Survival Marker miRNA-Mediated Subpathways of Breast Invasive Carcinoma Derived From Activity Profile

Abstract

We not only collected 1102 samples form 3 different breast invasive carcinoma (BRCA) datasets, which included a TCGA dataset and 2 GEO datasets, but also collected 23 other cancer datasets, one of which included more than 100 samples. Using these datasets, we topologically inferred miRNA-mediated subpathway activity profiles, which integrated gene expression profiles, prior gene interaction information and target relations between miRNAs and genes, and topological information. Then we constructed the Global Directed Pathway Network (GDPN) with genes as nodes, and from 3 BRCA datasets and other 23 cancer datasets identified a set of miRNA-mediated subpathways that are survival-related risk markers. The results showed large activity values correlated with poor prognosis, such as hsa-miR-107 and hsa-miR-142-3p of BRCA datasets. We assessed the stability and robustness of the miRNA-mediated subpathway survival markers with 2 GEO datasets and 23 external independent datasets. The results showed that the proposed method can significantly reduce noise from sequencing errors and samples heterogeneity by integrating pathway topological information, and can break down the boundary of pathways and provide a new measure for detecting survival-related markers. The top miRNA-mediated subpathways are more reliable in stratifying high risk group and low risk group and selecting therapeutic strategies.

Keywords

• miRNA-mediated subpathway
• survival marker
• prognosis
• topological information
• machine learning