Systematic identification of lncRNA-based prognostic biomarkers for glioblastoma

• Published in: Aging (Albany, NY.) Vol. 11; no. 21; pp. 9405 - 9423
• Main Authors: Li, Mingdong, Long, Shengrong, Hu, Jinqu, Wang, Zan, Geng, Chao, Ou, Shaowu
• Format: Journal Article
• Language: English
• Published: United States Impact Journals 06.11.2019
• Subjects: Research Paper; lncRNAs; prognostic biomarkers; functional modules; glioblastoma; copy number alterations
• ISSN: 1945-4589; 1945-4589
• DOI: 10.18632/aging.102393

Glioblastoma (GBM), a primary malignant tumor of the central nervous system, has a very poor prognosis. Analysis of global GBM samples has revealed a variety of long non-coding RNAs (lncRNAs) associated with prognosis; nevertheless, there remains a lack of accurate prognostic markers. Using RNA-Seq, methylation, copy number variation (CNV), mutation and clinical follow-up data for GBM patients from The Cancer Genome Atlas, we performed univariate analysis, multi-cluster analysis, differential analysis of different subtypes of lncRNA and coding genes, weighted gene co-expression network analyses, gene set enrichment analysis, Kyoto Encyclopedia of Genes and Genomes analysis, Gene Ontology analysis, and lncRNA CNV analyses. Our analyses yielded five lncRNAs closely related to survival and prognosis for GBM. To verify the predictive role of these five lncRNAs on the prognosis of GBM patients, the corresponding RNA-seq data from Chinese Glioma Genome Atlas were downloaded and analyzed, and comparable results were obtained. The role of one lncRNA LINC00152 has been observed previously; the others are novel findings. Expression of these lncRNAs could become effective predictors of survival and potential prognostic biomarkers for patients with GBM.