ArticleMolecular medicine reports2017
Colon cancer recurrence‑associated genes revealed by WGCNA co‑expression network analysis.
Article in Molecular medicine reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
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78 citing papers in PubMed, 131 citations in OpenAlex.
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- Integrated systems biology reveals an 8-gene signature predicting early-stage lung adenocarcinoma progression and patient survival.Scientific reports · 2025Article
- Hyperlipidemia-associated specific modules and hub genes revealed by integrative methods of WGCNA and MetaDE.Frontiers in genetics · 2025Article
- Targeted co-expression networks for the study of traits.Scientific reports · 2024Article
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- Exploring extracellular matrix and prostaglandin pathway alterations across varying resection margin distances of right-sided colonic adenocarcinoma.BMC cancer · 2023Article
- Weighted correlation network analysis identifies multiple susceptibility loci for low-grade glioma.Cancer medicine · 2023Article
- Heme Metabolism-Related Gene TENT5C is a Prognostic Marker and Investigating Its Immunological Role in Colon Cancer.Pharmacogenomics and personalized medicine · 2023Article
- Proteome Network Analysis Identifies Potential Biomarkers for Brain Aging.Journal of Alzheimer's disease : JAD · 2023Article
- The noncoding RNAs regulating pyroptosis in colon adenocarcinoma were derived from the construction of a ceRNA network and used to develop a prognostic model.BMC medical genomics · 2022Article
- Identifying pancreatic cancer-associated miRNAs using weighted gene co-expression network analysis.Oncology letters · 2022Article
- Identification of hub genes in bladder cancer based on weighted gene co-expression network analysis from TCGA database.Cancer reports (Hoboken, N.J.) · 2022Article
- Transcriptomic analysis provides insights into molecular mechanisms of thermal physiology.BMC genomics · 2022Article
18 more citing papers are in PubMed but not listed here.
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5 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The present study aimed to identify the recurrence‑associated genes in colon cancer, which may provide theoretical evidence for the development of novel methods to prevent tumor recurrence. Colon cancer and matched normal samples microarray data (E‑GEOD‑39582) were downloaded from ArrayExpress. Genes with significant variation were identified, followed by the screening of differentially expressed genes (DEGs). Subsequently, the co‑expression network of DEGs was constructed using the weighted correlation network analysis (WGCNA) method, which was verified using the validation dataset. The significant modules associated with recurrence in the network were subsequently screened and verified in another independent dataset E‑GEOD‑33113. Function and pathway enrichment analyses were also conducted to determine the roles of selected genes. Survival analysis was performed to identify the association between these genes and survival. A total of 434 DEGs were identified in the colon samples, and stress‑associated endoplasmic reticulum protein family member 2 (SERP2) and long non‑coding RNA‑0219 (LINC0219) were determined to be the vital DEGs between all the three sub‑type groups with different clinical features. The brown module was identified to be the most significant module in the co‑expression network associated with the recurrence of colon cancer, which was verified in the E‑GEOD‑33113 dataset. Top 10 genes in the brown module, including EGF containing fibulin like extracellular matrix protein 2 (EFEMP2), fibrillin 1 (FBN1) and secreted protein acidic and cysteine rich (SPARC) were also associated with survival time of colon cancer patients. Further analysis revealed that the function of cell adhesion, biological adhesion, extracellular matrix (ECM) organization, pathways of ECM‑receptor interaction and focal adhesion were the significantly changed terms in colon cancer. In conclusion, SERP2, EFEMP2, FBN1, SPARC, and LINC0219 were revealed to be the recurrence‑associated molecular and prognostic indicators in colon cancer by WGCNA co‑expression network analysis.
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