ArticleBioscience reports2020
Identification of key gene modules and genes in colorectal cancer by co-expression analysis weighted gene co-expression network analysis.
Article in Bioscience reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Identification of a metastasis-associated prognostic gene signature in osteosarcoma through integrated bioinformatics and functional analysis.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Machine Learning-Based Gene Expression Analysis to Identify Prognostic Biomarkers in Upper Tract Urothelial Carcinoma.Cancers · 2025Article
- Article
- CircZFR promotes colorectal cancer progression via stabilizing BCLAF1 and regulating the miR-3127-5p/RTKN2 axis.Science China. Life sciences · 2024Article
- Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis.BMC genomics · 2023Article
- Impact of storage conditions on peripheral leukocytes transcriptome.Molecular biology reports · 2021Article
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Authors and funding
8 authors.
Funding
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Abstract
Colorectal cancer (CRC) has been one of the most common malignancies worldwide, which tends to get worse for the growth and aging of the population and westernized lifestyle. However, there is no effective treatment due to the complexity of its etiology. Hence, the pathogenic mechanisms remain to be clearly defined. In the present study, we adopted an advanced analytical method-Weighted Gene Co-expression Network Analysis (WGCNA) to identify the key gene modules and hub genes associated with CRC. In total, five gene co-expression modules were highly associated with CRC, of which, one gene module correlated with CRC significantly positive (R = 0.88). Functional enrichment analysis of genes in primary gene module found metabolic pathways, which might be a potentially important pathway involved in CRC. Further, we identified and verified some hub genes positively correlated with CRC by using Cytoscape software and UALCAN databases, including PAICS, ATR, AASDHPPT, DDX18, NUP107 and TOMM6. The present study discovered key gene modules and hub genes associated with CRC, which provide references to understand the pathogenesis of CRC and may be novel candidate target genes of CRC.
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