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

Frontiers in Oncology

Jianmin Zhang²  Yiming Zhu³  Chi-Hao Zhang³  Anwen Shao⁴  Yuan-Bo Pan⁴  Qing-Wei Zhang⁶ 

[1] Brain Research Institute, Zhejiang University, Hangzhou, China;Collaborative Innovation Center for Brain Science, Zhejiang University, Hangzhou, China;Department of General Surgery, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China;Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China;Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China;Shanghai Institute of Digestive Disease, Shanghai Jiao Tong University, Shanghai, China;

关键词: glioma;    lncRNA;    prognosis;    survival;    chemotherapy;    nomogram;   

DOI  :  10.3389/fonc.2020.01057

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown