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Deciphering the contributions of cuproptosis in the development of hypertrophic scar using single-cell analysis and machine learning techniques

Hypertrophic scar (HS) is a chronic inflammatory skin disease characterized by excessive deposition of extracellular matrix, but the exact mechanisms related to its formation remain unclear, making it difficult to treat. This study aimed to investigate the potential role of cuproptosis in the inform...

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Autores principales: Song, Binyu, Liu, Wei, Zhu, Yuhan, Peng, Yixuan, Cui, Zhiwei, Gao, Botao, Chen, Lin, Yu, Zhou, Song, Baoqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318401/
https://www.ncbi.nlm.nih.gov/pubmed/37409114
http://dx.doi.org/10.3389/fimmu.2023.1207522
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author Song, Binyu
Liu, Wei
Zhu, Yuhan
Peng, Yixuan
Cui, Zhiwei
Gao, Botao
Chen, Lin
Yu, Zhou
Song, Baoqiang
author_facet Song, Binyu
Liu, Wei
Zhu, Yuhan
Peng, Yixuan
Cui, Zhiwei
Gao, Botao
Chen, Lin
Yu, Zhou
Song, Baoqiang
author_sort Song, Binyu
collection PubMed
description Hypertrophic scar (HS) is a chronic inflammatory skin disease characterized by excessive deposition of extracellular matrix, but the exact mechanisms related to its formation remain unclear, making it difficult to treat. This study aimed to investigate the potential role of cuproptosis in the information of HS. To this end, we used single-cell sequencing and bulk transcriptome data, and screened for cuproptosis-related genes (CRGs) using differential gene analysis and machine learning algorithms (random forest and support vector machine). Through this process, we identified a group of genes, including ATP7A, ULK1, and MTF1, as novel therapeutic targets for HS. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to confirm the mRNA expression of ATP7A, ULK1, and MTF1 in both HS and normal skin (NS) tissues. We also constructed a diagnostic model for HS and analyzed the immune infiltration characteristics. Additionally, we used the expression profiles of CRGs to perform subgroup analysis of HS. We focused mainly on fibroblasts in the transcriptional profile at single-cell resolution. By calculating the cuproptosis activity of each fibroblast, we found that cuproptosis activity of normal skin fibroblasts increased, providing further insights into the pathogenesis of HS. We also analyzed the cell communication network and transcription factor regulatory network activity, and found the existence of a fibroblast-centered communication regulation network in HS, where cuproptosis activity in fibroblasts affects intercellular communication. Using transcription factor regulatory activity network analysis, we obtained highly active transcription factors, and correlation analysis with CRGs suggested that CRGs may serve as potential target genes for transcription factors. Overall, our study provides new insights into the pathophysiological mechanisms of HS, which may inspire new ideas for the diagnosis and treatment.
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spelling pubmed-103184012023-07-05 Deciphering the contributions of cuproptosis in the development of hypertrophic scar using single-cell analysis and machine learning techniques Song, Binyu Liu, Wei Zhu, Yuhan Peng, Yixuan Cui, Zhiwei Gao, Botao Chen, Lin Yu, Zhou Song, Baoqiang Front Immunol Immunology Hypertrophic scar (HS) is a chronic inflammatory skin disease characterized by excessive deposition of extracellular matrix, but the exact mechanisms related to its formation remain unclear, making it difficult to treat. This study aimed to investigate the potential role of cuproptosis in the information of HS. To this end, we used single-cell sequencing and bulk transcriptome data, and screened for cuproptosis-related genes (CRGs) using differential gene analysis and machine learning algorithms (random forest and support vector machine). Through this process, we identified a group of genes, including ATP7A, ULK1, and MTF1, as novel therapeutic targets for HS. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to confirm the mRNA expression of ATP7A, ULK1, and MTF1 in both HS and normal skin (NS) tissues. We also constructed a diagnostic model for HS and analyzed the immune infiltration characteristics. Additionally, we used the expression profiles of CRGs to perform subgroup analysis of HS. We focused mainly on fibroblasts in the transcriptional profile at single-cell resolution. By calculating the cuproptosis activity of each fibroblast, we found that cuproptosis activity of normal skin fibroblasts increased, providing further insights into the pathogenesis of HS. We also analyzed the cell communication network and transcription factor regulatory network activity, and found the existence of a fibroblast-centered communication regulation network in HS, where cuproptosis activity in fibroblasts affects intercellular communication. Using transcription factor regulatory activity network analysis, we obtained highly active transcription factors, and correlation analysis with CRGs suggested that CRGs may serve as potential target genes for transcription factors. Overall, our study provides new insights into the pathophysiological mechanisms of HS, which may inspire new ideas for the diagnosis and treatment. Frontiers Media S.A. 2023-06-20 /pmc/articles/PMC10318401/ /pubmed/37409114 http://dx.doi.org/10.3389/fimmu.2023.1207522 Text en Copyright © 2023 Song, Liu, Zhu, Peng, Cui, Gao, Chen, Yu and Song https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Song, Binyu
Liu, Wei
Zhu, Yuhan
Peng, Yixuan
Cui, Zhiwei
Gao, Botao
Chen, Lin
Yu, Zhou
Song, Baoqiang
Deciphering the contributions of cuproptosis in the development of hypertrophic scar using single-cell analysis and machine learning techniques
title Deciphering the contributions of cuproptosis in the development of hypertrophic scar using single-cell analysis and machine learning techniques
title_full Deciphering the contributions of cuproptosis in the development of hypertrophic scar using single-cell analysis and machine learning techniques
title_fullStr Deciphering the contributions of cuproptosis in the development of hypertrophic scar using single-cell analysis and machine learning techniques
title_full_unstemmed Deciphering the contributions of cuproptosis in the development of hypertrophic scar using single-cell analysis and machine learning techniques
title_short Deciphering the contributions of cuproptosis in the development of hypertrophic scar using single-cell analysis and machine learning techniques
title_sort deciphering the contributions of cuproptosis in the development of hypertrophic scar using single-cell analysis and machine learning techniques
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318401/
https://www.ncbi.nlm.nih.gov/pubmed/37409114
http://dx.doi.org/10.3389/fimmu.2023.1207522
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