{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T05:24:06Z","timestamp":1761629046810,"version":"build-2065373602"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"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>Introduction<\/jats:title>\n                    <jats:p>This research aimed to explore key immune-related inflammatory genes and associated molecular mechanisms on hypertrophic scar (HTS), to provide new perspectives for disease prognosis and diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The gene expression profiles were obtained from the public GEO database. The immune-related inflammatory genes were identified based on DEGs from HTS vs. normal samples, immune-related genes explored by WGCNA, as well as inflammation-related genes from the database. Signature genes were screened using machine learning methods, followed by nomogram validation. Then, the immune infiltration, GSEA pathway analysis, target drug prediction and interaction analysis associated with signature genes were further investigated. Finally, validation analysis was performed using tissue samples from HTS patients to verify the expression of signature genes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 73 differentially expressed immune-related inflammatory genes were identified. Through three machine learning analysis approaches, four signature genes (COL1A1, A2M, TIMP1, and COL1A2) were identified, and they exhibited strong prognostic value in nomogram analysis. Immune infiltration and GSEA analysis revealed significant associations between these signature genes and Nature killer T cells, as well as the ECM receptor interaction pathway. Validation analysis via qRT-PCR and Western blot confirmed significant differential expression of all signature genes in HTS compared with normal skin tissues. Furthermore, transfection of HTS fibroblasts with si-COL1A1 not only reduced COL1A1 expression but also suppressed fibroblasts proliferation while promoting apoptosis, indicating that COL1A1 promotes proliferation and inhibits apoptosis in HTS fibroblasts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The immune-inflammation related genes COL1A1, A2M, TIMP1, and COL1A2 were identified as novel signature genes in HTS. The nomogram established based on these genes demonstrated high clinical diagnosis value. These findings provide evidence for early diagnosis and personalized therapeutic strategies in HTS management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2025.1645721","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T05:17:50Z","timestamp":1761628670000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Immune-related inflammatory gene in hypertrophic scar: prognostic and molecular mechanisms via integrated machine learning-WGCNA 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