Key regulators in prostate cancer identified by co-expression module analysis

Junfeng Jiang, Peilin Jia, Zhongming Zhao1

  • 1Center for Systems Biology, Soochow University, Jiangsu, China. zhongming.zhao@vanderbilt.edu.

BMC Genomics
|November 25, 2014
PubMed
Abstract

Insights

This study identifies key gene co-expression modules and regulatory elements, including transcription factors and microRNAs, crucial for prostate cancer (PrCa) development. These findings offer new insights into the complex etiology of PrCa.

Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • Prostate cancer (PrCa) is a leading global cancer in men.
  • The etiology of PrCa is poorly understood despite extensive gene investigation.
  • Replication of PrCa candidate genes and their biological functions remain challenging.

Purpose of the Study:

  • To identify key players in the prostate cancer system.
  • To reconstruct prostate cancer co-expressed gene modules within Gene Ontology (GO) functional sets.
  • To find conserved modules enriched with PrCa candidate genes.

Main Methods:

  • Reconstruction of PrCa co-expressed modules using Gene Ontology (GO) annotation (biological process, GO_BP).
  • Identification of conserved modules across two independent gene expression datasets.
  • Enrichment analysis of modules with PrCa candidate genes from eQTL, SCNA, mutation, and prognostic data.

Main Results:

  • 118 GO_BP terms and 55 conserved co-expression modules were identified.
  • Five modules were significantly enriched with PrCa candidate genes.
  • Two transcription factors (NFAT, SP1) and three microRNAs (hsa-miR-19a, hsa-miR-15a, hsa-miR-200b) were found critical for PrCa development.

Conclusions:

  • Genes with similar functions play roles in disease via co-expression.
  • Modules with different functions can be synergistically regulated by common genetic components like TFs and microRNAs.
  • This study provides a framework for understanding PrCa etiology through gene co-expression network analysis.

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