Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification

TL;DR

This paper introduces MITHrIL, a pathway analysis method that incorporates microRNAs and regulatory elements to improve phenotype classification accuracy from high-dimensional genomic data.

Contribution

MITHrIL extends traditional pathway analysis by adding missing regulatory elements, enhancing phenotype classification accuracy and outperforming existing methods.

Findings

MITHrIL outperforms competitors in pathway deregulation analysis.

The method accurately classifies tumor samples from TCGA.

Incorporating microRNAs improves biological insight.

Abstract

Motivation: Prediction of phenotypes from high-dimensional data is a crucial task in precision biology and medicine. Many technologies employ genomic biomarkers to characterize phenotypes. However, such elements are not sufficient to explain the underlying biology. To improve this, pathway analysis techniques have been proposed. Nevertheless, such methods have shown lack of accuracy in phenotypes classification. Results: Here we propose a novel methodology called MITHrIL (Mirna enrIched paTHway Impact anaLysis) for the analysis of signaling pathways, which has built on top of the work of Tarca et al., 2009. MITHrIL extends pathways by adding missing regulatory elements, such as microRNAs, and their interactions with genes. The method takes as input the expression values of genes and/or microRNAs and returns a list of pathways sorted according to their deregulation degree, together with the corresponding statistical significance (p-values). Our analysis shows that MITHrIL outperforms its competitors even in the worst case. In addition, our method is able to correctly classify sets of tumor samples drawn from TCGA. Availability: MITHrIL is freely available at the following URL: http://alpha.dmi.unict.it/mithril/