Methods: Two datasets were used as training and validation cohorts to establish the predictive model. We used three types of screening criteria: background analysis, pathway analysis, and functional analysis provided by the cBioportal website. Fisher's exact test and multivariable logistic regression were performed to screen out related genes. Furthermore, we performed receiver operating characteristic (ROC) and Kaplan-Meier curve analyses to evaluate the correlation between the selected genes and overall survival.
Result: We screened five genes (KNL1, NRXN1, C6, CCDC169-SOHLH2, and TTN) that were highly related to recurrence of GC. The area under the receiver operating characteristic (ROC) curve was 0.813, which was much higher than that of the baseline model (AUC = 0.699). This result suggested that the mutation of five selected genes had a significant effect on the prediction of recurrence compared with other factors (age, stages, history, etc.). Furthermore, the Kaplan-Meier estimator also revealed that the mutation of five genes positively correlated with patient survival.
Conclusions: The patients who have mutations in these five genes may experience longer survival than those who do not have mutations. This five-gene panel will likely be a practical tool for prognostic evaluation and will provide another possible way for clinicians to determine therapy.
Copyright © 2022 Shuxin Li et al.