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Identification of Three Genes Associated with Metastasis in Melanoma and Construction of a Predictive Model: A Multiracial Identification

The aim of this study was to identify hub genes associated with metastasis and prognosis in melanoma. Weighted gene coexpression network analysis (WGCNA) was performed to screen and identify hub genes. ROC and K-M analyses were used to verify the hub genes in the internal and external data sets. The...

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Autores principales: Chen, Ying, Wang, Dan, Li, Qingyun, Zhang, Yiyi, Peng, Zheng, He, Yu, Lin, Bin, Xu, Meifang, Chen, Qiong, Chen, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9148232/
https://www.ncbi.nlm.nih.gov/pubmed/35637857
http://dx.doi.org/10.1155/2022/4567063
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author Chen, Ying
Wang, Dan
Li, Qingyun
Zhang, Yiyi
Peng, Zheng
He, Yu
Lin, Bin
Xu, Meifang
Chen, Qiong
Chen, Yang
author_facet Chen, Ying
Wang, Dan
Li, Qingyun
Zhang, Yiyi
Peng, Zheng
He, Yu
Lin, Bin
Xu, Meifang
Chen, Qiong
Chen, Yang
author_sort Chen, Ying
collection PubMed
description The aim of this study was to identify hub genes associated with metastasis and prognosis in melanoma. Weighted gene coexpression network analysis (WGCNA) was performed to screen and identify hub genes. ROC and K-M analyses were used to verify the hub genes in the internal and external data sets. The risk score model and nomogram model were constructed based on the IHC result. Through WGCNA, the three hub genes, SNRPD2, SNRPD3, and EIF4A3, were identified. In the external data set, the hub genes identified were associated with the worse prognosis (TCGA, SNRPD2, P ≤ 0.02; SNRPD3, P = 0.12; EIF4A3, P = 0.11; GSE65904, SNRPD2, P = 0.04; SNRPD3, P = 0.10; EIF4A3, P < 0.01; GSE19234, SNRPD2, P < 0.01; SNRPD3, P < 0.01; EIF4A3, P < 0.01). In the GSE8401, we found that the hub genes were highly expressed in the metastasis compared with the nonmetastasis group (SNRPD2, 988.5 ± 47.83 vs. 738.4 ± 35.35, P < 0.01; SNRPD3, 502.7 ± 25.7 vs. 416.4 ± 23.88, P = 0.02; EIF4A3, 567.6 ± 19.56 vs. 495.2 ± 21.1, P = 0.01). Moreover, the hub genes were identified by the IHC in our data set. The result was similar with the external data set. The hub genes could predict the metastasis and prognosis in the Chinese MM patients. Finally, the GSEA and Pearson analysis demonstrated that the SNRPD2 was associated with the immunotherapy. The three hub genes were identified and validated in MM patients in external and internal data sets. The risk factor model was constructed and verified as a powerful model to predict metastasis and prognosis in MM patients.
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spelling pubmed-91482322022-05-29 Identification of Three Genes Associated with Metastasis in Melanoma and Construction of a Predictive Model: A Multiracial Identification Chen, Ying Wang, Dan Li, Qingyun Zhang, Yiyi Peng, Zheng He, Yu Lin, Bin Xu, Meifang Chen, Qiong Chen, Yang J Oncol Research Article The aim of this study was to identify hub genes associated with metastasis and prognosis in melanoma. Weighted gene coexpression network analysis (WGCNA) was performed to screen and identify hub genes. ROC and K-M analyses were used to verify the hub genes in the internal and external data sets. The risk score model and nomogram model were constructed based on the IHC result. Through WGCNA, the three hub genes, SNRPD2, SNRPD3, and EIF4A3, were identified. In the external data set, the hub genes identified were associated with the worse prognosis (TCGA, SNRPD2, P ≤ 0.02; SNRPD3, P = 0.12; EIF4A3, P = 0.11; GSE65904, SNRPD2, P = 0.04; SNRPD3, P = 0.10; EIF4A3, P < 0.01; GSE19234, SNRPD2, P < 0.01; SNRPD3, P < 0.01; EIF4A3, P < 0.01). In the GSE8401, we found that the hub genes were highly expressed in the metastasis compared with the nonmetastasis group (SNRPD2, 988.5 ± 47.83 vs. 738.4 ± 35.35, P < 0.01; SNRPD3, 502.7 ± 25.7 vs. 416.4 ± 23.88, P = 0.02; EIF4A3, 567.6 ± 19.56 vs. 495.2 ± 21.1, P = 0.01). Moreover, the hub genes were identified by the IHC in our data set. The result was similar with the external data set. The hub genes could predict the metastasis and prognosis in the Chinese MM patients. Finally, the GSEA and Pearson analysis demonstrated that the SNRPD2 was associated with the immunotherapy. The three hub genes were identified and validated in MM patients in external and internal data sets. The risk factor model was constructed and verified as a powerful model to predict metastasis and prognosis in MM patients. Hindawi 2022-05-21 /pmc/articles/PMC9148232/ /pubmed/35637857 http://dx.doi.org/10.1155/2022/4567063 Text en Copyright © 2022 Ying Chen et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Chen, Ying
Wang, Dan
Li, Qingyun
Zhang, Yiyi
Peng, Zheng
He, Yu
Lin, Bin
Xu, Meifang
Chen, Qiong
Chen, Yang
Identification of Three Genes Associated with Metastasis in Melanoma and Construction of a Predictive Model: A Multiracial Identification
title Identification of Three Genes Associated with Metastasis in Melanoma and Construction of a Predictive Model: A Multiracial Identification
title_full Identification of Three Genes Associated with Metastasis in Melanoma and Construction of a Predictive Model: A Multiracial Identification
title_fullStr Identification of Three Genes Associated with Metastasis in Melanoma and Construction of a Predictive Model: A Multiracial Identification
title_full_unstemmed Identification of Three Genes Associated with Metastasis in Melanoma and Construction of a Predictive Model: A Multiracial Identification
title_short Identification of Three Genes Associated with Metastasis in Melanoma and Construction of a Predictive Model: A Multiracial Identification
title_sort identification of three genes associated with metastasis in melanoma and construction of a predictive model: a multiracial identification
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9148232/
https://www.ncbi.nlm.nih.gov/pubmed/35637857
http://dx.doi.org/10.1155/2022/4567063
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