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Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma
BACKGROUND: Lower grade glioma (LGG) are a heterogeneous tumor that may develop into high-grade malignant glioma seriously shortens patient survival time. The clinical prognostic biomarker of lower-grade glioma is still lacking. The aim of our study is to explore novel biomarkers for LGG that contri...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6944128/ https://www.ncbi.nlm.nih.gov/pubmed/31921517 http://dx.doi.org/10.7717/peerj.8312 |
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author | Xiao, Kai Liu, Qing Peng, Gang Su, Jun Qin, Chao-Ying Wang, Xiang-Yu |
author_facet | Xiao, Kai Liu, Qing Peng, Gang Su, Jun Qin, Chao-Ying Wang, Xiang-Yu |
author_sort | Xiao, Kai |
collection | PubMed |
description | BACKGROUND: Lower grade glioma (LGG) are a heterogeneous tumor that may develop into high-grade malignant glioma seriously shortens patient survival time. The clinical prognostic biomarker of lower-grade glioma is still lacking. The aim of our study is to explore novel biomarkers for LGG that contribute to distinguish potential malignancy in low-grade glioma, to guide clinical adoption of more rational and effective treatments. METHODS: The RNA-seq data for LGG was downloaded from UCSC Xena and the Chinese Glioma Genome Atlas (CGGA). By a robust likelihood-based survival model, least absolute shrinkage and selection operator regression and multivariate Cox regression analysis, we developed a three-gene signature and established a risk score to predict the prognosis of patient with LGG. The three-gene signature was an independent survival predictor compared to other clinical parameters. Based on the signature related risk score system, stratified survival analysis was performed in patients with different age group, gender and pathologic grade. The prognostic signature was validated in the CGGA dataset. Finally, weighted gene co-expression network analysis (WGCNA) was carried out to find the co-expression genes related to the member of the signature and enrichment analysis of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were conducted for those co-expression network. To prove the efficiency of the model, time-dependent receiver operating characteristic curves of our model and other models are constructed. RESULTS: In this study, a three-gene signature (WEE1, CRTAC1, SEMA4G) was constructed. Based on the model, the risk score of each patient was calculated with LGG (low-risk vs. high-risk, hazard ratio (HR) = 0.198 (95% CI [0.120–0.325])) and patients in the high-risk group had significantly poorer survival results than those in the low-risk group. Furthermore, the model was validated in the CGGA dataset. Lastly, by WGCNA, we constructed the co-expression network of the three genes and conducted the enrichment of GO and KEGG. Our study identified a three-gene model that showed satisfactory performance in predicting the 1-, 3- and 5-year survival of LGG patients compared to other models and may be a promising independent biomarker of LGG. |
format | Online Article Text |
id | pubmed-6944128 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-69441282020-01-09 Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma Xiao, Kai Liu, Qing Peng, Gang Su, Jun Qin, Chao-Ying Wang, Xiang-Yu PeerJ Bioinformatics BACKGROUND: Lower grade glioma (LGG) are a heterogeneous tumor that may develop into high-grade malignant glioma seriously shortens patient survival time. The clinical prognostic biomarker of lower-grade glioma is still lacking. The aim of our study is to explore novel biomarkers for LGG that contribute to distinguish potential malignancy in low-grade glioma, to guide clinical adoption of more rational and effective treatments. METHODS: The RNA-seq data for LGG was downloaded from UCSC Xena and the Chinese Glioma Genome Atlas (CGGA). By a robust likelihood-based survival model, least absolute shrinkage and selection operator regression and multivariate Cox regression analysis, we developed a three-gene signature and established a risk score to predict the prognosis of patient with LGG. The three-gene signature was an independent survival predictor compared to other clinical parameters. Based on the signature related risk score system, stratified survival analysis was performed in patients with different age group, gender and pathologic grade. The prognostic signature was validated in the CGGA dataset. Finally, weighted gene co-expression network analysis (WGCNA) was carried out to find the co-expression genes related to the member of the signature and enrichment analysis of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were conducted for those co-expression network. To prove the efficiency of the model, time-dependent receiver operating characteristic curves of our model and other models are constructed. RESULTS: In this study, a three-gene signature (WEE1, CRTAC1, SEMA4G) was constructed. Based on the model, the risk score of each patient was calculated with LGG (low-risk vs. high-risk, hazard ratio (HR) = 0.198 (95% CI [0.120–0.325])) and patients in the high-risk group had significantly poorer survival results than those in the low-risk group. Furthermore, the model was validated in the CGGA dataset. Lastly, by WGCNA, we constructed the co-expression network of the three genes and conducted the enrichment of GO and KEGG. Our study identified a three-gene model that showed satisfactory performance in predicting the 1-, 3- and 5-year survival of LGG patients compared to other models and may be a promising independent biomarker of LGG. PeerJ Inc. 2020-01-03 /pmc/articles/PMC6944128/ /pubmed/31921517 http://dx.doi.org/10.7717/peerj.8312 Text en © 2020 Xiao et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Xiao, Kai Liu, Qing Peng, Gang Su, Jun Qin, Chao-Ying Wang, Xiang-Yu Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma |
title | Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma |
title_full | Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma |
title_fullStr | Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma |
title_full_unstemmed | Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma |
title_short | Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma |
title_sort | identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6944128/ https://www.ncbi.nlm.nih.gov/pubmed/31921517 http://dx.doi.org/10.7717/peerj.8312 |
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