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Prognostic genes of melanoma identified by weighted gene co-expression network analysis and drug repositioning using a network-based method

Melanoma is one of the most malignant types of skin cancer. However, the efficacy and utility of available drug therapies for melanoma are limited. The objective of the present study was to identify potential genes associated with melanoma progression and to explore approved therapeutic drugs that t...

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Autores principales: Wang, Lu, Wei, Chuan-Yuan, Xu, Yuan-Yuan, Deng, Xin-Yi, Wang, Qiang, Ying, Jiang-Hui, Zhang, Si-Min, Yuan, Xin, Xuan, Tian-Fan, Pan, Yu-Yan, Gu, Jian-Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864934/
https://www.ncbi.nlm.nih.gov/pubmed/31788081
http://dx.doi.org/10.3892/ol.2019.10961
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author Wang, Lu
Wei, Chuan-Yuan
Xu, Yuan-Yuan
Deng, Xin-Yi
Wang, Qiang
Ying, Jiang-Hui
Zhang, Si-Min
Yuan, Xin
Xuan, Tian-Fan
Pan, Yu-Yan
Gu, Jian-Ying
author_facet Wang, Lu
Wei, Chuan-Yuan
Xu, Yuan-Yuan
Deng, Xin-Yi
Wang, Qiang
Ying, Jiang-Hui
Zhang, Si-Min
Yuan, Xin
Xuan, Tian-Fan
Pan, Yu-Yan
Gu, Jian-Ying
author_sort Wang, Lu
collection PubMed
description Melanoma is one of the most malignant types of skin cancer. However, the efficacy and utility of available drug therapies for melanoma are limited. The objective of the present study was to identify potential genes associated with melanoma progression and to explore approved therapeutic drugs that target these genes. Weighted gene co-expression network analysis was used to construct a gene co-expression network, explore the associations between genes and clinical characteristics and identify potential biomarkers. Gene expression profiles of the GSE65904 dataset were obtained from the Gene Expression Omnibus database. RNA-sequencing data and clinical information associated with melanoma obtained from The Cancer Genome Atlas were used for biomarker validation. A total of 15 modules were identified through average linkage hierarchical clustering. In the two significant modules, three network hub genes associated with melanoma prognosis were identified: C-X-C motif chemokine receptor 4 (CXCR4), interleukin 7 receptor (IL7R) and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit γ (PIK3CG). The receiver operating characteristic curve indicated that the mRNA levels of these genes exhibited excellent prognostic efficiency for primary and metastatic tumor tissues. In addition, the proximity between candidate genes associated with melanoma progression and drug targets obtained from DrugBank was calculated in the protein interaction network, and the top 15 drugs that may be suitable for treating melanoma were identified. In summary, co-expression network analysis led to the selection of CXCR4, IL7R and PIK3CG for further basic and clinical research on melanoma. Utilizing a network-based method, 15 drugs that exhibited potential for the treatment of melanoma were identified.
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spelling pubmed-68649342019-11-30 Prognostic genes of melanoma identified by weighted gene co-expression network analysis and drug repositioning using a network-based method Wang, Lu Wei, Chuan-Yuan Xu, Yuan-Yuan Deng, Xin-Yi Wang, Qiang Ying, Jiang-Hui Zhang, Si-Min Yuan, Xin Xuan, Tian-Fan Pan, Yu-Yan Gu, Jian-Ying Oncol Lett Articles Melanoma is one of the most malignant types of skin cancer. However, the efficacy and utility of available drug therapies for melanoma are limited. The objective of the present study was to identify potential genes associated with melanoma progression and to explore approved therapeutic drugs that target these genes. Weighted gene co-expression network analysis was used to construct a gene co-expression network, explore the associations between genes and clinical characteristics and identify potential biomarkers. Gene expression profiles of the GSE65904 dataset were obtained from the Gene Expression Omnibus database. RNA-sequencing data and clinical information associated with melanoma obtained from The Cancer Genome Atlas were used for biomarker validation. A total of 15 modules were identified through average linkage hierarchical clustering. In the two significant modules, three network hub genes associated with melanoma prognosis were identified: C-X-C motif chemokine receptor 4 (CXCR4), interleukin 7 receptor (IL7R) and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit γ (PIK3CG). The receiver operating characteristic curve indicated that the mRNA levels of these genes exhibited excellent prognostic efficiency for primary and metastatic tumor tissues. In addition, the proximity between candidate genes associated with melanoma progression and drug targets obtained from DrugBank was calculated in the protein interaction network, and the top 15 drugs that may be suitable for treating melanoma were identified. In summary, co-expression network analysis led to the selection of CXCR4, IL7R and PIK3CG for further basic and clinical research on melanoma. Utilizing a network-based method, 15 drugs that exhibited potential for the treatment of melanoma were identified. D.A. Spandidos 2019-12 2019-10-04 /pmc/articles/PMC6864934/ /pubmed/31788081 http://dx.doi.org/10.3892/ol.2019.10961 Text en Copyright: © Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Wang, Lu
Wei, Chuan-Yuan
Xu, Yuan-Yuan
Deng, Xin-Yi
Wang, Qiang
Ying, Jiang-Hui
Zhang, Si-Min
Yuan, Xin
Xuan, Tian-Fan
Pan, Yu-Yan
Gu, Jian-Ying
Prognostic genes of melanoma identified by weighted gene co-expression network analysis and drug repositioning using a network-based method
title Prognostic genes of melanoma identified by weighted gene co-expression network analysis and drug repositioning using a network-based method
title_full Prognostic genes of melanoma identified by weighted gene co-expression network analysis and drug repositioning using a network-based method
title_fullStr Prognostic genes of melanoma identified by weighted gene co-expression network analysis and drug repositioning using a network-based method
title_full_unstemmed Prognostic genes of melanoma identified by weighted gene co-expression network analysis and drug repositioning using a network-based method
title_short Prognostic genes of melanoma identified by weighted gene co-expression network analysis and drug repositioning using a network-based method
title_sort prognostic genes of melanoma identified by weighted gene co-expression network analysis and drug repositioning using a network-based method
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864934/
https://www.ncbi.nlm.nih.gov/pubmed/31788081
http://dx.doi.org/10.3892/ol.2019.10961
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