Comprehensive Analysis of MRNA-level and MiRNA-level Subpathway Activities for Identifying Robust Ovarian Cancer Prognostic Signatures

Ovarian cancer (OvCa) causes the highest mortality among all gynaecologic cancers. A large number of mRNA- or miRNA-based signatures were identified for OvCa patient prognosis. However, the comprehensive analysis of function-level prognostic signatures is currently not considered in OvCa. In the present study, we respectively inferred subpathway activities from mRNA and miRNA levels based on high-throughput expression profiles and reconstructed subpathways. Firstly, the activities of two tumour pathways were calculated and the difference between normal and tumour samples were analysed using multiple tumour types. Then, we calculated subpathway activities for OvCa based on the expression profiles from both mRNA and miRNA levels. Furthermore, based on these subpathway activity matrices, we performed bootstrap analysis to obtain sub-training sets and utilized univariate method to identify robust OvCa prognostic subpathways. A comprehensive comparison of subpathway results between these two levels was performed. As a result, we observed subpathway mutual exclusion trend between the levels of mRNA and miRNA, which indicated the necessary of combining mRNA-miRNA levels. Finally, by using ICGC data as testing sets, we utilized two strategies to verify survival predictive power of the mRNA-miRNA combined subpathway signatures and performed comparisons with results from individual levels. It was confirmed that our framework displayed application to identify robust and efficient prognostic signatures for OvCa, and the combined signatures indeed exhibited advantages over individual ones. In the study, we took a step forward in relevant novel integrated functional signatures for OvCa prognosis.