Identification of Potential Biomarkers for Anti-PD-1 Therapy in Melanoma by Weighted Correlation Network Analysis
Melanoma is the most malignant form of skin cancer, which seriously threatens human life and health. Anti-PD-1 immunotherapy has shown clinical benefits in improving patients’ overall survival, but some melanoma patients failed to respond. Effective therapeutic biomarkers are vital to evaluate and o...
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doaj-654ff34ec64249dabcb0eda1f79ccaf82020-11-25T02:01:47ZengMDPI AGGenes2073-44252020-04-011143543510.3390/genes11040435Identification of Potential Biomarkers for Anti-PD-1 Therapy in Melanoma by Weighted Correlation Network AnalysisXuanyi Wang0Zixuan Chai1Yinghong Li2Fei Long3Youjin Hao4Guizhi Pan5Mingwei Liu6Bo Li7Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing 400046, ChinaKey Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing 400046, ChinaSchool of Biological Information, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaKey Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing 400046, ChinaCollege of Life Sciences, Chongqing Normal University, Chongqing 401331, ChinaKey Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing 400046, ChinaKey Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing 400046, ChinaCollege of Life Sciences, Chongqing Normal University, Chongqing 401331, ChinaMelanoma is the most malignant form of skin cancer, which seriously threatens human life and health. Anti-PD-1 immunotherapy has shown clinical benefits in improving patients’ overall survival, but some melanoma patients failed to respond. Effective therapeutic biomarkers are vital to evaluate and optimize benefits from anti-PD-1 treatment. Although the establishment of immunotherapy biomarkers is well underway, studies that identify predictors by gene network-based approaches are lacking. Here, we retrieved the existing datasets (GSE91061, GSE78220 and GSE93157, 79 samples in total) on anti-PD-1 therapy to explore potential therapeutic biomarkers in melanoma using weighted correlation network analysis (WGCNA), function validation and clinical corroboration. As a result, 13 hub genes as critical nodes were traced from the key module associated with clinical features. After receiver operating characteristic (ROC) curve validation by an independent dataset (GSE78220), six hub genes with diagnostic significance were further recovered. Moreover, these six genes were revealed to be closely associated not only with the immune system regulation, immune infiltration, and validated immunotherapy biomarkers, but also with excellent prognostic value and significant expression level in melanoma. The random forest prediction model constructed using these six genes presented a great diagnostic ability for anti-PD-1 immunotherapy response. Taken together, <i>IRF1</i>, <i>JAK2</i>, <i>CD8A</i>, <i>IRF8</i>, <i>STAT5B,</i> and <i>SELL</i> may serve as predictive therapeutic biomarkers for melanoma and could facilitate future anti-PD-1 therapy.https://www.mdpi.com/2073-4425/11/4/435melanomaanti-PD-1 therapyWGCNAbiomarker |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xuanyi Wang Zixuan Chai Yinghong Li Fei Long Youjin Hao Guizhi Pan Mingwei Liu Bo Li |
spellingShingle |
Xuanyi Wang Zixuan Chai Yinghong Li Fei Long Youjin Hao Guizhi Pan Mingwei Liu Bo Li Identification of Potential Biomarkers for Anti-PD-1 Therapy in Melanoma by Weighted Correlation Network Analysis Genes melanoma anti-PD-1 therapy WGCNA biomarker |
author_facet |
Xuanyi Wang Zixuan Chai Yinghong Li Fei Long Youjin Hao Guizhi Pan Mingwei Liu Bo Li |
author_sort |
Xuanyi Wang |
title |
Identification of Potential Biomarkers for Anti-PD-1 Therapy in Melanoma by Weighted Correlation Network Analysis |
title_short |
Identification of Potential Biomarkers for Anti-PD-1 Therapy in Melanoma by Weighted Correlation Network Analysis |
title_full |
Identification of Potential Biomarkers for Anti-PD-1 Therapy in Melanoma by Weighted Correlation Network Analysis |
title_fullStr |
Identification of Potential Biomarkers for Anti-PD-1 Therapy in Melanoma by Weighted Correlation Network Analysis |
title_full_unstemmed |
Identification of Potential Biomarkers for Anti-PD-1 Therapy in Melanoma by Weighted Correlation Network Analysis |
title_sort |
identification of potential biomarkers for anti-pd-1 therapy in melanoma by weighted correlation network analysis |
publisher |
MDPI AG |
series |
Genes |
issn |
2073-4425 |
publishDate |
2020-04-01 |
description |
Melanoma is the most malignant form of skin cancer, which seriously threatens human life and health. Anti-PD-1 immunotherapy has shown clinical benefits in improving patients’ overall survival, but some melanoma patients failed to respond. Effective therapeutic biomarkers are vital to evaluate and optimize benefits from anti-PD-1 treatment. Although the establishment of immunotherapy biomarkers is well underway, studies that identify predictors by gene network-based approaches are lacking. Here, we retrieved the existing datasets (GSE91061, GSE78220 and GSE93157, 79 samples in total) on anti-PD-1 therapy to explore potential therapeutic biomarkers in melanoma using weighted correlation network analysis (WGCNA), function validation and clinical corroboration. As a result, 13 hub genes as critical nodes were traced from the key module associated with clinical features. After receiver operating characteristic (ROC) curve validation by an independent dataset (GSE78220), six hub genes with diagnostic significance were further recovered. Moreover, these six genes were revealed to be closely associated not only with the immune system regulation, immune infiltration, and validated immunotherapy biomarkers, but also with excellent prognostic value and significant expression level in melanoma. The random forest prediction model constructed using these six genes presented a great diagnostic ability for anti-PD-1 immunotherapy response. Taken together, <i>IRF1</i>, <i>JAK2</i>, <i>CD8A</i>, <i>IRF8</i>, <i>STAT5B,</i> and <i>SELL</i> may serve as predictive therapeutic biomarkers for melanoma and could facilitate future anti-PD-1 therapy. |
topic |
melanoma anti-PD-1 therapy WGCNA biomarker |
url |
https://www.mdpi.com/2073-4425/11/4/435 |
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