Department of Dermatology, Tai'an Central Hospital, Tai'an, Shandong, China.
Dermatology and Cosmetic Medicine Center, Weifang People's Hospital, Weifang, Shandong, China.
Skin Res Technol. 2024 Sep;30(9):e70024. doi: 10.1111/srt.70024.
This study aims to reveal the mechanism of fibroblast-related mitochondrial genes on keloid formation and explore promising signature genes for keloid diagnosis.
The distribution of fibroblasts between the keloid sample and control sample based on three keloid datasets, followed by the differentially expressed genes (DEGs) investigation and associated enrichment analysis. Then, hub genes were explored based on DEGs, mitochondrial genes from an online database, as well as fibroblast-related genes that were revealed by WCGNA. Subsequently, signature genes were screened through machine learning, and their diagnostic value was validated by nomogram. Moreover, the targeted drugs and related transcriptional regulation of these genes were analyzed. Finally, the verification analysis was performed on signature genes using qPCR analysis.
A total of totally 329 DEGs were revealed based on three datasets, followed by enrichment analysis. WGCNA revealed a total of 258 fibroblast-related genes, which were primarily assembled in functions like muscle tissue development. By using machine learning, we screened four signature genes (ACSF2, ALDH1B1, OCIAD2, and SIRT4) based on eight hub genes (fibroblast-related mitochondrial genes). Nomogram and validation analyses confirmed the well-diagnostic performance of these four genes in keloid. Immune infiltration and drug correlation analyses showed that SIRT4 was significantly associated with immune cell type 2 T helper cells and molecular drug cyclosporin. All these findings provided new perspectives for the clinical diagnosis and therapy of keloid.
The fibroblast-related mitochondrial genes including SIRT4, OCIAD2, ALDH1B1, and ACSF2 were novel signature genes for keloid diagnosis, offering novel targets and strategies for diagnosis and therapy of keloid.
本研究旨在揭示成纤维细胞相关的线粒体基因在瘢痕疙瘩形成中的作用机制，并探索瘢痕疙瘩诊断的潜在特征基因。
基于三个瘢痕疙瘩数据集，分析基于三个瘢痕疙瘩数据集的样本中瘢痕疙瘩和成纤维细胞的分布，然后进行差异表达基因（DEGs）的调查和相关的富集分析。然后，根据 DEGs、来自在线数据库的线粒体基因以及 WGCNA 揭示的成纤维细胞相关基因，探索关键基因。随后，通过机器学习筛选特征基因，并通过列线图验证其诊断价值。此外，还分析了这些基因的靶向药物和相关转录调控。最后，通过 qPCR 分析对特征基因进行验证分析。
基于三个数据集共发现了 329 个 DEGs，随后进行了富集分析。WGCNA 共揭示了 258 个成纤维细胞相关基因，主要与肌肉组织发育等功能有关。通过机器学习，我们从 8 个关键基因（成纤维细胞相关的线粒体基因）中筛选出了 4 个特征基因（ACSF2、ALDH1B1、OCIAD2 和 SIRT4）。列线图和验证分析证实了这四个基因在瘢痕疙瘩诊断中的良好性能。免疫浸润和药物相关性分析表明，SIRT4 与免疫细胞类型 2 T 辅助细胞和分子药物环孢素显著相关。所有这些发现为瘢痕疙瘩的临床诊断和治疗提供了新的视角。
包括 SIRT4、OCIAD2、ALDH1B1 和 ACSF2 在内的成纤维细胞相关线粒体基因是瘢痕疙瘩诊断的新特征基因，为瘢痕疙瘩的诊断和治疗提供了新的靶点和策略。
