Prognostic and Predictive Value of a Long Non-coding RNA Signature in Glioma: A lncRNA Expression Analysis

Current histologic-based grading system does not accurately predict which patients with glioma have better outcomes or benefit from adjuvant chemotherapy. We believed that combining the expression profiles of multiple long non-coding RNAs (lncRNAs) into a single model could improve prediction accuracy. We included 1094 glioma patients from three different datasets. Using the least absolute shrinkage and selection operator (LASSO) Cox regression model, we built a multiple-lncRNA-based classifier in training set. The predictive and prognostic accuracy of the classifier was validated using the internal test set and two external independent sets. Through LASSO model, a ten lncRNAs classifier, was constructed. Using this classifier, we classified patients in training set into high- or low-risk groups with significantly different overall survival (OS, HR = 8.42, 95% CI = 4.99-14.2, p<0.0001). The prognostic power of the classifier was assessed in other sets. The classifier was an independent prognostic factor, and had better prognostic value than clinicopathological risk factors. The patients in high-risk group were found to have a favorable response to adjuvant chemotherapy (HR = 0.4, 95% CI = 0.25-0.64, p<0.0001). We built a nomogram that integrated the ten-lncRNA-based classifier and four clinicopathological risk factors to predict 3- and 5-year OS. Gene set variation analysis (GSVA) showed that pathways related to tumorigenesis, undifferentiated cancer, and epithelial mesenchymal transition enriched in the high-risk groups. Our classifier built on ten-lncRNA is a reliable prognostic and predictive tool for OS in glioma patients, and could predict which patients benefit from adjuvant chemotherapy.