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Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm
Background: Psoriasis is a chronic and immune-mediated skin disorder that currently has no cure. Pyroptosis has been proved to be involved in the pathogenesis and progression of psoriasis. However, the role pyroptosis plays in psoriasis remains elusive. Methods: RNA-sequencing data of psoriasis pati...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9468882/ https://www.ncbi.nlm.nih.gov/pubmed/36110207 http://dx.doi.org/10.3389/fgene.2022.850108 |
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author | Song, Jian-Kun Zhang, Ying Fei, Xiao-Ya Chen, Yi-Ran Luo, Ying Jiang, Jing-Si Ru, Yi Xiang, Yan-Wei Li, Bin Luo, Yue Kuai, Le |
author_facet | Song, Jian-Kun Zhang, Ying Fei, Xiao-Ya Chen, Yi-Ran Luo, Ying Jiang, Jing-Si Ru, Yi Xiang, Yan-Wei Li, Bin Luo, Yue Kuai, Le |
author_sort | Song, Jian-Kun |
collection | PubMed |
description | Background: Psoriasis is a chronic and immune-mediated skin disorder that currently has no cure. Pyroptosis has been proved to be involved in the pathogenesis and progression of psoriasis. However, the role pyroptosis plays in psoriasis remains elusive. Methods: RNA-sequencing data of psoriasis patients were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed pyroptosis-related genes (PRGs) between psoriasis patients and normal individuals were obtained. A principal component analysis (PCA) was conducted to determine whether PRGs could be used to distinguish the samples. PRG and immune cell correlation was also investigated. Subsequently, a novel diagnostic model comprising PRGs for psoriasis was constructed using a random forest algorithm (ntree = 400). A receiver operating characteristic (ROC) analysis was used to evaluate the classification performance through both internal and external validation. Consensus clustering analysis was used to investigate whether there was a difference in biological functions within PRG-based subtypes. Finally, the expression of the kernel PRGs were validated in vivo by qRT-PCR. Results: We identified a total of 39 PRGs, which could distinguish psoriasis samples from normal samples. The process of T cell CD4 memory activated and mast cells resting were correlated with PRGs. Ten PRGs, IL-1β, AIM2, CASP5, DHX9, CASP4, CYCS, CASP1, GZMB, CHMP2B, and CASP8, were subsequently screened using a random forest diagnostic model. ROC analysis revealed that our model has good diagnostic performance in both internal validation (area under the curve [AUC] = 0.930 [95% CI 0.877–0.984]) and external validation (mean AUC = 0.852). PRG subtypes indicated differences in metabolic processes and the MAPK signaling pathway. Finally, the qRT-PCR results demonstrated the apparent dysregulation of PRGs in psoriasis, especially AIM2 and GZMB. Conclusion: Pyroptosis may play a crucial role in psoriasis and could provide new insights into the diagnosis and underlying mechanisms of psoriasis. |
format | Online Article Text |
id | pubmed-9468882 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94688822022-09-14 Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm Song, Jian-Kun Zhang, Ying Fei, Xiao-Ya Chen, Yi-Ran Luo, Ying Jiang, Jing-Si Ru, Yi Xiang, Yan-Wei Li, Bin Luo, Yue Kuai, Le Front Genet Genetics Background: Psoriasis is a chronic and immune-mediated skin disorder that currently has no cure. Pyroptosis has been proved to be involved in the pathogenesis and progression of psoriasis. However, the role pyroptosis plays in psoriasis remains elusive. Methods: RNA-sequencing data of psoriasis patients were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed pyroptosis-related genes (PRGs) between psoriasis patients and normal individuals were obtained. A principal component analysis (PCA) was conducted to determine whether PRGs could be used to distinguish the samples. PRG and immune cell correlation was also investigated. Subsequently, a novel diagnostic model comprising PRGs for psoriasis was constructed using a random forest algorithm (ntree = 400). A receiver operating characteristic (ROC) analysis was used to evaluate the classification performance through both internal and external validation. Consensus clustering analysis was used to investigate whether there was a difference in biological functions within PRG-based subtypes. Finally, the expression of the kernel PRGs were validated in vivo by qRT-PCR. Results: We identified a total of 39 PRGs, which could distinguish psoriasis samples from normal samples. The process of T cell CD4 memory activated and mast cells resting were correlated with PRGs. Ten PRGs, IL-1β, AIM2, CASP5, DHX9, CASP4, CYCS, CASP1, GZMB, CHMP2B, and CASP8, were subsequently screened using a random forest diagnostic model. ROC analysis revealed that our model has good diagnostic performance in both internal validation (area under the curve [AUC] = 0.930 [95% CI 0.877–0.984]) and external validation (mean AUC = 0.852). PRG subtypes indicated differences in metabolic processes and the MAPK signaling pathway. Finally, the qRT-PCR results demonstrated the apparent dysregulation of PRGs in psoriasis, especially AIM2 and GZMB. Conclusion: Pyroptosis may play a crucial role in psoriasis and could provide new insights into the diagnosis and underlying mechanisms of psoriasis. Frontiers Media S.A. 2022-08-30 /pmc/articles/PMC9468882/ /pubmed/36110207 http://dx.doi.org/10.3389/fgene.2022.850108 Text en Copyright © 2022 Song, Zhang, Fei, Chen, Luo, Jiang, Ru, Xiang, Li, Luo and Kuai. 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 | Genetics Song, Jian-Kun Zhang, Ying Fei, Xiao-Ya Chen, Yi-Ran Luo, Ying Jiang, Jing-Si Ru, Yi Xiang, Yan-Wei Li, Bin Luo, Yue Kuai, Le Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm |
title | Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm |
title_full | Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm |
title_fullStr | Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm |
title_full_unstemmed | Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm |
title_short | Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm |
title_sort | classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9468882/ https://www.ncbi.nlm.nih.gov/pubmed/36110207 http://dx.doi.org/10.3389/fgene.2022.850108 |
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