Systematic Identification of Survival-Associated Alternative Splicing Events in Kidney Renal Clear Cell Carcinoma

Comput Math Methods Med. 2021 Apr 19:2021:5576933. doi: 10.1155/2021/5576933. eCollection 2021.

Abstract

There is growing evidence that aberrant alternative splicing (AS) is highly correlated with driving tumorigenesis, but its function in kidney renal clear cell carcinoma (KIRC) remains to be discovered. In this study, we obtained the level-3 RNA sequencing and clinical data of KIRC from The Cancer Genome Atlas (TGCA). Combining with the splicing event detail information from TGCA SpliceSeq database, we established the independent prognosis signatures for KIRC with the univariate and multivariate Cox regression analyses. Then, we used the Kaplan-Meier analysis and receiver operating characteristic curves (ROCs) to assess the accuracy of prognosis signatures. We also constructed the regulatory network of splicing factors (SFs) and AS events. Our results showed that a total of 12029 survival-associated AS events of 5761 genes were found in 524 KIRC patients. All types of prognosis signatures displayed a satisfactory ability to reliably predict, especially in exon skip model which the area under curve of ROC was 0.802. Moreover, 18 splicing factors (SFs) highly correlated to AS events were identified. With the construction of the SF-AS interactive network, we found that SF powerfully promotes the occurrence of abnormal AS and may have a profound role in KIRC. Collectively, we screened survival-associated AS events and established prognosis signatures for KIRC, coupling with the SF-AS interactive network, which might provide a key perspective to clarify the potential mechanism of AS in KIRC.

MeSH terms

  • Alternative Splicing*
  • Carcinoma, Renal Cell / genetics*
  • Carcinoma, Renal Cell / metabolism
  • Carcinoma, Renal Cell / mortality
  • Computational Biology
  • Databases, Nucleic Acid
  • Gene Regulatory Networks
  • Humans
  • Kaplan-Meier Estimate
  • Kidney Neoplasms / genetics*
  • Kidney Neoplasms / metabolism
  • Kidney Neoplasms / mortality
  • Prognosis
  • Proportional Hazards Models
  • ROC Curve