Combinatorial Ranking of Gene Sets to Predict Disease Relapse: The Retinoic Acid Pathway in Early Prostate Cancer.

ABSTRACT:

Background

Quantitative high-throughput data deposited in consortia such as International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) present opportunities and challenges for computational analyses.

Methods

We present a computational strategy to systematically rank and investigate a large number (2¹⁰-2²⁰) of clinically testable gene sets, using combinatorial gene subset generation and disease-free survival (DFS) analyses. This approach integrates protein-protein interaction networks, gene expression, DNA methylation, and copy number data, in association with DFS profiles from patient clinical records.

Results

As a case study, we applied this pipeline to systematically analyze the role of ALDH1A2 in prostate cancer (P

SUBMITTER: Nim HT 

PROVIDER: S-EPMC5350134 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature