Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer - Zhou, Leqi; Yu, Yue; Wen, Rongbo; Zheng, Kuo; Jiang, Siyuan; Zhu, Xiaoming; Sui, Jinke; Gong, Haifeng; Lou, Zheng; Hao, Liqiang; Yu, Guanyu; Zhang, Wei (Frontiers in Oncology, 2022)
Background: Most prognostic signatures of colorectal cancer (CRC) are developed to predict overall survival (OS). Gene signatures predicting recurrence-free survival (RFS) are rarely reported and postoperative recurrence results in poor outcome. Thus, we aim to construct a robust, individualized gene signature that can predict both OS and RFS of CRC patients. Methods: Prognostic genes that were significantly associated with both OS and RFS in GSE39582 and TCGA cohorts were screened via univariate Cox regression analysis and Venn diagram. These genes were then submitted to least absolute shrinkage and selection operator (LASSO) regression analysis and following multivariate Cox regression analysis to obtain an optimal gene signature. Kaplan–Meier (K-M), calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of this signature. A nomogram integrating prognostic factors was constructed to predict 1-year, 3-year and 5-year survival probability. Function annotation and pathway enrichment analyses were used to elucidate the biological implication of this model. Results: A total of 186 genes significantly associated with both OS and RFS were identified. Based on these genes, LASSO and multivariate Cox regression analyses determined an 8-gene signature contained ATOH1, CACNB1, CEBPA, EPPHB2, HIST1H2BJ, INHBB, LYPD6 and ZBED3. Signature high-risk cases had worse OS in the GSE39582 training cohort (hazard ratio [HR] = 1.54, 95% confidence interval [CI] = 1.42 to 1.67) and the TCGA validation cohort (HR = 1.39, 95% CI = 1.24 to 1.56), and worse RFS in both cohorts (GSE39582: HR = 1.49, 95% CI = 1.35 to 1.64; TCGA: HR = 1.39, 95% CI = 1.25 to 1.56). The area under the curves (AUCs) of this model in the training and validation cohorts were all around 0.7, which were higher or no less than several previous models, suggesting that this signature could improve OS and RFS prediction of CRC patients. The risk score was related to multiple oncological pathways. CACNB1, HIST1H2BJ and INHBB were significantly upregulated in CRC tissues. Conclusion: A credible OS and RFS prediction signature with multi-cohort and cross-platform compatibility was constructed in CRC. This signature might facilitate personalized treatment and survival improvement of CRC patients.