Objective:To screen the differentially expressed genes (DEG) related to inflammatory response associated with the prognosis of colon cancer based on the bioinformatics approach, and to construct and validate a prognostic model for colon cancer.Methods:RNA sequencing and clinical data of 472 colon cancer patients and normal colon tissues of 41 healthy people were retrieved from the Cancer Genome Atlas (TCGA) database. Gene expression related to prognosis of colon cancer and clinical data were retrieved from the International Cancer Genome Consortium (ICGC) database. The retrieval time was all from the establishment of library to November 2022. A total of 200 genes associated with inflammatory response obtained from the Gene Set Enrichment Analysis (GSEA) database were compared with the RNA sequencing gene dataset of colon cancer and normal colon tissues obtained from the TCGA database, and then DEG associated with inflammatory response were obtained. The prognosis-related DEG in the TCGA database were analyzed by using Cox proportional risk model, and the inflammatory response-related DEG were intersected with the prognosis-related DEG to obtain the prognosis-related inflammatory response-related DEG. The prognostic model of colon cancer was constructed by using LASSO Cox regression. Risk scores were calculated, and colon cancer patients in the TCGA database were divided into two groups of low risk (< the median value) and high risk (≥the median value) according to the median value of risk scores. Principal component analysis (PCA) was performed on patients in both groups, and survival analysis was performed by using Kaplan-Meier method. The efficacy of risk score in predicting the overall survival (OS) of colon cancer patients in the TCGA database was analyzed based on the R software timeROC program package. Clinical data from the ICGC database were applied to externally validate the constructed prognostic model, and patients with colon cancer in the ICGC database were classified into high and low risk groups based on the median risk score of patients with colon cancer in the TCGA database. By using R software, single-sample gene set enrichment analysis (ssGESA), immunophenotyping difference analysis, immune microenvironment correlation analysis, and immune checkpoint gene difference analysis of immune cells and immune function were performed for prognosis-related inflammation response-related DEG in the TCGA database.Results:A total of 60 inflammatory response-related DEG and 12 prognosis-related DEG were obtained; and 6 prognosis-related inflammatory response-related DEG (CCL24, GP1BA, SLC4A4, SRI, SPHK1, TIMP1) were obtained by taking the intersection set. LASSO Cox regression analysis showed that a prognostic model for colon cancer was constructed based on 6 prognosis-related inflammatory response-related DEG, and the risk score was calculated as = -0.113×CCL24+0.568×GP1BA+ (-0.375)×SLC4A4+(-0.051)×SRI+0.287×SPHK1+0.345×TIMP1. PCA results showed that patients with colon cancer could be better classified into 2 clusters. The OS in the high-risk group was worse than that in the low-risk group in the TCGA database ( P < 0.001); the area of the curve (AUC) of the prognostic risk score for predicting the OS rates of 1-year, 3-year, 5-year was 0.701, 0.685, and 0.675, respectively. The OS of the low-risk group was better than that of the high-risk group in the ICGC database; AUC of the prognostic risk score for predicting the OS rates of 1-year, 2-year, 3-year was 0.760, 0.788, and 0.743, respectively. ssGSEA analysis showed that the level of immune cell infiltration in the high-risk group in the TCGA database was high, especially the scores of activated dendritic cells, macrophages, neutrophils, plasmacytoid dendritic cells, T helper cells, and follicular helper T cells in the high-risk group were higher than those in the low-risk group, while the score of helper T cells 2 (Th2) in the high-risk group was lower compared with that in the low-risk group (all P < 0.05); in terms of immune function, the high-risk group had higher scores of antigen-presenting cell (APC) co-inhibition, APC co-stimulation, immune checkpoint, human leukocyte antigen (HLA), promotion of inflammation, parainflammation, T-cell stimulation, type Ⅰ interferon (IFN) response, and type ⅡIFN response scores compared with those in the low-risk group (all P < 0.05). The results of immunophenotyping analysis showed that IFN-γ-dominant type (C2) had the highest inflammatory response score, and the differences were statistically significant when compared with trauma healing type (C1) and inflammatory response type (C3), respectively (all P < 0.05). Immune microenvironment stromal cells and immune cells were all positively correlated with prognostic risk scores ( r values were 0.35 and 0.21, respectively, both P < 0.01). The results of immune checkpoint difference analysis showed there was a statistically significant difference in programmed-death receptor ligand 1 (PD-L1) expression level between high-risk group and low-risk group ( P = 0.002), and PD-L1 expression level was positively correlated with prognostic risk score ( r = 0.23, P < 0.01). Conclusions:Inflammatory response-related genes may play an important role in tumor immunity of colon cancer and can be used in the prognostic analysis and immunotherapy of colon cancer patients.