Integrated analysis of differential expression and alternative splicing of non-small cell lung cancer based on RNA sequencing.

Abstract

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with high morbidity and mortality rates. Numerous diagnosis and treatment methods have been proposed, and the prognosis of NSCLC has improved to a certain extent. However, the mechanisms of NSCLC remain largely unknown, and additional studies are required. In the present study, the RNA sequencing dataset of NSCLC was downloaded from the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/). The clean reads obtained from the raw data were mapped to the University of California Santa Cruz human genome (hg19), based on TopHat, and were assembled into transcripts via Cufflink. The differential expression (DE) and differential alternative splicing (DAS) genes were screened out through Cuffdiff and rMATS, respectively. The significantly enriched gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes pathways were obtained through the Database of Annotation, Visualization and Integrated Discovery (DAVID). Different numbers of DE and DAS genes were identified in different types of NSCLC samples, but a number of common functions and pathways were obtained, including biological processes associated with abnormal immune and cell activity. GO terms and pathways associated with substance metabolism, including the insulin signaling pathway and oxidative phosphorylation, were enriched in DAS genes rather than DE genes. Integrated analysis of differential expression and alternative splicing may be helpful in understanding the mechanisms of NSCLC, in addition to its early diagnosis and treatment.