A 14 transcription factors-associated nomogram predicts the recurrence-free survival of gastric cancer

Abstract

Background: We aimed to construct and validate a novel transcription factors (TFs) signature for the prediction of gastric cancer (GC) patient’s recurrence-free survival (RFS) from TCGA and Gene Expression Omnibus (GEO) database and improve the predictive ability of RFS in GC patients.Methods: We searched TCGA database and GEO database to obtain gene expression data and related clinical information for GC. In total, 722 TFs and 384 GC patients with intact clinical information were identified to develop a novel TF signature. All TFs were included in a univariate Cox regression model. We then used the least absolute shrinkage and selection operator (LASSO) Cox regression model which included only TFs with P < 0.05 in the univariate model to identify candidate TFs related to RFS. After further adjustment, multivariate Cox regression was performed based on the candidate TFs for the identification of TF signatures in the RFS evaluation of GC patients.Results: We successfully confirmed the high ability of the 14-TF panel for predicting GC patients’ RFS by receiver operating characteristic (ROC). AUCs at 1, 3, 5 years in internal validation dataset were 0.827, 0.817, 0.811, respectively. Some similar results were calculated in external validation dataset (0.808, 0.907, 0.813, respectively) and entire dataset (0.815, 0.849, 0.801, respectively). Besides, our model makes a good distinction between the high-risk group and the low-risk group. Furthermore, a nomogram was developed via risk score, sex, cancer status and tumor grade, and C-index, ROC, the calibration plots as well as decision curve analysis (DCA) demonstrated good ability and clinical application of the nomogram.Conclusions: We successfully established and validated a novel 14-TF-associated nomogram for predicting the RFS of GC.