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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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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