Uncovering biomarker genes with enriched classification potential from Hallmark gene sets

Colin A Targonski,Benjamin T Shealy,Melissa C Smith,Courtney A Shearer,F Alex Feltus

doi:10.1038/s41598-019-46059-1

Abstract

Given the complex relationship between gene expression and phenotypic outcomes, computationally efficient approaches are needed to sift through large high-dimensional datasets in order to identify biologically relevant biomarkers. In this report, we describe a method of identifying the most salient biomarker genes in a dataset, which we call “candidate genes”, by evaluating the ability of gene combinations to classify samples from a dataset, which we call “classification potential”. Our algorithm, Gene Oracle, uses a neural network to test user defined gene sets for polygenic classification potential and then uses a combinatorial approach to further decompose selected gene sets into candidate and non-candidate biomarker genes. We tested this algorithm on curated gene sets from the Molecular Signatures Database (MSigDB) quantified in RNAseq gene expression matrices obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data repositories. First, we identified which MSigDB Hallmark subsets have significant classification potential for both the TCGA and GTEx datasets. Then, we identified the most discriminatory candidate biomarker genes in each Hallmark gene set and provide evidence that the improved biomarker potential of these genes may be due to reduced functional complexity.

Highlights

We parsed the global gene expression matrix (GEM) for Genotype-Tissue Expression (GTEx) or The Cancer Genome Atlas (TCGA) into sub-GEMs for 50 molecular signatures called the Hallmark gene sets
The Gene Oracle algorithm was evaluated for its effectiveness in uncovering the most salient molecular signatures from 50 Hallmark gene sets
The RNA expression values for both the GTEx and TCGA datasets were extracted from the master GEMs and used to classify the labeled subgroups using the multilayer perceptron (MLP)

Summary

Introduction

A powerful, broad ranging, and popular collection of curated gene sets controlling known biological processes is the The Molecular Signatures Database (MSigDB), developed as a source of co-functionally enriched genes for biological condition association via Gene Set Enrichment Analysis[7]. Since these gene sets are enriched for common biological function, it is plausible that the combined information in a given gene set, captured via the quantification www.nature.com/scientificreports/. This top-down approach has yielded new and valuable knowledge of genes associated with tumor biology

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