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m5C-Related Signatures for Predicting Prognosis in Cutaneous Melanoma with Machine Learning

BACKGROUND: Cutaneous melanoma (CM) is one of the most life-threatening primary skin cancers and is prone to distant metastases. A widespread presence of posttranscriptional modification of RNA, 5-methylcytosine (m5C), has been observed in human cancers. However, the potential mechanism of the tumor...

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Autores principales: Huang, Maoxin, Zhang, Yi, Ou, Xiaohong, Wang, Caiyun, Wang, Xueqing, Qin, Bibo, Zhang, Qiong, Yu, Jie, Zhang, Jianxiang, Yu, Jianbin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360728/
https://www.ncbi.nlm.nih.gov/pubmed/34394351
http://dx.doi.org/10.1155/2021/6173206
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author Huang, Maoxin
Zhang, Yi
Ou, Xiaohong
Wang, Caiyun
Wang, Xueqing
Qin, Bibo
Zhang, Qiong
Yu, Jie
Zhang, Jianxiang
Yu, Jianbin
author_facet Huang, Maoxin
Zhang, Yi
Ou, Xiaohong
Wang, Caiyun
Wang, Xueqing
Qin, Bibo
Zhang, Qiong
Yu, Jie
Zhang, Jianxiang
Yu, Jianbin
author_sort Huang, Maoxin
collection PubMed
description BACKGROUND: Cutaneous melanoma (CM) is one of the most life-threatening primary skin cancers and is prone to distant metastases. A widespread presence of posttranscriptional modification of RNA, 5-methylcytosine (m5C), has been observed in human cancers. However, the potential mechanism of the tumorigenesis and prognosis in CM by dysregulated m5C-related regulators is obscure. METHODS: We use comprehensive bioinformatics analyses to explore the expression of m5C regulators in CM, the prognostic implications of the m5C regulators, the frequency of the copy number variant (CNV), and somatic mutations in m5C regulators. Additionally, the CM patients were divided into three clusters for better predicting clinical features and outcomes via consensus clustering of m5C regulators. Then, the risk score was established via Lasso Cox regression analysis. Next, the prognosis value and clinical characteristics of m5C-related signatures were further explored. Then, machine learning was used to recognize the outstanding m5C regulators to risk score. Finally, the expression level and clinical value of USUN6 were analyzed via the tissue microarray (TMA) cohort. RESULTS: We found that m5C regulators were dysregulated in CM, with a high frequency of somatic mutations and CNV alterations of the m5C regulatory gene in CM. Furthermore, 16 m5C-related proteins interacted with each other frequently, and we divided CM patients into three clusters to better predicting clinical features and outcomes. Then, five m5C regulators were selected as a risk score based on the LASSO model. The XGBoost algorithm recognized that NOP2 and NSUN6 were the most significant risk score contributors. Immunohistochemistry has verified that low expression of USUN6 was closely correlated with CM progression. CONCLUSION: The m5C-related signatures can be used as new prognostic biomarkers and therapeutic targets for CM, and NSUN6 might play a vital role in tumorigenesis and malignant progression.
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spelling pubmed-83607282021-08-13 m5C-Related Signatures for Predicting Prognosis in Cutaneous Melanoma with Machine Learning Huang, Maoxin Zhang, Yi Ou, Xiaohong Wang, Caiyun Wang, Xueqing Qin, Bibo Zhang, Qiong Yu, Jie Zhang, Jianxiang Yu, Jianbin J Oncol Research Article BACKGROUND: Cutaneous melanoma (CM) is one of the most life-threatening primary skin cancers and is prone to distant metastases. A widespread presence of posttranscriptional modification of RNA, 5-methylcytosine (m5C), has been observed in human cancers. However, the potential mechanism of the tumorigenesis and prognosis in CM by dysregulated m5C-related regulators is obscure. METHODS: We use comprehensive bioinformatics analyses to explore the expression of m5C regulators in CM, the prognostic implications of the m5C regulators, the frequency of the copy number variant (CNV), and somatic mutations in m5C regulators. Additionally, the CM patients were divided into three clusters for better predicting clinical features and outcomes via consensus clustering of m5C regulators. Then, the risk score was established via Lasso Cox regression analysis. Next, the prognosis value and clinical characteristics of m5C-related signatures were further explored. Then, machine learning was used to recognize the outstanding m5C regulators to risk score. Finally, the expression level and clinical value of USUN6 were analyzed via the tissue microarray (TMA) cohort. RESULTS: We found that m5C regulators were dysregulated in CM, with a high frequency of somatic mutations and CNV alterations of the m5C regulatory gene in CM. Furthermore, 16 m5C-related proteins interacted with each other frequently, and we divided CM patients into three clusters to better predicting clinical features and outcomes. Then, five m5C regulators were selected as a risk score based on the LASSO model. The XGBoost algorithm recognized that NOP2 and NSUN6 were the most significant risk score contributors. Immunohistochemistry has verified that low expression of USUN6 was closely correlated with CM progression. CONCLUSION: The m5C-related signatures can be used as new prognostic biomarkers and therapeutic targets for CM, and NSUN6 might play a vital role in tumorigenesis and malignant progression. Hindawi 2021-08-04 /pmc/articles/PMC8360728/ /pubmed/34394351 http://dx.doi.org/10.1155/2021/6173206 Text en Copyright © 2021 Maoxin Huang 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
Huang, Maoxin
Zhang, Yi
Ou, Xiaohong
Wang, Caiyun
Wang, Xueqing
Qin, Bibo
Zhang, Qiong
Yu, Jie
Zhang, Jianxiang
Yu, Jianbin
m5C-Related Signatures for Predicting Prognosis in Cutaneous Melanoma with Machine Learning
title m5C-Related Signatures for Predicting Prognosis in Cutaneous Melanoma with Machine Learning
title_full m5C-Related Signatures for Predicting Prognosis in Cutaneous Melanoma with Machine Learning
title_fullStr m5C-Related Signatures for Predicting Prognosis in Cutaneous Melanoma with Machine Learning
title_full_unstemmed m5C-Related Signatures for Predicting Prognosis in Cutaneous Melanoma with Machine Learning
title_short m5C-Related Signatures for Predicting Prognosis in Cutaneous Melanoma with Machine Learning
title_sort m5c-related signatures for predicting prognosis in cutaneous melanoma with machine learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360728/
https://www.ncbi.nlm.nih.gov/pubmed/34394351
http://dx.doi.org/10.1155/2021/6173206
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