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Prediction of survival and immunotherapy response by the combined classifier of G protein-coupled receptors and tumor microenvironment in melanoma
BACKGROUND: Melanoma is the deadliest form of skin tumor, and G protein-coupled receptors (GPCRs) play crucial roles in its carcinogenesis. Furthermore, the tumor microenvironment (TME) affects the overall survival (OS) and the response to immunotherapy. The combination of GPCRs and TME from a multi...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10504724/ https://www.ncbi.nlm.nih.gov/pubmed/37716991 http://dx.doi.org/10.1186/s40001-023-01346-6 |
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author | Shen, Kangjie Wang, Qiangcheng Wang, Lu Yang, Yang Ren, Min Li, Yanlin Gao, Zixu Zheng, Shaoluan Ding, Yiteng Ji, Jiani Wei, Chenlu Zhang, Tianyi Zhu, Yu Feng, Jia Qin, Feng Yang, Yanwen Wei, Chuanyuan Gu, Jianying |
author_facet | Shen, Kangjie Wang, Qiangcheng Wang, Lu Yang, Yang Ren, Min Li, Yanlin Gao, Zixu Zheng, Shaoluan Ding, Yiteng Ji, Jiani Wei, Chenlu Zhang, Tianyi Zhu, Yu Feng, Jia Qin, Feng Yang, Yanwen Wei, Chuanyuan Gu, Jianying |
author_sort | Shen, Kangjie |
collection | PubMed |
description | BACKGROUND: Melanoma is the deadliest form of skin tumor, and G protein-coupled receptors (GPCRs) play crucial roles in its carcinogenesis. Furthermore, the tumor microenvironment (TME) affects the overall survival (OS) and the response to immunotherapy. The combination of GPCRs and TME from a multi-omics perspective may help to predict the survival of the melanoma patients and their response to immunotherapy. METHODS: Bulk-seq, single-cell RNA sequencing (scRNA-seq), gene mutations, immunotherapy responses, and clinicopathologic feature data were downloaded from public databases, and prognostic GPCRs and immune cells were screened using multiple machine learning algorithms. The expression levels of GPCRs were detected using real-time quantitative polymerase chain reaction (qPCR) in A375 and HaCaT cell lines. The GPCR–TME classifier was constructed and verified using different cohorts and multi-omics. Gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and tracking tumor immunophenotype (TIP) were used to identify the key biological pathways among the GPCR–TME subgroups. Then, tumor mutational burden (TMB), vital mutant genes, antigen presentation genes, and immune checkpoints were compared among the subgroups. Finally, the differences in immunotherapy response rates among the GPCR–TME subgroups were investigated. RESULTS: A total of 12 GPCRs and five immune cell types were screened to establish the GPCR–TME classifier. No significant differences in the expression levels of the 12 GPCRs were found in the two cell lines. Patients with high GPCR score or low TME score had a poor OS; thus, the GPCR(low)/TME(high) subgroup had the most favorable OS. The scRNA-seq result revealed that immune cells had a higher GPCR score than tumor and stromal cells. The GPCR–TME classifier acted as an independent prognostic factor for melanoma. GSEA, WGCNA, and TIP demonstrated that the GPCR(low)/TME(high) subgroup was related to the activation and recruitment of anti-tumor immune cells and the positive regulation of the immune response. From a genomic perspective, the GPCR(low)/TME(high) subgroup had higher TMB, and different mutant genes. Ultimately, higher expression levels of antigen presentation genes and immune checkpoints were observed in the GPCR(low)/TME(high) subgroup, and the melanoma immunotherapy cohorts confirmed that the response rate was highest in the GPCR(low)/TME(high) cohort. CONCLUSIONS: We have developed a GPCR–TME classifier that could predict the OS and immunotherapy response of patients with melanoma highly effectively based on multi-omics analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40001-023-01346-6. |
format | Online Article Text |
