Evidence map›Paper›PMID 42780662›Full record

ArticleFrontiers in immunology2026

Identification and experimental validation of CCL26 as a core comorbid gene and biomarker for atopic dermatitis and allergic asthma via machine learning and multi-omics analysis.

Yunfan Gu, Shilei Shi, Yuqing Rao, Shuang Zhang, Jiaqi Zhang, Weiming Zhang, Xianyu Zeng

Abstract read
In one paragraph

Article in Frontiers in immunology, 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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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yunfan GuSchool of Traditional Chinese Medicine, Hubei University of Chinese Medicine, Wuhan, China.
Shilei ShiFirst Clinical College, Hubei University of Chinese Medicine, Wuhan, China.
Yuqing RaoSchool of Traditional Chinese Medicine, Hubei University of Chinese Medicine, Wuhan, China.
Shuang ZhangFirst Clinical College, Hubei University of Chinese Medicine, Wuhan, China.
Jiaqi ZhangFirst Clinical College, Hubei University of Chinese Medicine, Wuhan, China.
Weiming ZhangDepartment of Dermatology, Traditional Chinese and Western Medicine Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Xianyu ZengSchool of Traditional Chinese Medicine, Hubei University of Chinese Medicine, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Atopic dermatitis (AD) and allergic asthma (AA) frequently co-occur and are a core component of the "atopic march." However, the shared molecular mechanisms linking these conditions, as well as the core comorbid genes with diagnostic value, remain poorly understood. Methods: This study integrated differential expression analysis and weighted gene co-expression network analysis (WGCNA) of bulk RNA sequencing data from AD and AA to identify comorbidity-associated genes. Diagnostic models were built using 8 feature selection algorithms and 175 machine-learning combinations, and then validated across multiple independent external cohorts. Single-cell transcriptomic data from AD and AA were integrated to identify pathogenic cell subclusters using the Scissor, scAB, and DEGAS algorithms and to examine the distribution of comorbid genes. Their functional mechanisms were clarified through virtual gene knockouts (Geneformer, CellOracle, and scTenifoldKnk) and cell-cell communication analyses (CellChat and MultiNicheNet), and were validated Results: Following differential expression analysis, WGCNA, and machine-learning-based feature selection, CCL26 showed consistent and significant upregulation across all cohorts. A diagnostic model based on CCL26 performed well in the external validation cohort. Single-cell analysis revealed that CCL26 is specifically enriched in pathogenic cell subsets, including inflammatory vascular smooth muscle cells and fibroblasts in AD skin, as well as basal- and secretory-lineage epithelial cells in AA airways. Virtual gene knockout revealed that CCL26 is involved in regulating tissue remodeling and inflammatory pathways. Cell-to-cell communication and spatial transcriptomic analyses further confirmed that CCL26 mediates communication between pathogenic cell subsets and Th2 cells via the CCR3 receptor, leading to the formation of self-amplifying inflammatory microenvironments in the skin and airways. Conclusion: This study identifies CCL26 as a core comorbid gene and a potential diagnostic biomarker linking AD and AA. Furthermore, it elucidates the molecular mechanism by which CCL26 drives skin-airway inflammation via the CCL26-CCR3 axis, offering a novel perspective on atopic comorbidities.

Indexed as

AsthmaChemokine CCL26Dermatitis, AtopicMachine LearningBiomarkersGene Expression ProfilingHumansMultiomicsTranscriptomeBiomarkersCCL26 protein, humanChemokine CCL26allergic asthmaatopic dermatitisCCL26chemotactic cytokinesmachine learningsingle-cell transcriptomicsspatial transcriptomics

Identifiers

PMID42780662
PMCPMC13597448

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