Identification of novel immune-related signatures for keloid diagnosis and treatment: insights from integrated bulk RNA-seq and scRNA-seq analysis

• Published in: Human genomics 2024-07, Vol.18 (1), p.80-17, Article 80
• Main Authors: Xiao, Kui, Wang, Sisi, Chen, Wenxin, Hu, Yiping, Chen, Ziang, Liu, Peng, Zhang, Jinli, Chen, Bin, Zhang, Zhi, Li, Xiaojian
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural network; Cell Differentiation - genetics; Diagnosis; Drug interaction; Fibroblast; Fibroblasts; Fibroblasts - immunology; Fibroblasts - metabolism; Fibroblasts - pathology; Gene expression; Gene Expression Profiling; Gene Regulatory Networks; Humans; Immune genes; Immunoglobulins; Immunosuppressive agents; Keloid; Keloid - diagnosis; Keloid - drug therapy; Keloid - genetics; Keloid - immunology; Keloid - pathology; Machine Learning; Microenvironments; Neural networks; Performance evaluation; Radiation therapy; Retinoic acid; Reverse transcription; RNA-Seq; ScRNA-seq; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Single-Cell Gene Expression Analysis; Skin diseases; Support vector machines; Transcriptome - genetics; Transcriptomes; Tretinoin - pharmacology; Tretinoin - therapeutic use; Western blotting
• ISSN: 1479-7364; 1473-9542; 1479-7364
• DOI: 10.1186/s40246-024-00647-z

Keloid is a disease characterized by proliferation of fibrous tissue after the healing of skin tissue, which seriously affects the daily life of patients. However, the clinical treatment of keloids still has limitations, that is, it is not effective in controlling keloids, resulting in a high recurrence rate. Thus, it is urgent to identify new signatures to improve the diagnosis and treatment of keloids. Bulk RNA seq and scRNA seq data were downloaded from the GEO database. First, we used WGCNA and MEGENA to co-identify keloid/immune-related DEGs. Subsequently, we used three machine learning algorithms (Randomforest, SVM-RFE, and LASSO) to identify hub immune-related genes of keloid (KHIGs) and investigated the heterogeneous expression of KHIGs during fibroblast subpopulation differentiation using scRNA-seq. Finally, we used HE and Masson staining, quantitative reverse transcription-PCR, western blotting, immunohistochemical, and Immunofluorescent assay to investigate the dysregulated expression and the mechanism of retinoic acid in keloids. In the present study, we identified PTGFR, RBP5, and LIF as KHIGs and validated their diagnostic performance. Subsequently, we constructed a novel artificial neural network molecular diagnostic model based on the transcriptome pattern of KHIGs, which is expected to break through the current dilemma faced by molecular diagnosis of keloids in the clinic. Meanwhile, the constructed IG score can also effectively predict keloid risk, which provides a new strategy for keloid prevention. Additionally, we observed that KHIGs were also heterogeneously expressed in the constructed differentiation trajectories of fibroblast subtypes, which may affect the differentiation of fibroblast subtypes and thus lead to dysregulation of the immune microenvironment in keloids. Finally, we found that retinoic acid may treat or alleviate keloids by inhibiting RBP5 to differentiate pro-inflammatory fibroblasts (PIF) to mesenchymal fibroblasts (MF), which further reduces collagen secretion. In summary, the present study provides novel immune signatures (PTGFR, RBP5, and LIF) for keloid diagnosis and treatment, and identifies retinoic acid as potential anti-keloid drugs. More importantly, we provide a new perspective for understanding the interactions between different fibroblast subtypes in keloids and the remodeling of their immune microenvironment.