Integrating high-throughput microRNA and mRNA expression data to identify risk mRNA signature for pancreatic cancer prognosis.

Abstract

Pancreatic cancer is a malignancy of the digestive system characterized by poor prognosis. A number of prognostic messenger RNA (mRNA) signatures have been identified by using the high-throughput expression profiles. MicroRNAs (miRNA) play a critical role in regulating multiple cellular functions. However, no such integrated analysis of miRNAs and mRNAs for studying the prognostic mechanisms of pancreatic cancer has been reported. In this study, we first identified prognostic mRNAs and miRNAs based on The Cancer Genome Atlas datasets, and then performed an enrichment analysis to explore the underlying biological mechanisms involved in pancreatic cancer prognosis at the mRNA level. Furthermore, we performed an integrated analysis of mRNAs and miRNAs to identify prognostic subpathways, which were closely associated with pancreatic cancer genes and tumor hallmarks and involved in hypoxia, oxidative phosphyorylation and xenobiotic metabolisms. Meanwhile, we performed a random walk algorithm based on global network, prognostic mRNAs and miRNAs, and identified top risk mRNAs as the prognostic signature. Finally, an independent testing set was used to confirm the predictive power of the top mRNA signature, and most of these genes involved were known oncogenes. In conclusion, we performed a series of integrated analyses by comprehensively exploring pancreatic cancer prognosis and systematically optimized the prognostic signature for clinical use.