{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T11:10:22Z","timestamp":1788952222391,"version":"build-2803163510"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Atopic dermatitis (AD) and allergic asthma (AA) frequently co-occur and are a core component of the \u201catopic march.\u201d However, the shared molecular mechanisms linking these conditions, as well as the core comorbid genes with diagnostic value, remain poorly understood.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      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\n                      <jats:italic>in situ<\/jats:italic>\n                      using 10x Visium (AD) and Xenium (AA) spatial transcriptomics data. Finally, Candidate hub genes were validated\n                      <jats:italic>in vitro<\/jats:italic>\n                      using IL-4 and IL-13-stimulated tissue-specific cellular models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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.\n                      <jats:italic>In vitro<\/jats:italic>\n                      experiments confirmed that CCL26 expression was significantly elevated in IL-4 and IL-13-stimulated HDF and BEAS-2B cells.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1936742","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T10:16:52Z","timestamp":1788949012000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["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"],"prefix":"10.3389","volume":"17","author":[{"given":"Yunfan","family":"Gu","sequence":"first","affiliation":[{"name":"School of Traditional Chinese Medicine, Hubei University of Chinese Medicine","place":["Wuhan, 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