Development and Validation of Tumor Immunogenicity Based Gene Signature for Skin Cancer Risk Stratification

Melanoma is one of the most aggressive types of skin cancer, with significant heterogeneity in overall survival. Currently, tumor-node-metastasis (TNM) staging is insufficient to provide accurate survival prediction and appropriate treatment decision making for several types of tumors, such as those...

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Autores principales:	Maryam Yavartanoo, Gwan-Su Yi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	immunotherapy clinical outcomes prognosis immune evasion Biology (General) QH301-705.5 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/5ac35c1caf8f47788bd440ea67e0fa07
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spelling	oai:doaj.org-article:5ac35c1caf8f47788bd440ea67e0fa072021-11-11T17:25:44ZDevelopment and Validation of Tumor Immunogenicity Based Gene Signature for Skin Cancer Risk Stratification10.3390/ijms2221120251422-00671661-6596https://doaj.org/article/5ac35c1caf8f47788bd440ea67e0fa072021-11-01T00:00:00Zhttps://www.mdpi.com/1422-0067/22/21/12025https://doaj.org/toc/1661-6596https://doaj.org/toc/1422-0067Melanoma is one of the most aggressive types of skin cancer, with significant heterogeneity in overall survival. Currently, tumor-node-metastasis (TNM) staging is insufficient to provide accurate survival prediction and appropriate treatment decision making for several types of tumors, such as those in melanoma patients. Therefore, the identification of more reliable prognosis biomarkers is urgently essential. Recent studies have shown that low immune cells infiltration is significantly associated with unfavorable clinical outcome in melanoma patients. Here we constructed a prognostic-related gene signature for melanoma risk stratification by quantifying the levels of several cancer hallmarks and identify the Wnt/β-catenin activation pathway as a primary risk factor for low tumor immunity. A series of bioinformatics and statistical methods were combined and applied to construct a Wnt-immune-related prognosis gene signature. With this gene signature, we computed risk scores for individual patients that can predict overall survival. To evaluate the robustness of the result, we validated the signature in multiple independent GEO datasets. Finally, an overall survival-related nomogram was established based on the gene signature and clinicopathological features. The Wnt-immune-related prognostic risk score could better predict overall survival compared with standard clinicopathological features. Our results provide a comprehensive map of the oncogene-immune-related gene signature that can serve as valuable biomarkers for better clinical decision making.Maryam YavartanooGwan-Su YiMDPI AGarticleimmunotherapyclinical outcomesprognosisimmune evasionBiology (General)QH301-705.5ChemistryQD1-999ENInternational Journal of Molecular Sciences, Vol 22, Iss 12025, p 12025 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	immunotherapy clinical outcomes prognosis immune evasion Biology (General) QH301-705.5 Chemistry QD1-999
spellingShingle	immunotherapy clinical outcomes prognosis immune evasion Biology (General) QH301-705.5 Chemistry QD1-999 Maryam Yavartanoo Gwan-Su Yi Development and Validation of Tumor Immunogenicity Based Gene Signature for Skin Cancer Risk Stratification
description	Melanoma is one of the most aggressive types of skin cancer, with significant heterogeneity in overall survival. Currently, tumor-node-metastasis (TNM) staging is insufficient to provide accurate survival prediction and appropriate treatment decision making for several types of tumors, such as those in melanoma patients. Therefore, the identification of more reliable prognosis biomarkers is urgently essential. Recent studies have shown that low immune cells infiltration is significantly associated with unfavorable clinical outcome in melanoma patients. Here we constructed a prognostic-related gene signature for melanoma risk stratification by quantifying the levels of several cancer hallmarks and identify the Wnt/β-catenin activation pathway as a primary risk factor for low tumor immunity. A series of bioinformatics and statistical methods were combined and applied to construct a Wnt-immune-related prognosis gene signature. With this gene signature, we computed risk scores for individual patients that can predict overall survival. To evaluate the robustness of the result, we validated the signature in multiple independent GEO datasets. Finally, an overall survival-related nomogram was established based on the gene signature and clinicopathological features. The Wnt-immune-related prognostic risk score could better predict overall survival compared with standard clinicopathological features. Our results provide a comprehensive map of the oncogene-immune-related gene signature that can serve as valuable biomarkers for better clinical decision making.
format	article
author	Maryam Yavartanoo Gwan-Su Yi
author_facet	Maryam Yavartanoo Gwan-Su Yi
author_sort	Maryam Yavartanoo
title	Development and Validation of Tumor Immunogenicity Based Gene Signature for Skin Cancer Risk Stratification
title_short	Development and Validation of Tumor Immunogenicity Based Gene Signature for Skin Cancer Risk Stratification
title_full	Development and Validation of Tumor Immunogenicity Based Gene Signature for Skin Cancer Risk Stratification
title_fullStr	Development and Validation of Tumor Immunogenicity Based Gene Signature for Skin Cancer Risk Stratification
title_full_unstemmed	Development and Validation of Tumor Immunogenicity Based Gene Signature for Skin Cancer Risk Stratification
title_sort	development and validation of tumor immunogenicity based gene signature for skin cancer risk stratification
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5ac35c1caf8f47788bd440ea67e0fa07
work_keys_str_mv	AT maryamyavartanoo developmentandvalidationoftumorimmunogenicitybasedgenesignatureforskincancerriskstratification AT gwansuyi developmentandvalidationoftumorimmunogenicitybasedgenesignatureforskincancerriskstratification
_version_	1718432119369236480

Development and Validation of Tumor Immunogenicity Based Gene Signature for Skin Cancer Risk Stratification

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