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Peer-reviewedCC BY 4.0Frontiers in Immunology

Identification of psoriasis-associated hub genes by integrated transcriptomics and machine learning

  • Yaqi Zhao1,2,3,4
  • Xiao Liang1,2,3,4
  • Mingwei Tang1,2,3,4
  • Lin Qiao1,2,3,4
  • Wei Bai1,2,3,4
  • Can Huang1,2,3,4
  • Mengtao Li1,2,3,4
  • Xiaofeng Zeng1,2,3,4
  • Xiaomei Leng1,2,3,4
4 affiliations
  1. 1Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College
  2. 2National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology
  3. 3State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital
  4. 4Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education
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Abstract

This study aims to systematically identify key hub genes in psoriasis pathogenesis through the integration of bioinformatics, machine learning and experimental validation methods. Four psoriasis microarray datasets were retrieved from the GEO database, with differentially expressed genes identified through comparative analysis. A dual machine learning approach screened core hub genes, whose expression levels and diagnostic efficacy were validated in an independent cohort. CIBERSORT was employed for immune cell infiltration analysis, whilst a multi-database approach constructed an mRNA-miRNA-lncRNA regulatory network. Molecular docking technology assessed interactions between the environmental pollutant bisphenol A, predicted drugs, and hub genes. Finally, an in vitro psoriasis model was established by stimulating HaCaT cells with the M5 cytokine mixture, with hub gene expression validated via RT-qPCR. Results show that 77 co-differentially expressed genes were identified in psoriasis. Machine learning screened five key hub genes: CARHSP1, HPSE, PCLAF, SERPINB4, and SPRR2C. These were significantly upregulated in the validation cohort and demonstrated strong discriminative value for distinguishing psoriatic lesional skin from paired non-lesional skin, rather than serving as standalone diagnostic biomarkers for psoriasis. Immune cell infiltration analysis revealed extensive associations with immune subpopulations including T cells and macrophages. Molecular docking computationally predicted a potential binding interaction between the environmental pollutant BPA and the hub gene HPSE, which awaits future experimental validation. Concurrently, Genistein and HPSE were suggested a potential interaction that awaits functional validation. In vitro experiments confirmed significant upregulation of HPSE, PCLAF, and SPRR2C mRNA levels in M5-induced psoriasis cell models. HPSE emerged as a multifunctional core gene associated with immune dysregulation. Molecular docking additionally predicted a potential interaction between the environmental pollutant BPA and HPSE; however, this interaction remains a computational hypothesis requiring experimental validation.

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Cite

BibTeXRIS
BibTeX
@article{zhao2026identification,
  title = {{Identification of psoriasis-associated hub genes by integrated transcriptomics and machine learning}},
  author = {Yaqi Zhao and Xiao Liang and Mingwei Tang and Lin Qiao and Wei Bai and Can Huang and Mengtao Li and Xiaofeng Zeng and Xiaomei Leng},
  journal = {Frontiers in Immunology},
  year = {2026},
  month = oct,
  doi = {10.3389/fimmu.2026.1937515},
  url = {https://www.frontiersin.org/articles/10.3389/fimmu.2026.1937515/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Identification of psoriasis-associated hub genes by integrated transcriptomics and machine learning
AU  - Yaqi Zhao
AU  - Xiao Liang
AU  - Mingwei Tang
AU  - Lin Qiao
AU  - Wei Bai
AU  - Can Huang
AU  - Mengtao Li
AU  - Xiaofeng Zeng
AU  - Xiaomei Leng
PY  - 2026
DA  - 2026/10/05
JO  - Frontiers in Immunology
DO  - 10.3389/fimmu.2026.1937515
UR  - https://www.frontiersin.org/articles/10.3389/fimmu.2026.1937515/full
AB  - This study aims to systematically identify key hub genes in psoriasis pathogenesis through the integration of bioinformatics, machine learning and experimental validation methods. Four psoriasis microarray datasets were retrieved from the GEO database, with differentially expressed genes identified through comparative analysis. A dual machine learning approach screened core hub genes, whose expression levels and diagnostic efficacy were validated in an independent cohort. CIBERSORT was employed for immune cell infiltration analysis, whilst a multi-database approach constructed an mRNA-miRNA-lncRNA regulatory network. Molecular docking technology assessed interactions between the environmental pollutant bisphenol A, predicted drugs, and hub genes. Finally, an in vitro psoriasis model was established by stimulating HaCaT cells with the M5 cytokine mixture, with hub gene expression validated via RT-qPCR. Results show that 77 co-differentially expressed genes were identified in psoriasis. Machine learning screened five key hub genes: CARHSP1, HPSE, PCLAF, SERPINB4, and SPRR2C. These were significantly upregulated in the validation cohort and demonstrated strong discriminative value for distinguishing psoriatic lesional skin from paired non-lesional skin, rather than serving as standalone diagnostic biomarkers for psoriasis. Immune cell infiltration analysis revealed extensive associations with immune subpopulations including T cells and macrophages. Molecular docking computationally predicted a potential binding interaction between the environmental pollutant BPA and the hub gene HPSE, which awaits future experimental validation. Concurrently, Genistein and HPSE were suggested a potential interaction that awaits functional validation. In vitro experiments confirmed significant upregulation of HPSE, PCLAF, and SPRR2C mRNA levels in M5-induced psoriasis cell models. HPSE emerged as a multifunctional core gene associated with immune dysregulation. Molecular docking additionally predicted a potential interaction between the environmental pollutant BPA and HPSE; however, this interaction remains a computational hypothesis requiring experimental validation.
SN  - 1664-3224
ER  -