·To construct colorectal cancer (CRC) prognostic risk score model, analyze the significant differences of cancer hallmark signaling pathway or biological process among CRC patients with different scores, and predict the immunotherapy effect of the model on other cancer patients.

·Eight independent CRC microarray datasets and two CRC RNA-seq datasets were collected from a public database. Differentially expressed genes (DEGs) in each CRC dataset were screened. Based on DEGs with intersection from different datasets, univariate Cox regression model was used to screen the genes associated with adverse prognosis. LASSO regression and multivariate Cox regression models were used to construct CRC prognostic risk score model. According to the risk scores, the patients were divided into high risk group and low risk group. The area under the curve (AUC) of receiver operator characteristic curve and Kaplan-Meier (KM) survival analysis were used to evaluate the model performance. Multivariate Cox regression model was used to analyze whether risk score was an independent prognostic factor for CRC. Gene set enrichment analysis (GSEA) was used to analyze the differences of cancer hallmark gene sets-related pathways between the CRC patients in the high risk group and low risk group. KM survival analysis and chi-square test were used to predict the immunotherapy effect of other cancer patients, so as to evaluate the application value of CRC prognostic risk score model.

·Univariate Cox regression analysis showed that 16 genes associated with adverse prognosis were obtained from DEGs with intersection from different datasets. Based on this, a CRC prognostic risk score model containing 8 gene signatures was constructed. In the training set (AUC_{max}=0.788) and internal/external validation sets (AUC_{mean}>0.600), the model displayed moderate accuracy, and the patients in the low risk group of all the above sets had significantly higher survival rate than those in the high risk group. Multivariate Cox regression analysis showed that risk score was an independent prognostic factor for CRC. GSEA results showed that cancer hallmark gene sets-related pathways were significantly enriched in CRC patients of the high risk group. KM survival analysis and chi-square test showed that other cancer patients in the low risk group had higher survival rate and better immunotherapy effect.

·The CRC risk score prognosis model containing 8 gene signatures is successfully constructed, which can provide reference for improving the prognosis of CRC patients and predicting the immunotherapy effect on other cancer patients.