A novel 9-gene signature for the prediction of postoperative recurrence in stage II/III colorectal cancer - Xin, Cheng; Lai, Yi; Ji, Liqiang; Wang, Ye; Li, Shihao; Hao, Liqiang; Zhang, Wei; Meng, Ronggui; Xu, Jun; Hong, Yonggang; Lou, Zheng (Frontiers in Genetics, 2023)
Background: Individualized recurrence risk prediction of patients with stage II/III colorectal cancer (CRC) is crucial for postoperative treatment decisions. However, there is still a lack of effective approaches for identifying stage II and III CRC patients at high risk of recurrence. In this study, we aimed to establish a credible gene model for ameliorating the risk assessment of patients with stage II/III CRC. Methods: Recurrence-free survival (RFS)-related genes were screened by using Univariate Cox regression analysis in GSE17538, GSE39582 and GSE161158 cohorts. The common prognostic genes were identified by Venn diagram and subsequently subjected to least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis for signature construction. The Kaplan-Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were adopted to assess the predictive accuracy and superiority of our risk model. Single-sample gene set enrichment analysis (ssGSEA) was employed for investigating the relationships between infiltrative abundances of immune cell and risk scores. Genes significantly associated with risk scores were identified to explore the biological implications of the 9-gene signature. Results: Survival analyses identified 347 credible RFS-related genes. Utilizing these genes, a 9-gene signature was constructed, which was composed of MRPL41, FGD3, RBM38, SPINK1, DKK1, GAL3ST4, INHBB, CTB-113P19.1 and FAM214B. K-M curves verified the survival differences between low- and high-risk groups classified by the 9-gene signature. The area under the curve (AUC) vales of this signature were near to or no less than previously reported prognostic signatures and clinical factors, suggesting that this model could provide improved RFS prediction. The ssGSEA algorithm estimated that eight immune cells were aberrantly infiltrated in the high-risk group, including regulatory T cell. Furthermore, the signature was associated with multiple oncogenic pathways including cell adhesion and angiogenesis. Conclusion: A novel RFS prediction model for patients with stage II/III CRC was constructed with multi-cohort validation. The proposed signature might help clinicians better manage patients with stage II/III CRC.