ArticleThe Journal of international medical research2022
An epithelial-mesenchymal transition-related prognostic model for colorectal cancer based on weighted gene co-expression network analysis.
Article in The Journal of international medical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 3 citations in OpenAlex.
- Prognostic model construction and drug prediction in colorectal cancer using mitochondrial programmed cell death-related genes.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Tumor Exosomal L1 Cell Adhesion Molecule Promotes Brain Metastasis of Lung Cancer.Research (Washington, D.C.) · 2026Article
- DDX59-AS1: A Novel Prognostic Biomarker and Immunotherapy Predictor in Lung Adenocarcinoma.Current medicinal chemistry · 2026Article
- TSPOAP1-AS1: A Novel Biomarker for the Prognosis and Therapeutic Target in Cervical Cancer.Combinatorial chemistry & high throughput screening · 2026Article
- TLN1 interacts with NGFR and suppresses the development of castration-resistant prostate cancer by upregulating NGFR.Frontiers in immunology · 2026Article
- LNX1-AS2 as a Key Prognostic and Immunotherapy Response Biomarker for Lung Adenocarcinoma.Current medicinal chemistry · 2025Article
- TLN1: an oncogene associated with tumorigenesis and progression.Discover oncology · 2024Review
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo identify susceptibility modules and genes for colorectal cancer (CRC) using weighted gene co-expression network analysis (WGCNA).
methodsFour microarray datasets were downloaded from the Gene Expression Omnibus database. We divided the tumor samples into three subgroups based on consensus clustering of gene expression, and analyzed the correlations between the subgroups and clinical features. The genetic features of the subgroups were investigated by gene set enrichment analysis (GSEA). A gene expression network was constructed using WGCNA, and a protein-protein interaction (PPI) network was used to identify the key genes. Gene modules were annotated by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses.
resultsWe divided the cancer cases into three subgroups based on consensus clustering (subgroups I, II, III). The green module identified by WGCNA was correlated with clinical characteristics. Ten key genes were identified according to their degree of connectivity in the protein-protein interaction network:
conclusionWe identified 10 hub genes as candidate biomarkers for CRC. These key genes may provide a theoretical basis for targeted therapy against CRC.
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