Department of Burns and Plastic and Cosmetic Surgery, Xi' an Ninth Hospital, Xi'an, Shaanxi Province, 710054, China.
Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, 266003, China.
J Burn Care Res. 2024 Sep 6;45(5):1217-1231. doi: 10.1093/jbcr/irae018.
The aim of this study was to investigate the correlation between cuproptosis-related genes and immunoinfiltration in keloid, develop a predictive model for keloid occurrence, and explore potential therapeutic drugs. The microarray datasets (GSE7890 and GSE145725) were obtained from Gene Expression Omnibus database to identify the differentially expressed genes (DEGs) between keloid and nonkeloid samples. Key genes were identified through immunoinfiltration analysis and DEGs and then analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, followed by the identification of protein-protein interaction networks, transcription factors, and miRNAs associated with key genes. Additionally, a logistic regression analysis was performed to develop a predictive model for keloid occurrence, and potential candidate drugs for keloid treatment were identified. Three key genes (FDX1, PDHB, and DBT) were identified, showing involvement in acetyl-CoA biosynthesis, mitochondrial matrix, oxidoreductase activity, and the tricarboxylic acid cycle. Immune infiltration analysis suggested the involvement of B cells, Th1 cells, dendritic cells, T helper cells, antigen-presenting cell coinhibition, and T cell coinhibition in keloid. These genes were used to develop a logistic regression-based nomogram for predicting keloid occurrence with an area under the curve of 0.859 and good calibration. We identified 32 potential drug molecules and extracted the top 10 compounds based on their P-values, showing promise in targeting key genes and potentially effective against keloid. Our study identified some genes in keloid pathogenesis and potential therapeutic drugs. The predictive model enhances early diagnosis and management. Further research is needed to validate and explore clinical implications.
本研究旨在探讨卷曲相关基因与瘢痕疙瘩免疫浸润之间的相关性，构建瘢痕疙瘩发生的预测模型，并探索潜在的治疗药物。从基因表达综合数据库中获取微阵列数据集（GSE7890 和 GSE145725），以鉴定瘢痕疙瘩和非瘢痕疙瘩样本之间的差异表达基因（DEGs）。通过免疫浸润分析和 DEGs 鉴定关键基因，然后进行基因本体论和京都基因与基因组百科全书分析，接着鉴定与关键基因相关的蛋白质-蛋白质相互作用网络、转录因子和 miRNA。此外，还进行了逻辑回归分析，以构建瘢痕疙瘩发生的预测模型，并鉴定瘢痕疙瘩治疗的潜在候选药物。鉴定出三个关键基因（FDX1、PDHB 和 DBT），它们参与乙酰辅酶 A 生物合成、线粒体基质、氧化还原酶活性和三羧酸循环。免疫浸润分析表明 B 细胞、Th1 细胞、树突状细胞、T 辅助细胞、抗原呈递细胞抑制和 T 细胞抑制参与了瘢痕疙瘩的发生。这些基因被用于开发基于逻辑回归的预测瘢痕疙瘩发生的列线图，曲线下面积为 0.859，具有良好的校准度。我们鉴定了 32 种潜在的药物分子，并根据 P 值提取了前 10 种化合物，这些化合物有望靶向关键基因，并可能对瘢痕疙瘩有效。本研究鉴定了瘢痕疙瘩发病机制中的一些基因和潜在的治疗药物。预测模型有助于早期诊断和管理。需要进一步研究来验证和探索临床意义。
