Integrated Single-Cell Transcriptomics, Multi-Omics, and Mendelian Randomization Reveal Ferroptosis-Related Molecular Subtypes and a Genetically Supported Causal Candidate in Psoriasis.

@article{xu2026integrated,
  title = {{Integrated Single-Cell Transcriptomics, Multi-Omics, and Mendelian Randomization Reveal Ferroptosis-Related Molecular Subtypes and a Genetically Supported Causal Candidate in Psoriasis.}},
  author = {Mengyue Xu and Yongjie Zuo and Hongxia Liu and Huangchao Jia and Xuewei Liu},
  journal = {Clinical, cosmetic and investigational dermatology},
  year = {2026},
  month = oct,
  doi = {10.2147/ccid.s637932},
  url = {https://europepmc.org/article/MED/42948308},
  note = {CC BY-NC},
}

TY  - JOUR
TI  - Integrated Single-Cell Transcriptomics, Multi-Omics, and Mendelian Randomization Reveal Ferroptosis-Related Molecular Subtypes and a Genetically Supported Causal Candidate in Psoriasis.
AU  - Mengyue Xu
AU  - Yongjie Zuo
AU  - Hongxia Liu
AU  - Huangchao Jia
AU  - Xuewei Liu
PY  - 2026
DA  - 2026/10/23
JO  - Clinical, cosmetic and investigational dermatology
DO  - 10.2147/ccid.s637932
UR  - https://europepmc.org/article/MED/42948308
AB  - Background  Ferroptosis contributes to inflammatory skin diseases, but its role in psoriasis remains incompletely understood. We performed an integrated multi‑omics analysis combining single‑cell transcriptomics, bulk transcriptomics, machine learning, consensus clustering, and Mendelian randomization (MR) to characterize ferroptosis‑related molecular features, intercellular communication, immune microenvironment remodeling, molecular subtypes, and causal biomarkers in psoriasis.Methods  Single‑cell RNA‑seq data (GSE151177) were analyzed to quantify ferroptosis activity across cell populations and infer intercellular communication networks. Bulk transcriptomic datasets (GSE30999, GSE78097) were integrated for differential expression and machine‑learning feature selection with SHAP interpretation. Consensus clustering, immune infiltration, functional enrichment, and drug repurposing analyses were performed. Cis‑eQTLs from eQTLGen (n=31,684) served as instruments for two‑sample MR with psoriasis GWAS from FinnGen (13,667 cases, 395,092 controls).Results  Macrophages/monocytes and fibroblasts showed the highest ferroptosis scores. MIF signaling (MIF-CD74/CD44, MIF-CD74/CXCR4, MIF-ACKR3) dominated keratinocyte outgoing communication. Transcriptomic integration yielded 72 overlapping ferroptosis-related genes; machine learning identified eight feature genes (CD24, EFNB2, DDX39A, NDRG4, GPRC5A, S100A8, PRSS8, ITPKC). CD24 (AUC=0.979) and S100A8 (AUC=0.974) exhibited top diagnostic performance. Consensus clustering stratified patients into two subtypes (C1, C2); all eight genes were highly expressed in C1, while C2 showed upregulated phagocytic but downregulated innate immune pathways. Drug enrichment suggested MS-275, genistein, and β-adrenoceptor agonists as potential ferroptosis-modulating compounds. MR demonstrated that only PRSS8 eQTL had a nominally significant causal association with psoriasis risk (P < 0.05, β = 0.0073).Conclusion  Our multi‑omics study delineates the ferroptosis‑related landscape in psoriasis, identifies MIF signaling as a key keratinocyte communication axis, establishes a high‑performance diagnostic model with eight feature genes, defines two distinct immune‑metabolic subtypes, and provides genetic evidence supporting PRSS8 as a putative causal biomarker for psoriasis susceptibility, offering novel insights and potential ferroptosis‑based biomarkers and therapeutic targets for psoriasis.
SN  - 1178-7015
ER  -