Immune Checkpoint-Related Genes Predict Prognosis and Immune Status in Patients With Colon Cancer

Objective Immune checkpoint gene was a key mechanism regulating the immune response in patients with colon cancer and had potential clinical application value for the prognosis and treatment of patients with colon cancer. However, there was no prognostic model based on the immune checkpoint genes to predict colon cancer patients' prognosis and immune status. Methods The gene expression profile data and corresponding clinical information of colon cancer patients were collected from The Cancer Genome Atlas （TCGA） and Gene Expression Omnibus （GEO） public databases and the differentially expressed immune checkpoint genes were identified. The least absolute contraction and selection operator （LASSO） Cox regression analysis was used to construct an immune checkpoint gene-related prognostic model of TCGA patients with colon cancer. The GEO colon cancer patient cohort （GSE143985） was used for validation. Kaplan-Meier curve and receiver operating characteristic （ROC） curve were used to evaluate the prediction accuracy of the model. Spearman correlation analysis was used to observe the prognosis model and immune cell infiltration. The immune checkpoint risk genes and clinical indicators were analyzed by univariate and multifactorial regression, and a clinical histogram was constructed based on the multivariate regression results. Results A total of 29 differentially expressed immune checkpoint-related genes were screened in normal and colon cancer tissues. After LASSO regression analysis, a prognostic model based on 14 immune checkpoint-related genes was constructed. The TCGA colon cancer patients were divided into high-risk and low-risk groups based on the median risk score. Kaplan-Meier survival analysis showed that patients in the high-risk group had poorer survival than those in the low-risk group. ROC analysis showed that the 1-year forecast area under the curve （AUC） values of the TCGA training cohort and the 3-year forecast AUC values were all greater than 0.7, while the 1-year forecast AUC values of the GEO verification cohort were all greater than 0.8, indicating the high accuracy and stability of the model. Immune cell infiltration analysis showed a significant correlation between risk scores and CD4+ T cells and CD8+ T cells. In addition, the C-index of the graph constructed based on immune checkpoint risk genes and clinical indicators was 0.767, indicating a high clinical application value. Conclusions We construct a new immune checkpoint gene-related prognostic model for colon cancer, which can be used to predict the prognosis and immune status of colon cancer patients and provide a certain guidance basis for the clinical prognosis of colon cancer patients.