Evidence mapPaperPMID 42008507Full record

ArticlePloS one2026

Identification and clinical implications of immune-related hub genes in psoriasis.

Yuzhen Sun, Ziguang Zhou, Yu Mao, Niu Liu, Yanfeng Li, Weiyuan Fang

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Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yuzhen SunDepartment of Dermatology, The First People's Hospital of Zhengzhou, Zhengzhou, Henan, China.
Ziguang ZhouDepartment of Dermatology, The First People's Hospital of Zhengzhou, Zhengzhou, Henan, China.
Yu MaoDepartment of Dermatology, The First People's Hospital of Zhengzhou, Zhengzhou, Henan, China.
Niu LiuDepartment of Dermatology, The First People's Hospital of Zhengzhou, Zhengzhou, Henan, China.
Yanfeng LiDepartment of Dermatology, The First People's Hospital of Zhengzhou, Zhengzhou, Henan, China.
Weiyuan FangDepartment of Pulmonary and Critical Care Medicine, Huashan Hospital of Fudan University, Shanghai, China.ORCID https://orcid.org/0000-0002-0460-4093

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPsoriasis, a chronic inflammatory skin disease affecting 2-3% of the global population, is driven by dysregulated immune responses. Despite advancements in biologic therapies, treatment challenges persist due to high recurrence rates. This study aimed to identify immune-related hub genes and elucidate their clinical implications in psoriasis pathogenesis and therapy.

methodsMultiple microarray datasets from psoriasis patients (GSE30999, GSE106992, GSE14905, GSE78097, and GSE117468) were obtained to identify immune-key genes by differential gene analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Subsequently, immune-related hub genes were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and Protein-Protein Interaction (PPI) networks, with further validation through Gene Set Enrichment Analysis (GSEA) and Receiver Operating Characteristic (ROC) curves to assess exploratory within-sample discrimination. Pearson correlation analysis evaluated the relationship between hub genes, skin lesion severity, and treatment outcomes. The study also conducted immune infiltration by using the Cell-type Identification by Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm and identified potential therapeutic targets by the Drug-Gene Interaction Database (DGIdb).

resultsThirty-one immune-related key genes were identified, and six hub genes (CLEC7A, CXCL1, IRF1, S100A12, S100A8, S100A9) were validated as central players in immune signaling pathways. These genes exhibited within-sample discrimination (AUC > 0.9) and correlated with disease severity and biological therapy efficacy. Immune infiltration analysis revealed increased activated memory CD4+ T cells and M1 macrophages in lesional skin, which was strongly associated with hub gene expression. Additionally, drug-gene interaction analysis identified potential therapeutic agents targeting these genes.

conclusionThis study identified six immune-related hub genes that were closely linked to the severity of psoriasis, the effectiveness of biological treatments, and infiltrated activated memory CD4+ T cells and M1 macrophages. Our findings elucidate a novel immune-related hub gene network in psoriasis and provide potential targets for the development and application of biologics.

Indexed as

Gene Regulatory NetworksPsoriasisAlgorithmsGene Expression ProfilingGene Expression RegulationHumansInterferon Regulatory Factor-1Protein Interaction MapsInterferon Regulatory Factor-1IRF1 protein, human

Identifiers

PMID42008507
PMCPMC13095007

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.