A new prognostic signature of 11 E3-related genes for colon cancer related to the immune microenvironment and somatic mutation

Abstract

Background Colon Cancer (COAD) is a common tumor in the gastrointestinal tract with a poor prognosis. It has been reported that ubiquitin-dependent modification systems influence tumor genesis and progression in various malignancies. Methods We collected the RNA expression data of the E3RGs from the TCGA-COAD program, used the “limma” R package to get differentially expressed E3RGs between COAD and healthy patients. Then we constructed the prognostic signature and calculated the risk score with univariate COX regression analysis and the LASSO analysis. We used a nomogram model to examine the predictive ability of the predictive model to predict OS rates at 1, 3, and 5 years. Next, we explored the significance of the predictive model under the stratified analysis. At last, we used bioinformatics and statistical methods to find some potential mechanisms in COAD cancer. Results We screened 137 E3-related genes (E3RGs), including 89 upregulated and 48 down-regulated E3RGs. Eleven genes (CORO2B, KCTD9, RNF32, BACH2, RBCK1, DPH7, WDR78, UCHL1, TRIM58, WDR72, and ZBTB18) were identified for the construction of prognostic signatures using univariate and multivariate Cox regression analysis, and lasso regression analysis. Kaplan-Meier curve analysis with log-rank testing showed a worse prognosis for patients with high risk based on the constructed E3RGs-based classifiers both in the train and test sets (P = 1.037e-05, P = 5.704e-03), and the proportion under ROC curves (AUC) was significant both in training and test groups (5-year AUC, 0.728 versus 0.892). Based on a stratified analysis, this 11-ERGs signature was a novel and attractive prognostic model independent of several clinicopathological parameters (age, sex, stage, TNM) in COAD. The enrichment and TME analysis of the signature confirmed that this signature might provide insight into the molecular mechanisms in COAD cancer. Conclusions We developed a novel independent risk model consisting of 11 ERGs and verified the effectiveness of this model in predicting the prognosis of COAD patients, providing important insights into survival prediction in COAD and several promising targets for COAD therapy.