A Prognostic Model Based on Nine DNA Methylation-Driven Genes Predicts Overall Survival for Colorectal Cancer

Abstract

Background: Colorectal cancer (CRC) is the third most frequently diagnosed malignancy and the fourth leading cause of cancer-related death among common tumors in the world. We aimed to establish and validate a risk assessment model to predict overall survival (OS) for the CRC patients. Methods: DNA methylation-driven genes were identified by integrating DNA methylation profile and transcriptome data from The Cancer Genome Atlas (TCGA) CRC cohort. Then, a risk score model was built based on LASSO, univariable Cox and multivariable Cox regression analysis. After analyzing the clinicopathological factors, a nomogram was constructed and assessed. Another cohort from GEO was used for external validation. Afterward, the molecular and immune characteristics in the two risk score groups were analyzed. Results: In total, 705 methylation-driven genes were identified. Based on the LASSO and Cox regression analyses, nine genes, i.e., LINC01555, GSTM1, HSPA1A, VWDE, MAGEA12, ARHGAP, PTPRD, ABHD12B and TMEM88, were selected for the development of a risk score model. The Kaplan–Meier curve indicated that patients in the low-risk group had considerably better OS (P = 2e-08). The verification performed in subgroups demonstrated the validity of the model. Then, we established an OS-associated nomogram that included the risk score and significant clinicopathological factors. The concordance index of the nomogram was 0.81. A comprehensive molecular and immune characteristics analysis showed that the high-risk group was associated with tumor invasion, infiltration of immune cells executing pro-tumor suppression (such as myeloid-derived suppressor cells, regulatory T cells, immature dendritic cells) and higher expression of common inhibitory checkpoint molecules (ICPs). Conclusion: Our nine-gene associated risk assessment model is a promising signature to distinguish the prognosis for CRC patients. It is expected to serve as a predictive tool with high sensitivity and specificity for individualized prediction of OS in the patients with CRC.