AccScience Publishing / EJMO / Online First / DOI: 10.36922/EJMO025150098
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ORIGINAL RESEARCH ARTICLE

Identifying the roles of hub gene in keloid formation using single-cell transcriptomics

Xiangbing Zheng1† ,  Zhengqiang Wan2† ,  Bing Liu1 ,  Jun Yin3 ,  Cheng Chen1 ,  Yuzhen Ma4 ,  Yong Zou1*
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1 Department of Burns and Plastic Surgery, The Second People’s Hospital of Yibin, Sichuan, China
2 Department of Thoracic Surgery, The First People’s Hospital of Suining, Suining, Sichuan, China
3 Department of Haematology, The Second People’s Hospital of Yibin, Yibin, Sichuan, China
4 Department of Oral and Maxillofacial Surgery, The First People’s Hospital of Suining, Suining, Sichuan, China
†These authors contributed equally to this work.
EJMO, 025150098 https://doi.org/10.36922/EJMO025150098
Received: 8 April 2025 | Revised: 31 May 2025 | Accepted: 13 June 2025 | Published online: 3 July 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Introduction: Keloid, a fibroproliferative tumor characterized by excessive collagen deposition and fibroblast hyperplasia, lacks effective therapeutic strategies due to unclear molecular mechanisms. Objective: This study aims to elucidate keloid pathogenesis and identify diagnostic biomarkers through multi-omics integration. Methods: Single-cell RNA sequencing (ScRNA-seq) data (GSE163973) and bulk RNA sequencing datasets (GSE162904/GSE145725) were analyzed. Fibroblast subpopulations were identified using the Seurat R package, and cell–cell interactions were explored using the CellChat R package. Weighted gene co-expression network analysis (WGCNA) was employed to identify key gene modules in fibroblasts. Hub genes were screened using Lasso regression and validated through machine learning algorithms and a gene-immune convolutional neural network (CNN). Immune infiltration patterns were evaluated using the MCP-counter and Immuno-Oncology Biological Research R packages. Results: ScRNA-seq analysis revealed eight distinct cell subtypes within keloid tissues, with fibroblasts significantly enriched compared to normal skin. Fibroblast clusters 1 and 5 exhibited elevated midkine–low-density lipoprotein receptor-related protein 1-mediated interactions and enhanced differentiation activity. WGCNA identified three critical modules—“brown,” “cyan,” and “yellow”—linked to fibroblast activation. Lasso regression produced an eight-gene signature that effectively distinguished keloid from normal skin (area under the curve = 0.885 – 0.889). Nonnegative matrix factorization classified keloids into four subtypes, each with distinct immune infiltration profiles correlated with hub gene expression. The gene-immune CNN model achieved 100% sensitivity and 88.9% specificity in diagnostic classification. Conclusion: This study elucidates the molecular mechanisms underlying keloid formation through integrated single-cell and transcriptomic analysis, proposing an eight-gene signature as a potential diagnostic and therapeutic target. The identified keloid subtypes and associated immune infiltration patterns provide novel insights for advancing precision medicine approaches in keloid management.

Keywords
Cell communication
Deep learning
Differentiation trajectory
Immune infiltration
Keloid
Single-cell RNA
Funding
This study was funded by the Medical Youth Innovation Research Project Plan of Sichuan Province of the Sichuan Medical Association (S20091).
Conflict of interest
The authors declare no conflicts of interest.
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Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing
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