ArticleFunctional & integrative genomics2022
Weighted gene co-expression network analysis combined with machine learning validation to identify key hub biomarkers in colorectal cancer.
Article in Functional & integrative genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- A novel biological function-based method for mining core genes in rare disease with limited cases.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Targeting PFKFB3 to restore glucose metabolism in acute pancreatitis via nanovesicle delivery.Molecular medicine (Cambridge, Mass.) · 2025Article
- Targeting the CCL28-STAT3-PLAC8 axis to suppress metastasis and remodel tumor microenvironment in colorectal cancer.Frontiers in immunology · 2025Article
- Weighted gene co-expression network analysis reveals key stromal prognostic markers in pancreatic cancer.Scientific reports · 2024Article
- Implications of single-cell immune landscape of tumor microenvironment for the colorectal cancer diagnostics and therapy.Medical oncology (Northwood, London, England) · 2023Review
- Identification of diagnostic biomarkers via weighted correlation network analysis in colorectal cancer using a system biology approach.Scientific reports · 2023Article
- A prospective prognostic signature for pancreatic adenocarcinoma based on ubiquitination-related mRNA-lncRNA with experimental validation in vitro and vivo.Functional & integrative genomics · 2023Article
- Establishment of a prognostic model for melanoma based on necroptosis-related genes.Functional & integrative genomics · 2023Article
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) is one of the most common malignancies worldwide; however, the potentially possible molecular biological mechanism of CRC is still not completely comprehended. This study aimed to confirm candidate key hub genes involved in the growth and development of CRC and their connection with immune infiltration as well as the related pathways. Gene expression data were selected from the GEO dataset. Hub genes for CRC were identified on the basis of differential expression analysis, weighted gene co-expression network analysis (WGCNA), and LASSO regression. Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA) were applied to reveal possible functions of the differential genes. Single-sample GSEA (ssGSEA) was implemented to identify the relationship between immune cells infiltration and hub genes. Two hundred and sixty-two differentially expressed genes (DEGs) were identified. Three modules were acquired based on WGCNA, and the blue module presented the highest relevance with CRC. Ten hub genes (AQP8, B3GALT5, CDH3, CEMIP, CPM, FOXQ1, PLAC8, SCNN1B, SPINK5, and SST) were acquired with LASSO analysis as underlying biomarkers for CRC. Compared with normal tissues, CRC tissues presented significantly higher numbers of CD4 T cells, CD8 T cells, B cells, natural regulatory T (Treg) cells, and monocytes. The functional enrichment analyses demonstrated that hub genes were primarily enriched in metabolic process, inflammatory-related, and immune-related response. Ten hub genes were identified to be involved in the occurrence and development of CRC and may be deemed as novel biomarkers for clinical diagnosis and treatment.
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