ArticleJournal of gastrointestinal oncology2025
Development of a prognostic risk model for colorectal cancer and association of the prognostic model with cancer stem cell and immune cell infiltration.
Article in Journal of gastrointestinal oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The development of a prognostic model for patients with colorectal cancer (CRC) can facilitate the assessment of patient survival and the effectiveness of clinical treatments. A reasonable prognostic model can provide a basis for individualized treatment, prognostic risk stratification, and subsequent therapy for CRC patients. The aim of our study was to construct a prognostic model for patients with CRC using sequencing data derived from The Cancer Genome Atlas (TCGA) database. Methods: Sequencing data of paracancerous tissues (n=51) and CRC samples (n=647) were downloaded from the TCGA database. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were employed to identify prognostic factors. A restricted cubic spline (RCS) model was used to assess the nonlinear relationship between risk score and poor overall survival (OS). The Genomics of Drug Sensitivity in Cancer (GDSC) database was accessed to evaluate the correlation between the prognostic model's risk score and drug sensitivity. The single-sample gene set enrichment analysis (ssGSEA), estimate, and CIBERSORT algorithms were applied to quantify the association between prognostic genes and immune cell infiltration in CRC. Results: Our findings revealed that six genes, including Niemann-Pick C1-like 1 ( Conclusions: We developed a prognostic risk model incorporating a six-gene panel for patients with CRC. Our analysis revealed a nonlinear relationship between this prognostic model and OS in patients with CRC. A high risk score was associated with poor prognosis, indicating that the adverse outcomes observed in patients with CRC may be influenced by cancer stem cell and immune cell infiltration. Our model provides a promising predictive method for the prognosis of CRC patients, but it still needs to be validated in a larger sample size.
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