Cargando…
Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis
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...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7941710/ https://www.ncbi.nlm.nih.gov/pubmed/33750353 http://dx.doi.org/10.1186/s12885-021-07972-9 |
_version_ | 1783662182928482304 |
---|---|
author | Zhang, Chunyu Liu, Haitao Xu, Pengfei Tan, Yinqiu Xu, Yang Wang, Long Liu, Baohui Chen, Qianxue Tian, Daofeng |
author_facet | Zhang, Chunyu Liu, Haitao Xu, Pengfei Tan, Yinqiu Xu, Yang Wang, Long Liu, Baohui Chen, Qianxue Tian, Daofeng |
author_sort | Zhang, Chunyu |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-07972-9. |
format | Online Article Text |
id | pubmed-7941710 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-79417102021-03-09 Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis Zhang, Chunyu Liu, Haitao Xu, Pengfei Tan, Yinqiu Xu, Yang Wang, Long Liu, Baohui Chen, Qianxue Tian, Daofeng BMC Cancer Research Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-07972-9. BioMed Central 2021-03-09 /pmc/articles/PMC7941710/ /pubmed/33750353 http://dx.doi.org/10.1186/s12885-021-07972-9 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Zhang, Chunyu Liu, Haitao Xu, Pengfei Tan, Yinqiu Xu, Yang Wang, Long Liu, Baohui Chen, Qianxue Tian, Daofeng Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis |
title | Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis |
title_full | Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis |
title_fullStr | Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis |
title_full_unstemmed | Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis |
title_short | Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis |
title_sort | identification and validation of a five-lncrna prognostic signature related to glioma using bioinformatics analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7941710/ https://www.ncbi.nlm.nih.gov/pubmed/33750353 http://dx.doi.org/10.1186/s12885-021-07972-9 |
work_keys_str_mv | AT zhangchunyu identificationandvalidationofafivelncrnaprognosticsignaturerelatedtogliomausingbioinformaticsanalysis AT liuhaitao identificationandvalidationofafivelncrnaprognosticsignaturerelatedtogliomausingbioinformaticsanalysis AT xupengfei identificationandvalidationofafivelncrnaprognosticsignaturerelatedtogliomausingbioinformaticsanalysis AT tanyinqiu identificationandvalidationofafivelncrnaprognosticsignaturerelatedtogliomausingbioinformaticsanalysis AT xuyang identificationandvalidationofafivelncrnaprognosticsignaturerelatedtogliomausingbioinformaticsanalysis AT wanglong identificationandvalidationofafivelncrnaprognosticsignaturerelatedtogliomausingbioinformaticsanalysis AT liubaohui identificationandvalidationofafivelncrnaprognosticsignaturerelatedtogliomausingbioinformaticsanalysis AT chenqianxue identificationandvalidationofafivelncrnaprognosticsignaturerelatedtogliomausingbioinformaticsanalysis AT tiandaofeng identificationandvalidationofafivelncrnaprognosticsignaturerelatedtogliomausingbioinformaticsanalysis |