A novel prognostic gene set for colon adenocarcinoma relative to the tumor microenvironment, chemotherapy, and immune therapy

Background: Colon adenocarcinoma (COAD) is a common aggressive malignant tumor. Heterogeneity in tumorigenesis and therapy response leads to an unsatisfactory overall survival of colon adenocarcinoma patients. Our study aimed to identify tools for a better prediction of colon adenocarcinoma prognosis, bolstering the development of a better personalized treatment and management. Method: We used the least absolute shrinkage and selection operator (LASSO) Cox model to analyze the prognosis-related gene datasets from the Gene Expression Omnibus (GEO) database and verified them using The Cancer Genome Atlas (TCGA) database. The area under the curve (AUC) was calculated using the receiver operating characteristic (ROC) curve to evaluate the predictive ability of the risk score model. Gene Set Enrichment Analysis (GSEA) was used to identify the significantly enriched and depleted biological processes. The tumor immune dysfunction and exclusion (TIDE) algorithm was taken to explore the relationship between the risk score and immunotherapy. The observations collectively helped us construct a nomogram to predict prognosis. Finally, the correlation between drug sensitivity and prognostic gene sets was conducted based on the Cancer Therapeutics Response Portal (CTRP) analyses. Results: We constructed a scoring model to assess the significance of the prognosis risk-related gene signatures, which was relative to common tumor characteristics and tumor mutational burdens. Patients with a high-risk score had higher tumor stage and poor prognosis (p< 0.05). Moreover, the expressions of these genes were in correlation with changes in the tumor microenvironment (TME). The risk score is an independent prognostic factor for COAD (p< 0.05). The accuracy of the novel nomogram model with a risk score and TNM-stage prediction prognosis in the predicting prognosis was higher than that of the TNM stage. Further analysis showed that a high-risk score was associated with tumor immune rejection. Patients with a low-risk score have a better prognosis with chemotherapy than those with a high-risk score. Compared to patients in the high-risk group, patients in the low-risk group had a significant survival advantage after receiving chemotherapy. In addition, the prognostic gene sets aid the assessment of drug sensitivity. Conclusion: This study establishes a new prognostic model to better predict the clinical outcome and TME characteristics of colon adenocarcinoma. We believe, our model also serves as a useful clinical tool to strengthen the functioning of chemotherapy, immunotherapy, and targeted drugs.