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A BRAF mutation-associated gene risk model for predicting the prognosis of melanoma
BRAF mutation plays an important role in the pathogenesis and progression of melanoma and is correlated to the prognosis of melanoma patients. However, fewer studies have attempted to develop a BRAF mutation-associated gene risk model for predicting the prognosis of melanoma. The current research ex...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10189240/ https://www.ncbi.nlm.nih.gov/pubmed/37205993 http://dx.doi.org/10.1016/j.heliyon.2023.e15939 |
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author | Huang, Xiang Gou, Wanrong Song, Qinxian Huang, Yan Wen, Chunlei Bo, Xue Jiang, Xian Feng, Jianguo Gao, Hong |
author_facet | Huang, Xiang Gou, Wanrong Song, Qinxian Huang, Yan Wen, Chunlei Bo, Xue Jiang, Xian Feng, Jianguo Gao, Hong |
author_sort | Huang, Xiang |
collection | PubMed |
description | BRAF mutation plays an important role in the pathogenesis and progression of melanoma and is correlated to the prognosis of melanoma patients. However, fewer studies have attempted to develop a BRAF mutation-associated gene risk model for predicting the prognosis of melanoma. The current research explores BRAF mutation-related biological features in melanoma and establishes a prognostic signature. First, we identified three significantly enriched KEGG pathways (glycosphingolipid biosynthesis - ganglio series, ether lipid metabolism, and glycosaminoglycan biosynthesis - keratan sulfate) and corresponding genes in the BRAF mutant group by gene set enrichment analysis. We then developed a prognostic signature based on 7 BRAF-associated genes (PLA2G2D, FUT8, PLA2G4E, PLA2G5, PLA2G1B, B3GNT2, and ST3GAL5) and assessed its prediction accuracy using ROC curve analysis. Finally, the nomogram was established according to the prognostic signature and independent clinical characteristics to predict the survival of melanoma patients. Furthermore, we found higher proportions of naive B cells, plasma cells, CD8 T cells, CD4 memory-activated T cells, and regulatory T cells in the low-risk group. Whereas lower proportions of M0, M1, and M2 macrophages and resting NK cells were observed in the high-risk group. The analysis also showed a significantly higher expression of immune checkpoint molecules (PD-1, PD-L1, CTLA4, BTLA, CD28, CD80, CD86, HAVCR2, ICOS, LAG3, and TIGIT) in the low-risk group. Our results provide novel insights into the effect of BRAF mutation on melanoma growth and indicate a promising direction toward immunotherapy and precision medicine in melanoma patients. |
format | Online Article Text |
id | pubmed-10189240 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-101892402023-05-18 A BRAF mutation-associated gene risk model for predicting the prognosis of melanoma Huang, Xiang Gou, Wanrong Song, Qinxian Huang, Yan Wen, Chunlei Bo, Xue Jiang, Xian Feng, Jianguo Gao, Hong Heliyon Research Article BRAF mutation plays an important role in the pathogenesis and progression of melanoma and is correlated to the prognosis of melanoma patients. However, fewer studies have attempted to develop a BRAF mutation-associated gene risk model for predicting the prognosis of melanoma. The current research explores BRAF mutation-related biological features in melanoma and establishes a prognostic signature. First, we identified three significantly enriched KEGG pathways (glycosphingolipid biosynthesis - ganglio series, ether lipid metabolism, and glycosaminoglycan biosynthesis - keratan sulfate) and corresponding genes in the BRAF mutant group by gene set enrichment analysis. We then developed a prognostic signature based on 7 BRAF-associated genes (PLA2G2D, FUT8, PLA2G4E, PLA2G5, PLA2G1B, B3GNT2, and ST3GAL5) and assessed its prediction accuracy using ROC curve analysis. Finally, the nomogram was established according to the prognostic signature and independent clinical characteristics to predict the survival of melanoma patients. Furthermore, we found higher proportions of naive B cells, plasma cells, CD8 T cells, CD4 memory-activated T cells, and regulatory T cells in the low-risk group. Whereas lower proportions of M0, M1, and M2 macrophages and resting NK cells were observed in the high-risk group. The analysis also showed a significantly higher expression of immune checkpoint molecules (PD-1, PD-L1, CTLA4, BTLA, CD28, CD80, CD86, HAVCR2, ICOS, LAG3, and TIGIT) in the low-risk group. Our results provide novel insights into the effect of BRAF mutation on melanoma growth and indicate a promising direction toward immunotherapy and precision medicine in melanoma patients. Elsevier 2023-05-02 /pmc/articles/PMC10189240/ /pubmed/37205993 http://dx.doi.org/10.1016/j.heliyon.2023.e15939 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Article Huang, Xiang Gou, Wanrong Song, Qinxian Huang, Yan Wen, Chunlei Bo, Xue Jiang, Xian Feng, Jianguo Gao, Hong A BRAF mutation-associated gene risk model for predicting the prognosis of melanoma |
title | A BRAF mutation-associated gene risk model for predicting the prognosis of melanoma |
title_full | A BRAF mutation-associated gene risk model for predicting the prognosis of melanoma |
title_fullStr | A BRAF mutation-associated gene risk model for predicting the prognosis of melanoma |
title_full_unstemmed | A BRAF mutation-associated gene risk model for predicting the prognosis of melanoma |
title_short | A BRAF mutation-associated gene risk model for predicting the prognosis of melanoma |
title_sort | braf mutation-associated gene risk model for predicting the prognosis of melanoma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10189240/ https://www.ncbi.nlm.nih.gov/pubmed/37205993 http://dx.doi.org/10.1016/j.heliyon.2023.e15939 |
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