Abstract:
Objective: To screen the genes related to pyroptosis in colon cancer (CC) tissues by bioinformatics approach and to explore their relationship with patient prognosis to provide new therapeutic targets for CC patients. Methods: Gene expression, transcriptional data and clinical data of CC patients were downloaded from TCGA database and GEO database, respectively. R software was used to extract the expression of pyroptosis-related genes in TCGA transcription data, and the differentially expressed genes (DEGs) were screened out to construct a protein interaction network of the DEGs. The genes were typed by univariate analysis and cluster analysis, and the survival differences between the two subtypes were compared to obtain prognosis-related genes. Then, through Lasso regression analysis, cross-validation and optimization, the gene coefficients (Coef) were obtained to construct a prognosis prediction model for CC. The median risk score of the TCGA samples was calculated according to the prediction model, and the samples were divided into high-and-low risk groups. The GEO samples were used as the validation group, and survival analysis (Kaplan-Meier analysis), ROC curve, risk curve, PCA, and t-SNE analysis were performed on TCGA and GEO samples, respectively. Combined with the risk scores in the model, univariate and multivariate analyses were conducted to find the independent prognostic factors for colon cancer patients. The GO and KEGG analyses were then performed for the high-and-low risk groups. Finally, by ssGSEA analysis, immune cells and immune-related functions were scored for each sample to obtain the difference in immune cells and immune cell-related functions between the high-and-low risk groups. Results: A total of 52 pyroptosis genes were identified in colon cancer and normal colon tissues, and 40 DEGs were selected. A prognostic risk prediction model for colon cancer based on 15 genes was constructed by Cox regression and Lasso regression analysis, and the colon cancer patients were divided into high-and-low risk groups, with significant differences in survival between the two groups (P<0.001). The risk score of TCGA samples was calculated according to the prediction model, and the obtained median risk score was verified using the GEO database, which showed a significant difference in survival between high-and-low risk groups (P=0.013). The risk score calculated by the prediction model was found to be an independent prognostic factor for predicting the survival of colon cancer patients. The GO enrichment analysis, KEGG enrichment analysis, and ssGSEA analysis of the DEGs showed a significant reduction in immune cell infiltration in the high-risk group of patients. Conclusion: A prognostic risk prediction model for colon cancer patients based on 15 genes by a bioinformatic approach was constructed. These genes also play an important role in colon cancer immunity.