Evidence map›Paper›PMID 40928556›Full record

ArticleFunctional & integrative genomics2025

Integrative analysis identifies FERMT3 as a key regulator of metabolic reprogramming in keloid scarring and metabolic syndrome.

Qian Lin, Beichen Cai, Feng Dong, Ruonan Ke, Xiuying Shan, Xuejun Ni, Lu Chen, Chuanshu Cai, Biao Wang

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Article in Functional & integrative genomics, 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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5 · Who and what money

Authors and funding

9 authors.

Qian Lin *Department of Plastic Surgery, the First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China.
Beichen Cai *Department of Plastic Surgery, the First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China.
Feng Dong *Department of Radiotherapy, Cancer Center, the First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China.
Ruonan KeDepartment of Plastic Surgery, the First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China.
Xiuying ShanDepartment of Plastic Surgery, the First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China.
Xuejun NiDepartment of Plastic Surgery, the First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China.
Lu ChenDepartment of Plastic Surgery, the First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China.
Chuanshu CaiDepartment of Radiotherapy, Cancer Center, the First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China. 857269804@qq.com.
Biao WangDepartment of Plastic Surgery, the First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China. biaowang@fjmu.edu.cn.

Funding

Fujian Provincial Health Technology Project 2024QNA025National Natural Science Foundation of China 82472576Natural Science Fundation of Fujian Province 2024J08167
6 · The paper itself

Abstract

backgroundKeloid scarring and Metabolic Syndrome (MS) are distinct conditions marked by chronic inflammation and tissue dysregulation, suggesting shared pathogenic mechanisms. Identifying common regulatory genes could unveil novel therapeutic targets. Methods. We performed an integrative analysis of public microarray datasets from keloid, MS, and respective healthy control tissues. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify shared gene modules. A diagnostic gene signature was developed using LASSO regression and machine learning, and validated on independent datasets. Single-cell RNA sequencing (scRNA-seq) data were analyzed to localize gene expression to specific cell types. The function of a top candidate gene, FERMT3, was investigated via in vitro experiments in macrophages and fibroblasts. Results. We identified 2,788 differentially expressed genes (DEGs) in keloids and 2,639 in MS compared to healthy controls, with 146 genes overlapping. WGCNA identified a key co-expression module (termed the "salmon" module) significantly associated with both conditions and enriched in metabolic and immune pathways. A 23-gene signature demonstrated fair to good predictive performance for both keloids (validation AUC = 0.783) and MS (AUC = 0.905). scRNA-seq analysis revealed that FERMT3 was highly expressed in macrophages and fibroblasts in keloid tissue. In vitro, modulation of FERMT3 in these cell types significantly altered their metabolic profiles (glycolysis, oxidative phosphorylation), inflammatory cytokine production, proliferation, and migration. Conclusions. Our integrative analysis identifies a shared transcriptomic signature between keloids and MS and highlights FERMT3 as a key potential regulator of the metabolic and inflammatory phenotypes in these conditions. These findings suggest that FERMT3 could be a promising therapeutic target for diseases driven by fibro-metabolic dysregulation.

Indexed as

KeloidMembrane ProteinsMetabolic SyndromeFibroblastsGene Regulatory NetworksHumansMacrophagesMetabolic ReprogrammingTranscriptomeMembrane ProteinsFERMT3FibroblastsKeloidsMacrophagesMetabolic reprogrammingMetabolic syndrome

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