Evidence map›Paper›PMID 41229415›Full record

ArticleFrontiers in immunology2025

Immune-related inflammatory gene in hypertrophic scar: prognostic and molecular mechanisms via integrated machine learning-WGCNA analysis.

Yufang Liu, Huiling Li, Chunfeng Zhao, Wang Xin

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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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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

4 authors.

Yufang LiuDepartment of Burn and Wound Repair, Weifang People's Hospital, Weifang, Shandong, China.
Huiling LiDepartment of Burn and Wound Repair, Weifang People's Hospital, Weifang, Shandong, China.
Chunfeng ZhaoDepartment of Pediatric Surgery, Weifang People's Hospital, Weifang, Shandong, China.
Wang XinDepartment of Pediatric Surgery, Weifang People's Hospital, Weifang, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: 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. Methods: 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. Results: 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. Discussion: 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.

Indexed as

Cicatrix, HypertrophicInflammationMachine LearningCollagen Type ICollagen Type I, alpha 1 ChainComputational BiologyDatabases, GeneticGene Expression ProfilingGene Regulatory NetworksHumansNomogramsPrognosisTissue Inhibitor of Metalloproteinase-1TranscriptomeCOL1A1 protein, humanCOL1A2 protein, humanCollagen Type ICollagen Type I, alpha 1 ChainTIMP1 protein, humanTissue Inhibitor of Metalloproteinase-1hypertrophic scarimmune cell infiltrationimmune-related inflammatory genemachine learningsignature genes and modevalidation analysis

Identifiers

PMID41229415
PMCPMC12602242

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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 OpenQuestion graph.