ArticleDiscover oncology2025
Immune gene features and prognosis in colorectal cancer: insights from ssGSEA typing.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Article
- Unraveling the PFOS-NSCLC axis: integrated network toxicology, machine learning, and causal inference identify EIF4EBP1 as a key molecular hub.Frontiers in public health · 2026Article
- Constructing a novel mitochondrial metabolism-related genes signature to evaluate tumor immune microenvironment and predict survival of colorectal cancer.Frontiers in medicine · 2025Article
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7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundColorectal cancer (CRC) is a molecularly heterogeneous disease, and its treatment and prognosis vary greatly among subgroups. Therefore, it is necessary to identify prognostic factors associated with the biological heterogeneity of CRC in order to improve patients' survival expectations.
methodsWe obtained and merged RNA-Seq data along with clinical details for colorectal cancer (CRC) from The Cancer Genome Atlas (TCGA) repository, and then performed immunocluster typing on all CRC specimens. We conducted differential expression gene (DEG) analysis, gene set enrichment analysis (GSEA), and tumor microenvironment (TME) analysis on CRC samples that were divided into high and low Immunity categories. Moreover, we pinpointed prognostic genes from immune-related gene (IRGs) sets, developed a prognostic risk model, and executed survival analysis, receiver operating characteristic (ROC) curve analysis, and independent prognostic analysis. Additionally, we assessed the risk for patients categorized into high- and low-risk groups based on the model. Lastly, we created a Nomogram to customize the prediction of survival outcomes in CRC patients.
resultsCRC samples were divided into high and low Immunity groups based on the median value of the immunity score. Between the two groups, a total of 1550 DEGs were identified and 395 differentially expressed immune-related genes (DE-IRGs) were identified by intersection with 2483 IRGs. The DE-IRGs of the high Immunity group were dominated by Cytokine receptor interactions, chemokine signaling pathways and immune cell-mediated cytotoxicity, and molecule function of immune effector process. TME analysis showed that most of the 27 immune cells and functions were highly enriched in high Immunity group, whose Immune Score, Stromal Score and ESTIMATE Score were significantly higher. Subsequently, a prognostic risk model of CRC was constructed based on 12 prognostic genes, and the accuracy and reliability of the model prediction were verified. Finally, Nomogram enabled accurate individual prediction of the survival prognosis of CRC patients.
conclusionsOur study develops an immune-related prognostic model and Nomogram that reliably predicts survival outcomes in CRC patients and enhances understanding of the tumor immunity and molecular mechanisms of CRC.
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