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Lario S, Ramírez-Lázaro MJ, Brunet-Vega A, Vila-Casadesús M, Aransay AM, Lozano JJ, Calvet X. Coding and non-coding co-expression network analysis identifies key modules and driver genes associated with precursor lesions of gastric cancer. Genomics 2022; 114:110370. [PMID: 35430283 DOI: 10.1016/j.ygeno.2022.110370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/23/2022] [Accepted: 04/11/2022] [Indexed: 01/14/2023]
Abstract
BACKGROUND Helicobacter pylori infection is the most important risk factor for gastric cancer (GC). Human gastric adenocarcinoma develops after long-term H. pylori infection via the Correa cascade. This carcinogenic pathway describes the progression from gastritis to atrophy, intestinal metaplasia (IM), dysplasia and GC. Patients with atrophy and intestinal metaplasia are considered to have precancerous lesions of GC (PLGC). H. pylori eradication and endoscopy surveillance are currently the only interventions for preventing GC. Better knowledge of the biology of human PLGC may help find stratification markers and contribute to better understanding of biological mechanisms. One way to achieve this is by using co-expression network analysis. Weighted gene co-expression network analysis (WGCNA) is often used to identify modules from co-expression networks and relate them to clinical traits. It also allows identification of driver genes that may be critical for PLGC. AIM The purpose of this study was to identify co-expression modules and differential gene expression in dyspeptic patients at different stages of the Correa pathway. METHODS We studied 96 gastric biopsies from 78 patients that were clinically classified as: non-active (n = 10) and chronic-active gastritis (n = 20), atrophy (n = 12), and IM (n = 36). Gene expression of coding RNAs was determined by microarrays and non-coding RNAs by RNA-seq. The WGCNA package was used for network construction, module detection, module preservation and hub and driver gene selection. RESULTS WGCNA identified 20 modules for coding RNAs and 4 for each miRNA and small RNA class. Modules were associated with antrum and corpus gastric locations, chronic gastritis and IM. Notably, coding RNA modules correlated with the Correa cascade. One was associated with the presence of H. pylori. In three modules, the module eigengene (ME) gradually increased in the stages toward IM, while in three others the inverse relationship was found. One miRNA module was negatively correlated to IM and was used for a mRNA-miRNA integration analysis. WGCNA also uncovered driver genes. Driver genes show both high connectivity within a module and are significantly associated with clinical traits. Some of those genes have been previously involved in H. pylori carcinogenesis, but others are new. Lastly, using similar external transcriptomic data, we confirmed that the discovered mRNA modules were highly preserved. CONCLUSION Our analysis captured co-expression modules that provide valuable information to understand the pathogenesis of the progression of PLGC.
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Affiliation(s)
- Sergio Lario
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Madrid, Spain; Digestive Diseases Unit, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain.
| | - María J Ramírez-Lázaro
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Madrid, Spain; Digestive Diseases Unit, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain
| | - Anna Brunet-Vega
- Oncology Unit, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain
| | - Maria Vila-Casadesús
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Madrid, Spain; Bioinformatics Platform, CIBEREHD, Barcelona, Spain
| | - Ana M Aransay
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Madrid, Spain; Genome Analysis Platform, CIC bioGUNE, Bizkaia Technology Park, Derio, Bizkaia, Spain
| | - Juan J Lozano
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Madrid, Spain; Bioinformatics Platform, CIBEREHD, Barcelona, Spain
| | - Xavier Calvet
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Madrid, Spain; Digestive Diseases Unit, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain; Departament de Medicina, UAB, Sabadell, Spain
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