title: Key regulators in prostate cancer identified by co-expression module analysis.
abstract: BACKGROUND
  Prostate cancer (PrCa) is the most commonly diagnosed cancer in men in the world. Despite the fact that a large number of its genes have been investigated, its etiology remains poorly understood. Furthermore, most PrCa candidate genes have not been rigorously replicated, and the methods by which they biologically function in PrCa remain largely unknown.
  
  
  RESULTS
  Aiming to identify key players in the complex prostate cancer system, we reconstructed PrCa co-expressed modules within functional gene sets defined by the Gene Ontology (GO) annotation (biological process, GO_BP). We primarily identified 118 GO_BP terms that were well-preserved between two independent gene expression datasets and a consequent 55 conserved co-expression modules within them. Five modules were then found to be significantly enriched with PrCa candidate genes collected from expression Quantitative Trait Loci (eQTL), somatic copy number alteration (SCNA), somatic mutation data, or prognostic analyses. Specifically, two transcription factors (TFs) (NFAT and SP1) and three microRNAs (hsa-miR-19a, hsa-miR-15a, and hsa-miR-200b) regulating these five candidate modules were found to be critical to the development of PrCa.
  
  
  CONCLUSIONS
  Collectively, our results indicated that genes with similar functions may play important roles in disease through co-expression, and modules with different functions could be regulated by similar genetic components, such as TFs and microRNAs, in a synergistic manner.
authors.forename: Junfeng
authors.initials: J
authors.lastname: Jiang
authors.forename: Peilin
authors.initials: P
authors.lastname: Jia
authors.affiliation: Center for Systems Biology, Soochow University, Jiangsu, China. zhongming.zhao@vanderbilt.edu.
authors.forename: Zhongming
authors.initials: Z
authors.lastname: Zhao
authors.forename: Bairong
authors.initials: B
authors.lastname: Shen
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D066264:Datasets as Topic; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D008297:Male; D035683:MicroRNAs / Q000235:genetics; D058977:Molecular Sequence Annotation; D011471:Prostatic Neoplasms / Q000235:genetics*; D014157:Transcription Factors / Q000235:genetics
chemical_list: D035683:MicroRNAs; D014157:Transcription Factors