Abstract
The current histologically based grading system for glioma does not accurately predict which patients will have better outcomes or benefit from adjuvant chemotherapy. We proposed that combining the expression profiles of multiple long non-coding RNAs (lncRNAs) into a single model could improve prediction accuracy. We included 1,094 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 on the basis of a training set. The predictive and prognostic accuracy of the classifier was validated using an internal test set and two external independent sets. Using this classifier, we classified patients in the 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 then assessed in the other sets. The classifier was an independent prognostic factor and had better prognostic value than clinicopathological risk factors. The patients in the 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 10-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 were enriched in the high-risk groups. Our classifier built on 10-lncRNAs is a reliable prognostic and predictive tool for OS in glioma patients and could predict which patients would benefit from adjuvant chemotherapy.
Highlights
About 81% of malignant brain tumors are due to gliomas [1]
The proposed classifier could accurately predict the survival of glioma patients better than each single long non-coding RNAs (lncRNAs) and clinicopathological risk factor
We built a nomogram for glioma for the first time based on the multi-lncRNA profile and clinical features, which achieved more accurate prediction than clinicopathological features or the classifier alone for 3 and 5 year OS
Summary
Maximal surgical resection followed by adjuvant chemotherapy or radiotherapy is the standard treatment for glioma patients [2]. Glioma can be divided into four groups (Grades I, II, III, and IV) [3]. The histopathological grading system is the key determinant for prognostic prediction and risk stratification for treatment decisions. The current histologically based grading system is not sufficient to predict which patients will have better outcomes or benefit from adjuvant chemotherapy. Molecular investigation could provide biomarkers for predicting OS and the benefits from adjuvant chemotherapy and for guiding treatment decisions for patients in different risk groups. It is necessary to augment the prognostic and predictive value of the histologically based grading system, which could be achieved with the use of new biomarkers
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