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Identification of a novel tumour microenvironment‐based prognostic biomarker in skin cutaneous melanoma
Skin cutaneous melanoma (SKCM) is one of the most destructive skin malignancies and has attracted worldwide attention. However, there is a lack of prognostic biomarkers, especially tumour microenvironment (TME)‐based prognostic biomarkers. Therefore, there is an urgent need to investigate the TME in...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8642691/ https://www.ncbi.nlm.nih.gov/pubmed/34755462 http://dx.doi.org/10.1111/jcmm.17021 |
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author | Yang, Rong‐Hua Liang, Bo Li, Jie‐Hua Pi, Xiao‐Bing Yu, Kai Xiang, Shi‐Jian Gu, Ning Chen, Xiao‐Dong Zhou, Si‐Tong |
author_facet | Yang, Rong‐Hua Liang, Bo Li, Jie‐Hua Pi, Xiao‐Bing Yu, Kai Xiang, Shi‐Jian Gu, Ning Chen, Xiao‐Dong Zhou, Si‐Tong |
author_sort | Yang, Rong‐Hua |
collection | PubMed |
description | Skin cutaneous melanoma (SKCM) is one of the most destructive skin malignancies and has attracted worldwide attention. However, there is a lack of prognostic biomarkers, especially tumour microenvironment (TME)‐based prognostic biomarkers. Therefore, there is an urgent need to investigate the TME in SKCM, as well as to identify efficient biomarkers for the diagnosis and treatment of SKCM patients. A comprehensive analysis was performed using SKCM samples from The Cancer Genome Atlas and normal samples from Genotype‐Tissue Expression. TME scores were calculated using the ESTIMATE algorithm, and differential TME scores and differentially expressed prognostic genes were successively identified. We further identified more reliable prognostic genes via least absolute shrinkage and selection operator regression analysis and constructed a prognostic prediction model to predict overall survival. Receiver operating characteristic analysis was used to evaluate the diagnostic efficacy, and Cox regression analysis was applied to explore the relationship with clinicopathological characteristics. Finally, we identified a novel prognostic biomarker and conducted a functional enrichment analysis. After considering ESTIMATEScore and tumour purity as differential TME scores, we identified 34 differentially expressed prognostic genes. Using least absolute shrinkage and selection operator regression, we identified seven potential prognostic biomarkers (SLC13A5, RBM24, IGHV3OR16‐15, PRSS35, SLC7A10, IGHV1‐69D and IGHV2‐26). Combined with receiver operating characteristic and regression analyses, we determined PRSS35 as a novel TME‐based prognostic biomarker in SKCM, and functional analysis enriched immune‐related cells, functions and signalling pathways. Our study indicated that PRSS35 could act as a potential prognostic biomarker in SKCM by investigating the TME, so as to provide new ideas and insights for the clinical diagnosis and treatment of SKCM. |
format | Online Article Text |
id | pubmed-8642691 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86426912021-12-15 Identification of a novel tumour microenvironment‐based prognostic biomarker in skin cutaneous melanoma Yang, Rong‐Hua Liang, Bo Li, Jie‐Hua Pi, Xiao‐Bing Yu, Kai Xiang, Shi‐Jian Gu, Ning Chen, Xiao‐Dong Zhou, Si‐Tong J Cell Mol Med Original Articles Skin cutaneous melanoma (SKCM) is one of the most destructive skin malignancies and has attracted worldwide attention. However, there is a lack of prognostic biomarkers, especially tumour microenvironment (TME)‐based prognostic biomarkers. Therefore, there is an urgent need to investigate the TME in SKCM, as well as to identify efficient biomarkers for the diagnosis and treatment of SKCM patients. A comprehensive analysis was performed using SKCM samples from The Cancer Genome Atlas and normal samples from Genotype‐Tissue Expression. TME scores were calculated using the ESTIMATE algorithm, and differential TME scores and differentially expressed prognostic genes were successively identified. We further identified more reliable prognostic genes via least absolute shrinkage and selection operator regression analysis and constructed a prognostic prediction model to predict overall survival. Receiver operating characteristic analysis was used to evaluate the diagnostic efficacy, and Cox regression analysis was applied to explore the relationship with clinicopathological characteristics. Finally, we identified a novel prognostic biomarker and conducted a functional enrichment analysis. After considering ESTIMATEScore and tumour purity as differential TME scores, we identified 34 differentially expressed prognostic genes. Using least absolute shrinkage and selection operator regression, we identified seven potential prognostic biomarkers (SLC13A5, RBM24, IGHV3OR16‐15, PRSS35, SLC7A10, IGHV1‐69D and IGHV2‐26). Combined with receiver operating characteristic and regression analyses, we determined PRSS35 as a novel TME‐based prognostic biomarker in SKCM, and functional analysis enriched immune‐related cells, functions and signalling pathways. Our study indicated that PRSS35 could act as a potential prognostic biomarker in SKCM by investigating the TME, so as to provide new ideas and insights for the clinical diagnosis and treatment of SKCM. John Wiley and Sons Inc. 2021-11-10 2021-12 /pmc/articles/PMC8642691/ /pubmed/34755462 http://dx.doi.org/10.1111/jcmm.17021 Text en © 2021 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Articles Yang, Rong‐Hua Liang, Bo Li, Jie‐Hua Pi, Xiao‐Bing Yu, Kai Xiang, Shi‐Jian Gu, Ning Chen, Xiao‐Dong Zhou, Si‐Tong Identification of a novel tumour microenvironment‐based prognostic biomarker in skin cutaneous melanoma |
title | Identification of a novel tumour microenvironment‐based prognostic biomarker in skin cutaneous melanoma |
title_full | Identification of a novel tumour microenvironment‐based prognostic biomarker in skin cutaneous melanoma |
title_fullStr | Identification of a novel tumour microenvironment‐based prognostic biomarker in skin cutaneous melanoma |
title_full_unstemmed | Identification of a novel tumour microenvironment‐based prognostic biomarker in skin cutaneous melanoma |
title_short | Identification of a novel tumour microenvironment‐based prognostic biomarker in skin cutaneous melanoma |
title_sort | identification of a novel tumour microenvironment‐based prognostic biomarker in skin cutaneous melanoma |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8642691/ https://www.ncbi.nlm.nih.gov/pubmed/34755462 http://dx.doi.org/10.1111/jcmm.17021 |
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