Identification of an epithelial-mesenchymal transition related long non-coding RNA (LncRNA) signature in Glioma

Bioengineered. 2021 Dec;12(1):4016-4031. doi: 10.1080/21655979.2021.1951927.

Abstract

Epithelial-mesenchymal transition (EMT)-related long non-coding RNAs (lncRNAs) may be exploited as potential therapeutic targets in gliomas. However, the prognostic value of EMT-related lncRNAs in gliomas is unclear. We obtained lncRNAs from The Cancer Genome Atlas and constructed EMT-related lncRNA co-expression networks to identify EMT-related lncRNAs. The Chinese Glioma Genome Atlas (CGGA) was used for validation. Gene set enrichment and principal component analyses were used for functional annotation. The EMT-lncRNA co-expression networks were constructed. A real-time quantitative polymerase chain reaction assay was performed to validate the bioinformatics results. A nine-EMT-related lncRNAs (HAR1A, LINC00641, LINC00900, MIR210HG, MIR22HG, PVT1, SLC25A21-AS1, SNAI3-AS1, and SNHG18) signature was identified in patients with glioma. Patients in the low-risk group had a longer overall survival (OS) than those in the high-risk group (P < 0.0001). Additionally, patients in the high-risk group showed no deletion of chromosomal arms 1p and/or 19q, isocitrate dehydrogenase wild type, and higher World Health Organization grade. Moreover, the signature was identified as an independent factor and was significantly associated with OS (P = 0.041, hazard ratio = 1.806). These findings were further validated using the CGGA dataset. The low- and high-risk groups showed different EMT statuses based on principal component analysis. To study the regulatory function of lncRNAs, a lncRNA-mediated ceRNA network was constructed, which showed that complex interactions of lncRNA-miRNA-mRNA may be a potential cause of EMT progression in gliomas. This study showed that the nine-EMT-related lncRNA signature has a prognostic value in gliomas.

Keywords: Epithelial-mesenchymal transition; glioma; long non-coding rna; prognostic signature.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brain Neoplasms / genetics*
  • Brain Neoplasms / immunology
  • Brain Neoplasms / pathology*
  • Carcinogenesis / genetics
  • Carcinogenesis / pathology
  • Epithelial-Mesenchymal Transition / genetics*
  • Gene Expression Profiling*
  • Gene Expression Regulation, Neoplastic
  • Gene Regulatory Networks
  • Glioma / genetics*
  • Glioma / immunology
  • Glioma / pathology*
  • Humans
  • MicroRNAs / genetics
  • MicroRNAs / metabolism
  • Molecular Sequence Annotation
  • Principal Component Analysis
  • Prognosis
  • Proportional Hazards Models
  • RNA, Long Noncoding / genetics
  • RNA, Long Noncoding / metabolism*
  • RNA, Messenger / genetics
  • Reproducibility of Results
  • Risk Factors
  • Transcription Factors / metabolism

Substances

  • MicroRNAs
  • RNA, Long Noncoding
  • RNA, Messenger
  • Transcription Factors

Grants and funding

This work was supported by the National Natural Science Foundation (Grants 81960456 and 82002660), Jiangxi Provincial Natural Science Foundation (20202ACB216004), Training Program for Young Talents of the Fujian Health System (Grant 2017-ZQN-90) and the Natural Science Foundation of Fujian Province (Grant 2018J01399);Training Program for Young Talents of the Fujian Health System [2017-ZQN-90];the Natural Science Foundation of Fujian Province [2018J01399].