Construction and Validation of a Risk Prediction Model for Colon Adenocarcinoma Prognosis Based on 11 Depression-Related Genes

Abstract

Background: The influences of depression on cancer have noticeably attracted scholars’ attention. This study is aimed at exploring the relationships between depression and colon adenocarcinoma (COAD).

Methods: Differentially expressed genes in COAD were overlapped with depression-related genes (DRGs) to obtain COAD-DRGs. A risk model was constructed to predict overall survival (OS) using univariate and multivariate Cox regression analyses. GSE39582 dataset was utilized to validate the model. A nomogram was developed utilizing the clinical data.

Results: A risk model containing 11 genes was constructed. The results of receiver operating characteristic curve analysis revealed that the model could well predict the OS. In the high-risk group, the infiltration levels of plasma cells, resting/activated memory CD4 T cells, and monocytes were reduced, and only the infiltration levels of CD8 T cells and regulatory T cells were elevated. Cox regression analysis indicated that the risk score emerged as an independent prognostic factor. Finally, a nomogram of comprehensive risk score, age, and pM stage was established, and the predictions of this model aligned well with the actual OS data.

Conclusion: A COAD risk prediction model was successfully constructed utilizing 11 DRGs. This model assists in implementing more effective treatment and care strategies, enhancing the clinical outcomes for COAD.