A prognostic model for endoplasmic reticulum stress related genes in colon cancer based on WGCNA and consensus clustering analysis

Objective To construct an endoplasmic reticulum stress related prognostic risk score for colon cancer and predict potential therapeutic agents through bioinformatics analysis.Methods Transcriptomic, clinical, and survival data of colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. Weighted Gene Co-Expression Network Analysis (WGCNA) and consensus clustering analysis were used to identify distinct subgroups. Differentially expressed genes among these subgroups were screened. The intersection of these differentially expressed genes with Endoplasmic reticulum stress(ERS) related genes was identified to obtain differentially expressed ERS-related genes. Subsequently, functional enrichment analysis was conducted. Univariate and multivariate Cox regression analyses were performed to select differentially expressed ERS-related genes associated with prognosis, and a prognostic risk score model was constructed. The tumor immune microenvironment was analyzed using the CIBERSORT algorithm, and drug sensitivity was assessed using the oncoPredict package.Results In the TCGA-COAD dataset, five key genes significantly associated with prognosis (FABP4, LEP, HAMP, NOX4, and CD36) were identified, and a prognostic risk score model for colon cancer patients was constructed based on these genes. The prognosis of patients in the low-risk group was significantly better than the prognosis of patients in the high-risk group (P<0.05). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses suggested that the target genes might be related to inflammation and tumor immunity. The prognostic risk score combined with clinical features—including age, gender, and tumor stage—effectively predicted patient outcomes. Immune infiltration analysis indicated that the low-risk group had higher levels of plasma cells, memory CD4+ T cells, monocytes, and dendritic cells, whereas the high-risk group exhibited higher levels of macrophages and neutrophils. Drug sensitivity analysis suggested that AZD1332_1463 and IGF1R_3801_1738 might be preferred therapeutic agents for high-risk patients, while afatinib, gefitinib, erlotinib, and tamoxifen could also be potential therapeutic options for low-risk group.Conclusion The prognostic risk score model based on ERS-related genes can effectively predict the prognosis of colon cancer patients and provides insights into potentially sensitive therapeutic agents, offering a valuable reference for clinical treatment.