Matteo Giulietti1, Giulia Occhipinti2, Giovanni Principato2, Francesco Piva2. 1. Department of Specialistic Clinical and Odontostomatological Sciences, Polytechnic University of Marche, Via Brecce Bianche, Ancona, 60131, Italy. m.giulietti@univpm.it. 2. Department of Specialistic Clinical and Odontostomatological Sciences, Polytechnic University of Marche, Via Brecce Bianche, Ancona, 60131, Italy.
Abstract
PURPOSE: 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.
PURPOSE: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.
Entities:
Keywords:
Biomarkers; Gene expression; PDAC; Pancreatic cancer; Systems biology; WGCNA
Authors: Kristina Goetze; Christian G Fabian; Andrea Siebers; Livia Binz; Daniel Faber; Stefano Indraccolo; Giorgia Nardo; Ulrike G A Sattler; Wolfgang Mueller-Klieser Journal: Cell Oncol (Dordr) Date: 2015-08-19 Impact factor: 6.730
Authors: Diane M Simeone; Baoan Ji; Mousumi Banerjee; Thiruvengadam Arumugam; Dawei Li; Michelle A Anderson; Ann Marie Bamberger; Joel Greenson; Randal E Brand; Vijaya Ramachandran; Craig D Logsdon Journal: Pancreas Date: 2007-05 Impact factor: 3.327
Authors: Adam E Frampton; Leandro Castellano; Teresa Colombo; Elisa Giovannetti; Jonathan Krell; Jimmy Jacob; Loredana Pellegrino; Laura Roca-Alonso; Niccola Funel; Tamara M H Gall; Alexander De Giorgio; Filipa G Pinho; Valerio Fulci; David J Britton; Raida Ahmad; Nagy A Habib; R Charles Coombes; Victoria Harding; Thomas Knösel; Justin Stebbing; Long R Jiao Journal: Gastroenterology Date: 2013-10-09 Impact factor: 22.682
Authors: Jeremy A Miller; Chaochao Cai; Peter Langfelder; Daniel H Geschwind; Sunil M Kurian; Daniel R Salomon; Steve Horvath Journal: BMC Bioinformatics Date: 2011-08-04 Impact factor: 3.169