Discovery of 5-Signature Predicting Survival of Patients with Lower-Grade Glioma

OBJECTIVE: In the study, we aimed to identify key microRNAs (miRNAs) and clinical factors associated with survival time of lower-grade glioma (LGG) and develop an expression-based miRNA signature to provide survival risk prediction for patients with LGG. METHODS: We obtained miRNA expression profiles and clinical information of patients with LGG from The Cancer Genome Atlas dataset. All 591 miRNAs were modeled using random Forest Survival, Regression, and Classification to construct a random forest model for survival analysis, and feature selection was performed. We used univariate and multivariate Cox regression analysis to screen differentially expressed miRNAs and clinical factors related to overall survival of patients with LGG. RESULTS: A total of 591 differentially expressed miRNAs were obtained between LGG and normal tissues. After univariate and multivariate Cox regression analysis, 2 predictive miRNAs (hsa-miR-10b-5p and hsa-miR-15b-5p) and 3 clinical factors (grade, age, and cancer status) were finally screened out to construct a 5-signature, based on which patients in the training dataset were divided into high-risk and low-risk groups. The competitive performance of the 5-signature was revealed by receiver operating characteristic curve analysis. The prognostic value of the 5-signature was successfully validated in the testing and validation dataset. CONCLUSIONS: Our study demonstrated the promising potential of the novel 5-signature as an independent biomarker for survival prediction of patients with LGG.