A novel 9-gene signature for the prediction of postoperative recurrence in stage II/III colorectal cancer

Individualized recurrence risk prediction in patients with stage II/III colorectal cancer (CRC) is crucial for making postoperative treatment decisions. However, there is still a lack of effective approaches for identifying patients with stage II and III CRC at a high risk of recurrence. In this study, we aimed to establish a credible gene model for improving the risk assessment of patients with stage II/III CRC.

A novel RFS prediction model for patients with stage II/III CRC was constructed using multicohort validation. The proposed signature may help clinicians better manage patients with stage II/III CRC.

Recurrence-free survival (RFS)-related genes were screened using Univariate Cox regression analysis in GSE17538, GSE39582, and GSE161158 cohorts. 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. Kaplan-Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were used to assess the predictive accuracy and superiority of our risk model. Single-sample gene set enrichment analysis (ssGSEA) was employed to investigate the relationship between the infiltrative abundances of immune cells and risk scores. Genes significantly associated with the risk scores were identified to explore the biological implications of the 9-gene signature.

Survival analysis identified 347 RFS-related genes. Using 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 the low- and high-risk groups classified by the 9-gene signature. The area under the curve (AUC) values of this signature were close to or no less than the previously reported prognostic signatures and clinical factors, suggesting that this model could provide improved RFS prediction. The ssGSEA algorithm estimated that eight immune cells, including regulatory T cells, were aberrantly infiltrated in the high-risk group. Furthermore, the signature was associated with multiple oncogenic pathways, including cell adhesion and angiogenesis.