Transcriptomic characterization of key psoriasis-associated genes based on single-cell RNA-seq and machine learning

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: Not reported in abstract; no log2 fold-change, odds ratio, or standardised effect size provided

• Primary Outcome: Identification of four psoriasis-associated key genes – DEFB4A, GJB2, SERPINB3, and SERPINB13 – upregulated in lesional skin, derived from a pipeline of 271 hub genes associated with psoriasis lesions and basal cells

• Nnt Or Sensitivity: Machine learning model performance metrics (AUC, sensitivity, specificity, F1-score) not reported; no NNT applicable to this discovery study design

• Confidence Interval: Not reported

Clinical Application

The identified genes are not currently measurable in routine clinical practice. DEFB4A, GJB2, SERPINB3, and SERPINB13 are not established clinical biomarkers. Proposed small-molecule compounds targeting these genes are at in silico stage only, with no reported Phase I–III clinical trial data. Translation to clinical practice would require extensive wet-laboratory validation, animal model studies, and clinical trials. In Australian practice, psoriasis management is guided by RACGP and Australasian College of Dermatologists frameworks. PBS-listed biologics targeting the IL-17/IL-23 axis (secukinumab, ixekizumab, guselkumab, risankizumab, ustekinumab) represent the current standard of care for moderate-to-severe disease. The TGA has not approved any therapeutic agent targeting DEFB4A, GJB2, SERPINB3, or SERPINB13. This study does not alter current Australian prescribing practice. Its relevance is confined to basic science and future translational research pipelines. Patients with plaque psoriasis; findings are exploratory and hypothesis-generating only. Not applicable to clinical decision-making in current form. Potentially relevant to future biomarker development or novel therapeutic target research in psoriasis.

Abstract

BACKGROUND: Psoriasis is a multifaceted skin and systemic disorder driven by a complex interplay of genetic, immunological, and environmental factors. Genetic predisposition plays a pivotal role, with the IL-17/IL-23 immune axis recognized as a central pathogenic pathway. Ongoing research, however, continues to uncover additional critical drivers, cytokines, intracellular signaling networks, and potential therapeutic targets. METHODS: Single-cell RNA sequencing (scRNA-seq) datasets comprising both psoriatic and healthy samples were obtained from the Gene Expression Omnibus (GEO). Cell-type proportions were estimated using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), and weighted gene co-expression network analysis (WGCNA) was applied to explore correlations between cell types and gene signatures. Machine learning algorithms were subsequently employed to identify four psoriasis-associated key genes: DEFB4A, GJB2, SERPINB3, and SERPINB13. Their expression was validated in bulk RNA-seq datasets. Using scRNA-seq data, we further investigated the lesional regulatory roles of these genes and their associated pathway alterations, and we proposed targeted therapeutic strategies. RESULTS: A series of algorithms identified 271 hub genes significantly associated with psoriasis lesions and basal cells. Machine learning analysis refined this set to four key genes in psoriasis: DEFB4A, GJB2, SERPINB3, and SERPINB13. CONCLUSIONS: These four psoriasis-associated driver genes were upregulated in lesional skin. We also screened small-molecule compounds targeting these genes, offering potential therapeutic strategies.

References

1. 1.Wang, W., Zhang, Q., & Xu, S. (2026). Transcriptomic characterization of key psoriasis-associated genes based on single-cell RNA-seq and machine learning. PLoS ONE. https://doi.org/10.1371/journal.pone.0352663