id | pubmed-10504724 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105047242023-09-17 Prediction of survival and immunotherapy response by the combined classifier of G protein-coupled receptors and tumor microenvironment in melanoma Shen, Kangjie Wang, Qiangcheng Wang, Lu Yang, Yang Ren, Min Li, Yanlin Gao, Zixu Zheng, Shaoluan Ding, Yiteng Ji, Jiani Wei, Chenlu Zhang, Tianyi Zhu, Yu Feng, Jia Qin, Feng Yang, Yanwen Wei, Chuanyuan Gu, Jianying Eur J Med Res Research BACKGROUND: Melanoma is the deadliest form of skin tumor, and G protein-coupled receptors (GPCRs) play crucial roles in its carcinogenesis. Furthermore, the tumor microenvironment (TME) affects the overall survival (OS) and the response to immunotherapy. The combination of GPCRs and TME from a multi-omics perspective may help to predict the survival of the melanoma patients and their response to immunotherapy. METHODS: Bulk-seq, single-cell RNA sequencing (scRNA-seq), gene mutations, immunotherapy responses, and clinicopathologic feature data were downloaded from public databases, and prognostic GPCRs and immune cells were screened using multiple machine learning algorithms. The expression levels of GPCRs were detected using real-time quantitative polymerase chain reaction (qPCR) in A375 and HaCaT cell lines. The GPCR–TME classifier was constructed and verified using different cohorts and multi-omics. Gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and tracking tumor immunophenotype (TIP) were used to identify the key biological pathways among the GPCR–TME subgroups. Then, tumor mutational burden (TMB), vital mutant genes, antigen presentation genes, and immune checkpoints were compared among the subgroups. Finally, the differences in immunotherapy response rates among the GPCR–TME subgroups were investigated. RESULTS: A total of 12 GPCRs and five immune cell types were screened to establish the GPCR–TME classifier. No significant differences in the expression levels of the 12 GPCRs were found in the two cell lines. Patients with high GPCR score or low TME score had a poor OS; thus, the GPCR(low)/TME(high) subgroup had the most favorable OS. The scRNA-seq result revealed that immune cells had a higher GPCR score than tumor and stromal cells. The GPCR–TME classifier acted as an independent prognostic factor for melanoma. GSEA, WGCNA, and TIP demonstrated that the GPCR(low)/TME(high) subgroup was related to the activation and recruitment of anti-tumor immune cells and the positive regulation of the immune response. From a genomic perspective, the GPCR(low)/TME(high) subgroup had higher TMB, and different mutant genes. Ultimately, higher expression levels of antigen presentation genes and immune checkpoints were observed in the GPCR(low)/TME(high) subgroup, and the melanoma immunotherapy cohorts confirmed that the response rate was highest in the GPCR(low)/TME(high) cohort. CONCLUSIONS: We have developed a GPCR–TME classifier that could predict the OS and immunotherapy response of patients with melanoma highly effectively based on multi-omics analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40001-023-01346-6. BioMed Central 2023-09-16 /pmc/articles/PMC10504724/ /pubmed/37716991 http://dx.doi.org/10.1186/s40001-023-01346-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Shen, Kangjie Wang, Qiangcheng Wang, Lu Yang, Yang Ren, Min Li, Yanlin Gao, Zixu Zheng, Shaoluan Ding, Yiteng Ji, Jiani Wei, Chenlu Zhang, Tianyi Zhu, Yu Feng, Jia Qin, Feng Yang, Yanwen Wei, Chuanyuan Gu, Jianying Prediction of survival and immunotherapy response by the combined classifier of G protein-coupled receptors and tumor microenvironment in melanoma |
title | Prediction of survival and immunotherapy response by the combined classifier of G protein-coupled receptors and tumor microenvironment in melanoma |
title_full | Prediction of survival and immunotherapy response by the combined classifier of G protein-coupled receptors and tumor microenvironment in melanoma |
title_fullStr | Prediction of survival and immunotherapy response by the combined classifier of G protein-coupled receptors and tumor microenvironment in melanoma |
title_full_unstemmed | Prediction of survival and immunotherapy response by the combined classifier of G protein-coupled receptors and tumor microenvironment in melanoma |
title_short | Prediction of survival and immunotherapy response by the combined classifier of G protein-coupled receptors and tumor microenvironment in melanoma |
title_sort | prediction of survival and immunotherapy response by the combined classifier of g protein-coupled receptors and tumor microenvironment in melanoma |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10504724/ https://www.ncbi.nlm.nih.gov/pubmed/37716991 http://dx.doi.org/10.1186/s40001-023-01346-6 |
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