The construction of a prognostic nomogram model for colorectal cancer and the prediction of immune characteristics and immune treatment responses based on the bioinformatics analysis of soluble mediator-related genes

Accurate prognosis prediction in colorectal cancer (CRC) is essential for personalized treatment. Soluble mediators are promising predictive biomarkers for evaluating outcomes. We sourced transcriptome data of CRC (COAD+READ) from TCGA and GEO. Soluble mediator-related genes (SMRGs) were identified via GeneCards. Through univariate Cox and Lasso regression analyses, prognosis-related feature genes were determined. A prognostic model was created using multivariate Cox regression, categorizing patients into high-risk (HR) and low-risk (LR) groups based on the median riskscore. KEGG pathway enrichment analysis and GSEA were undertaken on groups. ssGSEA assessed immune cell scores, while ESTIMATE analysis evaluated stromal and immune cell scores along with tumor purity. The CellMiner database identified potential drugs for HR patients. Pearson correlation analysis revealed the relationship between mismatch repair (MMR) genes and model genes. We identified 10 SMRGs. Pearson correlation analysis indicated positive correlations among these genes. GO analysis showed that most feature genes were linked to binding functions. KEGG analysis revealed that the HR group was enriched in pathways like Basal cell carcinoma and Glycosaminoglycan biosynthesis. The ssGSEA indicated higher immune cell scores in the LR group, alongside lower stromal scores. LR group also exhibited a lower TIDE score and higher immunophenoscore. Drug sensitivity analysis suggested PF-4708671, PI-103, and XAV939 as potential treatments for HR patients. There was significant correlation between model gene and MMR genes. The CRC prognostic model based on SMRGs effectively predicts patient prognosis and guides treatment strategies.