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 that the patients in high risk group had poor prognosis in all cohorts. The areas under the receiver operating characteristic curve of the signature in predicting the survival of glioma patients at 1, 3, and 5 years were 0.84, 0.92, 0.90 in the CGGA cohort; 0.8, 0.85 and 0.77 in the TCGA set and 0.72, 0.90 and 0.86 in our own cohort. Multivariate Cox analysis demonstrated that the five-lncRNA signature was an independent prognostic indicator in the three sets (CGGA set: HR = 2.002, p < 0.001; TCGA set: HR = 1.243, p = 0.007; Our cohort: HR = 4.457, p = 0.008, respectively). A nomogram including the lncRNAs signature and clinical covariates was constructed and demonstrated high predictive accuracy in predicting 1-, 3- and 5-year survival probability of glioma patients.

CONCLUSION:

We established a five-lncRNA signature as a potentially reliable tool for survival prediction of glioma patients.

• Authors: Chunyu Zhang, Haitao Liu, Pengfei Xu, Yinqiu Tan, Yang Xu, Long Wang, Baohui Liu, Qianxue Chen, Daofeng Tian
• Journal: BMC cancer (BMC Cancer) Vol. 21 Issue 1 Pg. 251 (Mar 09 2021) ISSN: 1471-2407 [Electronic] England
• PMID: 33750353 (Publication Type: Journal Article)

Chemical References

• RNA, Long Noncoding

Topics

• Brain Neoplasms (genetics, mortality)
• Computational Biology (methods)
• Gene Regulatory Networks
• Glioma (genetics, mortality)
• Humans
• Nomograms
• Prognosis
• Proportional Hazards Models
• RNA, Long Noncoding (analysis)