcinoma (KIRC) remains unknown. In this study, genome‐wide AS profiles were generated in 537 patients with KIRC in the cancer genome atlas. With a total of 42 522 mRNA AS events in 10 600 genes acquired, 8164 AS events were significantly associated with the survival of patients with KIRC. Logistic regression analysis of the least absolute shrinkage and selection operator was conducted to identify an optimized multivariate prognostic predicting mode containing four predictors. In this model, the receptor‐operator characteristic curves of the training set were built, and the areas under the curves (AUCs) at different times were >0.88, thus indicating a stable and powerful ability in distinguishing patients' outcome. Similarly, the AUCs of the test set at different times were >0.73, verifying the results of the training set. Correlation and gene ontology analyses revealed some potential functions of prognostic AS events. This study provided an optimized survival‐predicting model and promising data resources for future in‐depth studies on AS mechanisms in KIRC.