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 patie...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.850108/full |
| _version_ | 1797999839499255808 |
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| author | Jian-Kun Song Ying Zhang Ying Zhang Xiao-Ya Fei Yi-Ran Chen Yi-Ran Chen Ying Luo Ying Luo Jing-Si Jiang Jing-Si Jiang Yi Ru Yi Ru Yan-Wei Xiang Yan-Wei Xiang Bin Li Bin Li Yue Luo Le Kuai Le Kuai |
| author_facet | Jian-Kun Song Ying Zhang Ying Zhang Xiao-Ya Fei Yi-Ran Chen Yi-Ran Chen Ying Luo Ying Luo Jing-Si Jiang Jing-Si Jiang Yi Ru Yi Ru Yan-Wei Xiang Yan-Wei Xiang Bin Li Bin Li Yue Luo Le Kuai Le Kuai |
| author_sort | Jian-Kun Song |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T11:11:08Z |
| format | Article |
| id | doaj.art-cce61c39786240028aa36bc0e31018ec |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-11T11:11:08Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-cce61c39786240028aa36bc0e31018ec2022-12-22T04:27:30ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-08-011310.3389/fgene.2022.850108850108Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithmJian-Kun Song0Ying Zhang1Ying Zhang2Xiao-Ya Fei3Yi-Ran Chen4Yi-Ran Chen5Ying Luo6Ying Luo7Jing-Si Jiang8Jing-Si Jiang9Yi Ru10Yi Ru11Yan-Wei Xiang12Yan-Wei Xiang13Bin Li14Bin Li15Yue Luo16Le Kuai17Le Kuai18Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaInstitute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai, ChinaShanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaInstitute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai, ChinaDepartment of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaInstitute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai, ChinaDepartment of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaInstitute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai, ChinaDepartment of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaInstitute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai, ChinaDepartment of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaSchool of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaShanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, ChinaInstitute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai, ChinaShanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaInstitute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai, ChinaBackground: 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.https://www.frontiersin.org/articles/10.3389/fgene.2022.850108/fullpyroptosispyroptosis-related genespsoriasisrandom forest algorithmmachine learning |
| spellingShingle | Jian-Kun Song Ying Zhang Ying Zhang Xiao-Ya Fei Yi-Ran Chen Yi-Ran Chen Ying Luo Ying Luo Jing-Si Jiang Jing-Si Jiang Yi Ru Yi Ru Yan-Wei Xiang Yan-Wei Xiang Bin Li Bin Li Yue Luo Le Kuai Le Kuai Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm Frontiers in Genetics pyroptosis pyroptosis-related genes psoriasis random forest algorithm machine learning |
| 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 | pyroptosis pyroptosis-related genes psoriasis random forest algorithm machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.850108/full |
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