A Novel DNA Damage Repair-Related Gene Signature for Predicting Glioma Prognosis

Abstract

BackgroundGlioma is one of the most prevalent tumors in the central nervous system of adults and shows a poor prognosis. This study aimed to develop a DNA damage repair (DDR)-related gene signature to evaluate the prognosis of glioma patients.MethodsDifferentially expressed genes (DEGs) were extracted based on 276 DDR genes. Then, a gene signature was developed for the survival prediction in glioma patients by means of univariate, multivariate Cox, and least absolute shrinkage and selector operation (Lasso) analyses. After analyzing the clinical parameters, a nomogram was constructed and assessed. A total of 693 gliomas from the Chinese Glioma Genome Atlas (CGGA) were used for external validation. In addition, we used glioma tumor tissues for qPCR experiment to verify.ResultsA 12-DDR-related gene signature was identified from the 75 DEGs to stratify the survival risk of glioma patients. The overall survival of high-risk group was significantly shorter than that of low-risk group (P < 0.001). Besides, according to the risk score assessment, patients in high- or low-risk group also had significant correlations with clinicopathological parameters, including age (P < 0.01), grade (P < 0.001), IDH status (P < 0.001) and 1p19q codeletion status (P < 0.001). The nomogram provided favorable C-index and calibration plots. The C-index of training set and verification set was 0.761 and 0.746, respectively, and the calibration curve also showed that both training set and verification set were close to the standard curve. The qPCR results showed that there were significant differences in the expression of some typical DDR-related genes in tumor tissues and paracancer tissues (P(WEE1)=0.0002, P(RECQL)=0.0117, P(RPA1)=0.021, P(RRM1)=0.0035, P(PARP4)=0.0006, P(ELOA)=0.0023).ConclusionOur study developed a novel 12 DDR-related gene signature as a practical prognostic predictor for glioma patients. A nomogram combining the signature and clinical parameters was established as an individual clinical prediction tool.