Complete epigenomic and genomic analysis of transcriptome modulation within skin cutaneous melanoma

BACKGROUND Alternative splicing (AS), the mechanism underlying the occurrence of protein diversity, may result in cancer genesis and development when it becomes out of control, as suggested by a growing number of studies. However, systemically analyze of AS events at the genome-wide level for skin cutaneous melanoma (SKCM) is still in a preliminary phase. This study aimed to systemically analyze the bioinformatics of the AS events at a genome-wide level using The Cancer Genome Atlas (TCGA) SKCM data. MATERIAL AND METHODS The SpliceSeq tool was used to analyze the AS profiles for SKCM clinical specimens from the TCGA database. The association between AS events and overall survival was analyzed by Cox regression analysis. AS event intersections and a gene interaction network were established by UpSet plot. A multivariate survival model was used to establish a feature genes prognosis model. RESULTS A total of 103 SKCM patients with full clinical parameters available were included in this study. We established an AS network that investigated the relationship between AS events and clinical prognosis information. Furthermore, 4 underlying feature genes of SKCM (MCF2L, HARS, TFR2, and RALGPS1) were found in the AS network. We performed function analysis as well as correlation analysis of AS events with gene expression. Using the multivariate survival model, we further confirmed the 4 genes that impacted the classifying SKCM prognosis at the level of AS events as well as gene expression, especially in wild-type SKCM. CONCLUSIONS AS events could be ideal indicators for SKCM prognosis. The key feature gene MCF2L played an important role in wild-type SKCM.