Evidence map›Paper›PMID 42877539›Full record

ArticleFrontiers in molecular biosciences2026

Integrated bulk and single-cell transcriptomic analyses with experimental validation identify PLOD1 as a diagnostic biomarker and TGF-β1-responsive profibrotic mediator in dupuytren's disease.

Xianjie Lin, Mingming Wang, Yutian Lin, Yinshen Zhu, Zhijiang Ye, Haoran Chen

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Article in Frontiers in molecular biosciences, 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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5 · Who and what money

Authors and funding

6 authors.

Xianjie LinDepartment of Hand Surgery and Wound Repair Surgery, Wenzhou Medical College Affiliated Yueqing Hospital, Wenzhou, China.
Mingming WangDepartment of Hand Surgery and Wound Repair Surgery, Wenzhou Medical College Affiliated Yueqing Hospital, Wenzhou, China.
Yutian LinDepartment of Hand Surgery and Wound Repair Surgery, Wenzhou Medical College Affiliated Yueqing Hospital, Wenzhou, China.
Yinshen ZhuDepartment of Hand Surgery and Wound Repair Surgery, Wenzhou Medical College Affiliated Yueqing Hospital, Wenzhou, China.
Zhijiang YeDepartment of Hand Surgery and Wound Repair Surgery, Wenzhou Medical College Affiliated Yueqing Hospital, Wenzhou, China.
Haoran ChenDepartment of Hand Surgery and Wound Repair Surgery, Wenzhou Medical College Affiliated Yueqing Hospital, Wenzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Dupuytren's disease (DD) is a progressive fibroproliferative disease characterized by palmar fascia fibrosis, extracellular matrix deposition, and finger contractures. Reliable diagnostic biomarkers and regulatory mechanisms have not yet been fully defined. Methods: Firstly, integrate the public transcriptome datasets GSE59746 and GSE75152. Then, limma was used to identify differentially expressed genes (DEGs) and combined with Weighted Gene Co expression Network Analysis (WGCNA) to screen candidate genes related to DD. PPI network analysis, functional enrichment, and four machine learning algorithms (LASSO, Boruta, Random Forest, and SVM-RFE) were applied to identify key biomarkers. Use CIBERSORT to assess immune infiltration. Analyze the single-cell RNA sequencing dataset GSE173252 to determine cell localization and pseudo time series analysis. Virtual knockout analysis is used to predict downstream regulatory targets. Results: A total of 598 DEGs were identified, and the WGCNA brown module showed the strongest correlation with DD. Cross analysis identified 346 candidate genes, mainly enriched in extracellular matrix tissue, collagen containing matrix, and collagen trimer formation. Procollagen-lysine, 2-oxoglutarate 5-dioxygenase 1 (PLOD1) has been identified as the only overlapping biomarker among the four machine learning algorithms. PLOD1 is significantly upregulated in DD tissues and exhibits strong diagnostic performance, with an AUC of 0.908. Immunoassay showed a decrease in M0 macrophage infiltration, while there was a positive correlation between PLOD1 and M2 macrophages. Single cell analysis showed that PLOD1 was mainly enriched in chondrocyte related populations and increased along the late pseudotime trajectory. Virtual knockout indicates that FGF7 is a key downstream gene. TGF - β 1 significantly induces PLOD1 and pro fibrotic markers, while SB431542 inhibits these effects. Conclusion: PLOD1 is a promising biomarker and potential functional regulator for DD diagnosis, closely related to extracellular matrix remodeling, pro fibrotic signaling, and immune microenvironment changes.

Indexed as

dupuytren’s diseaseFGF7fibrosisM2 macrophagesmachine learningPLOD1single-cell RNA sequencing

Identifiers

PMID42877539
PMCPMC13662546

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