Study of prognostic splicing factors in cancer using machine learning approaches

Abstract

Abstract Splicing factors (SFs) are the major RNA-binding proteins (RBPs) and key molecules that regulate the splicing of mRNA molecules through binding to mRNAs. The expression of splicing factors is frequently deregulated in different cancer types, causing the generation of oncogenic proteins involved in cancer hallmarks. In this study, we investigated the genes that encode RNA-binding proteins and identified potential splicing factors that contribute to the aberrant splicing applying a random forest classification model. The result suggested 56 splicing factors were related to the prognosis of 13 cancers, two SF complexes in liver hepatocellular carcinoma, and one SF complex in esophageal carcinoma. Further systematic bioinformatics studies on these cancer prognostic splicing factors and their related alternative splicing events revealed the potential regulations in a cancer-specific manner. Our analysis found high ILF2-ILF3 expression correlates with poor prognosis in LIHC through alternative splicing. These findings emphasize the importance of SFs as potential indicators for prognosis or targets for therapeutic interventions. Their roles in cancer exhibit complexity and are contingent upon the specific context in which they operate. This recognition further underscores the need for a comprehensive understanding and exploration of the role of SFs in different types of cancer, paving the way for their potential utilization in prognostic assessments and the development of targeted therapies.