Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis.

ABSTRACT:

Background

To accurately predict the prognosis of glioma patients.

Methods

A total of 541 samples from the TCGA cohort, 181 observations from the CGGA database and 91 samples from our cohort were included in our study. Long non-coding RNAs (LncRNAs) associated with glioma WHO grade were evaluated by weighted gene co-expression network analysis (WGCNA). Five lncRNA features were selected out to construct prognostic signatures based on the Cox regression model.

Results

By weighted gene co-expression network analysis (WGCNA), 14 lncRNAs related to glioma grade were identified. Using univariate and multivariate Cox analysis, five lncRNAs (CYTOR, MIR155HG, LINC00641, AC120036.4 and PWAR6) were selected to develop the prognostic signature. The Kaplan-Meier curve depicted th

SUBMITTER: Zhang C 

PROVIDER: S-EPMC7941710 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature