Identification and Validation of a Novel DNA Damage and DNA Repair Related Genes Based Signature for Colon Cancer Prognosis - Wang, Xue-quan; Xu, Shi-wen; Wang, Wei; Piao, Song-zhe; Mao, Xin-li; Zhou, Xian-bin; Wang, Yi; Wu, Wei-dan; Ye, Li-ping; Li, Shao-wei (Frontiers in Genetics, 2021)
Backgrounds: Colorectal cancer (CRC) with high incidence, is the third mortality tumor. DNA damage and repair influence a variety of tumors. However, the role of these gene in colon cance prognosis has been less systematically investigated. Here, we aim to establish corresponding prognostic signature providing new therapeutic opportunities for CRC. Method:After related genes were collected from GSEA, univariate Cox regression was performed to evaluate each gene's prognostic relevance through TCGA-COAD dataset. Stepwise COX regression was used to establish a risk prediction model through the training sets randomly separated from TCGA cohort and validated in the remaining testing sets and two GEO data sets (GSE17538 and GSE38832). A 12-DNA-damage-and-repair-related gene-based signature able to classify COAD patients into high and low-risk groups was developed. The predictive ability of the risk model or nomogram were evaluated by different bioinformatics- methods. Gene functional enrichment analysis was performed to analyze the co-expressed genes of the risk-based genes. Result: A 12-gene based prognostic signature established within 160 significant survival-related genes from DNA damage and repair related gene sets performed well with an AUC of ROC 0.80 for 5 years in the TCGA -CODA data set. The signature includes CCNB3, ISY1, CDC25C, SMC1B, MC1R, LSP1P4, RIN2, TPM1, ELL3, POLG, CD36, NEK4. Kaplan-Meier survival curves showed that the prognosis of the risk status owns more significant differences than T, M, N, and stage prognostic parameters. A nomogram was constructed by LASSO regression analysis with T, M, N, age, and risk as prognostic parameters. ROC curve, C-index, Calibration analysis, and Decision Curve Analysis showed the risk module and nomogram performed best in 1-, 3- and 5-year. KEGG and GO and GSEA enrichment analysis suggest the risk involved in a variety of important biological processes and well-known cancer-related pathways. These differences may be the key factors affecting the final prognosis. Conclusion: The established gene signature for CRC prognosis provides a new molecular tool for prognosis, individualized diagnosis, and treatment. Therapies based on targeted DNA damage and repair mechanisms may formulate more sensitive and potential chemotherapy regimens, thereby expanding treatment options and potentially improving the clinical outcome of CRC patients.