Email updates

Keep up to date with the latest news and content from BMC Systems Biology and BioMed Central.

This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM): Systems Biology

Open Access Research

Integrative pathway analysis of genome-wide association studies and gene expression data in prostate cancer

Peilin Jia1, Yang Liu1 and Zhongming Zhao1234*

Author Affiliations

1 Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA

2 Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN, USA

3 Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN, USA

4 Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA

For all author emails, please log on.

BMC Systems Biology 2012, 6(Suppl 3):S13  doi:10.1186/1752-0509-6-S3-S13

Published: 17 December 2012

Abstract

Background

Pathway analysis of large-scale omics data assists us with the examination of the cumulative effects of multiple functionally related genes, which are difficult to detect using the traditional single gene/marker analysis. So far, most of the genomic studies have been conducted in a single domain, e.g., by genome-wide association studies (GWAS) or microarray gene expression investigation. A combined analysis of disease susceptibility genes across multiple platforms at the pathway level is an urgent need because it can reveal more reliable and more biologically important information.

Results

We performed an integrative pathway analysis of a GWAS dataset and a microarray gene expression dataset in prostate cancer. We obtained a comprehensive pathway annotation set from knowledge-based public resources, including KEGG pathways and the prostate cancer candidate gene set, and gene sets specifically defined based on cross-platform information. By leveraging on this pathway collection, we first searched for significant pathways in the GWAS dataset using four methods, which represent two broad groups of pathway analysis approaches. The significant pathways identified by each method varied greatly, but the results were more consistent within each method group than between groups. Next, we conducted a gene set enrichment analysis of the microarray gene expression data and found 13 pathways with cross-platform evidence, including "Fc gamma R-mediated phagocytosis" (PGWAS = 0.003, Pexpr < 0.001, and Pcombined = 6.18 × 10-8), "regulation of actin cytoskeleton" (PGWAS = 0.003, Pexpr = 0.009, and Pcombined = 3.34 × 10-4), and "Jak-STAT signaling pathway" (PGWAS = 0.001, Pexpr = 0.084, and Pcombined = 8.79 × 10-4).

Conclusions

Our results provide evidence at both the genetic variation and expression levels that several key pathways might have been involved in the pathological development of prostate cancer. Our framework that employs gene expression data to facilitate pathway analysis of GWAS data is not only feasible but also much needed in studying complex disease.