Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm

Jian-Kun Song; Ying Zhang; Xiao-Ya Fei; Yi-Ran Chen; Ying Luo; Jing-Si Jiang; Yi Ru; Yan-Wei Xiang; Bin Li; Yue Luo; Le Kuai

doi:10.3389/fgene.2022.850108

Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm

Front Genet. 2022 Aug 30:13:850108. doi: 10.3389/fgene.2022.850108. eCollection 2022.

Authors

Jian-Kun Song¹, Ying Zhang^{2

3}, Xiao-Ya Fei¹, Yi-Ran Chen^{2

3}, Ying Luo^{2

3}, Jing-Si Jiang^{2

3}, Yi Ru^{2

3}, Yan-Wei Xiang^{2

4}, Bin Li^{1

3}, Yue Luo¹, Le Kuai^{2

3}

Affiliations

¹ Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China.
² Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
³ Institute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China.
⁴ School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

Abstract

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.

Keywords: machine learning; psoriasis; pyroptosis; pyroptosis-related genes; random forest algorithm.