title: Weighted gene co-expression network analysis reveals key genes involved in pancreatic ductal adenocarcinoma development.
abstract: OBJECTIVE
  Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Up till now, the patient's prognosis remains poor which, among others, is due to the paucity of reliable early diagnostic biomarkers. In the past, candidate diagnostic biomarkers and therapeutic targets have been delineated from genes that were found to be differentially expressed in normal versus tumour samples. Recently, new systems biology approaches have been developed to analyse gene expression data, which may yield new biomarkers. As of yet, the weighted gene co-expression network analysis (WGCNA) tool has not been applied to PDAC microarray-based gene expression data.
  
  
  METHODS
  PDAC microarray-based gene expression datasets, listed in the Gene Expression Omnibus (GEO) database, were analysed. After pre-processing of the data, we built two final datasets, Normal and PDAC, encompassing 104 and 129 patient samples, respectively. Next, we constructed a weighted gene co-expression network and identified modules of co-expressed genes distinguishing normal from disease conditions. Functional annotations of the genes in these modules were carried out to highlight PDAC-associated molecular pathways and common regulatory mechanisms. Finally, overall survival analyses were carried out to assess the suitability of the genes identified as prognostic biomarkers.
  
  
  RESULTS
  Using WGCNA, we identified several key genes that may play important roles in PDAC. These genes are mainly related to either endoplasmic reticulum, mitochondrion or membrane functions, exhibit transferase or hydrolase activities and are involved in biological processes such as lipid metabolism or transmembrane transport. As a validation of the applied method, we found that some of the identified key genes (CEACAM1, MCU, VDAC1, CYCS, C15ORF52, TMEM51, LARP1 and ERLIN2) have previously been reported by others as potential PDAC biomarkers. Using overall survival analyses, we found that several of the newly identified genes may serve as biomarkers to stratify PDAC patients into low- and high-risk groups.
  
  
  CONCLUSIONS
  Using this new systems biology approach, we identified several genes that appear to be critical to PDAC development. As such, they may represent potential diagnostic biomarkers as well as therapeutic targets with clinical utility.
authors.affiliation: Department of Specialistic Clinical and Odontostomatological Sciences, Polytechnic University of Marche, Via Brecce Bianche, Ancona, 60131, Italy. m.giulietti@univpm.it.
authors.forename: Matteo
authors.initials: M
authors.lastname: Giulietti
authors.affiliation: Department of Specialistic Clinical and Odontostomatological Sciences, Polytechnic University of Marche, Via Brecce Bianche, Ancona, 60131, Italy.
authors.forename: Giulia
authors.initials: G
authors.lastname: Occhipinti
authors.affiliation: Department of Specialistic Clinical and Odontostomatological Sciences, Polytechnic University of Marche, Via Brecce Bianche, Ancona, 60131, Italy.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Principato
authors.affiliation: Department of Specialistic Clinical and Odontostomatological Sciences, Polytechnic University of Marche, Via Brecce Bianche, Ancona, 60131, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: Piva
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D021441:Carcinoma, Pancreatic Ductal / Q000235:genetics* / Q000401:mortality; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D010190:Pancreatic Neoplasms / Q000235:genetics* / Q000401:mortality; D016019:Survival Analysis; D059467:Transcriptome
chemical_list: D014408:Biomarkers, Tumor
keywords: Biomarkers; Gene expression; PDAC; Pancreatic cancer; Systems biology; WGCNA