ArticleBMC bioinformatics2023
Seven bacterial response-related genes are biomarkers for colon cancer.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 4 citations in OpenAlex.
- The Galectin Family in Colorectal Cancer: Integrating Molecular Mechanisms with Diagnostic, Prognostic, and Therapeutic Perspectives.Biomedicines · 2026Review
- Evaluation of Bacterial-related Genes' Role in Liver Hepatocellular Carcinoma through a Bioinformatics Strategy.Cell biochemistry and biophysics · 2025Article
- An audit of the PeptideAtlas database uncovers evidence for repurposed pseudogenes and co-opted retroviral ORFs.BMC genomics · 2025Article
- Comprehensive identification of hub mRNAs and lncRNAs in colorectal cancer using galaxy: an in silico transcriptome analysis.Discover oncology · 2025Article
- Exploring the prognostic significance of iron death‑related lncRNAs in colorectal cancer: A systematic review and meta‑analysis.Oncology letters · 2024Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundColon cancer (CC) is a common tumor that causes significant harm to human health. Bacteria play a vital role in cancer biology, particularly the biology of CC. Genes related to bacterial response were seldom used to construct prognosis models. We constructed a bacterial response-related risk model based on three Molecular Signatures Database gene sets to explore new markers for predicting CC prognosis.
methodsThe Cancer Genome Atlas (TCGA) colon adenocarcinoma samples were used as the training set, and Gene Expression Omnibus (GEO) databases were used as the test set. Differentially expressed bacterial response-related genes were identified for prognostic gene selection. Univariate Cox regression analysis, least absolute shrinkage and selection operator-penalized Cox regression analysis, and multivariate Cox regression analysis were performed to construct a prognostic risk model. The individual diagnostic effects of genes in the prognostic model were also evaluated. Moreover, differentially expressed long noncoding RNAs (lncRNAs) were identified. Finally, the expression of these genes was validated using quantitative polymerase chain reaction (qPCR) in cell lines and tissues.
resultsA prognostic signature was constructed based on seven bacterial response genes: LGALS4, RORC, DDIT3, NSUN5, RBCK1, RGL2, and SERPINE1. Patients were assigned a risk score based on the prognostic model, and patients in the TCGA cohort with a high risk score had a poorer prognosis than those with a low risk score; a similar finding was observed in the GEO cohort. These seven prognostic model genes were also independent diagnostic factors. Finally, qPCR validated the differential expression of the seven model genes and two coexpressed lncRNAs (C6orf223 and SLC12A9-AS1) in 27 pairs of CC and normal tissues. Differential expression of LGALS4 and NSUN5 was also verified in cell lines (FHC, COLO320DM, SW480).
conclusionsWe created a seven-gene bacterial response-related gene signature that can accurately predict the outcomes of patients with CC. This model can provide valuable insights for personalized treatment.
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