Evidence map›Paper›PMID 41333466›Full record

ArticleFrontiers in immunology2025

CHN1 as a potential predictive genetic biomarker for atopic dermatitis-related depression.

Yifei Wang, Yuqing Liu, Miao Chen, Danping Liu, Chen Shen

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Article in Frontiers in immunology, 2025. 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

5 authors.

Yifei Wang *Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yuqing Liu *Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Miao ChenDepartment of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Danping LiuDepartment of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Chen ShenDepartment of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The comorbidity of atopic dermatitis (AD) and depression has garnered increased attention in recent years, yet the immunopathological mechanisms underlying this connection remain unclear. To bridge this gap, the study aimed to uncover the immune regulatory networks and identify key genetic markers involved in the comorbidity of depression in AD. Methods: We performed RNA sequencing on peripheral blood mononuclear cells (PBMCs) collected from 20 AD patients with and without depression. By integrating bioinformatics analyses with machine learning, we conducted weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and employed machine learning models of least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE). Additionally, validation was carried out in an independent cohort of 20 participants to confirm the expression of the identified potential pivotal gene. Results: A total of 394 differentially expressed genes (DEGs) were identified in AD patients with depression as compared to those non-depressed counterparts. Weighted gene co-expression network analysis (WGCNA) pinpointed a pink module encompassing 83 genes strongly linked to depressive symptoms. Functional enrichment analysis highlighted biological processes related to neurotransmitter uptake and the negative regulation of T-helper (Th) 17 cell differentiation. Furthermore, machine learning models of least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) consistently identified CHN1 as a potential pivotal gene upregulated in AD patients with depression. The expression level of CHN1 demonstrated positive correlation with Th2 and Th17 cytokine signatures, as well as with the Hospital Anxiety and Depression Scale-Depression (HADS-D) score, and the Eczema Area and Severity Index (EASI). Validation in an independent cohort of 20 participants confirmed the significant upregulation of CHN1 in depressed AD patients. Discussion: Together, these findings reveal previously unrecognized immunoinflammatory axis underlying AD-associated depression, and shed light on CHN1 as a potential molecular bridge connecting peripheral inflammation and neuropsychiatric manifestations.

Indexed as

DepressionDermatitis, AtopicAdultBiomarkersComputational BiologyFemaleGene Expression ProfilingGene Regulatory NetworksGenetic MarkersHumansMachine LearningMaleMiddle AgedBiomarkersGenetic Markersatopic dermatitis (AD)CHN1comorbiditydepressiongeneneuro-immune mechanism

Identifiers

PMID41333466
PMCPMC12665567

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