Zhong Chi, Shi Ke, Li Peiting, Qiu Xiaohui, Wu Xianrui, Chen Shuyue, Liu Yang, Li Fuying, Zhao Zitong, Zhou Jianda, Liang Geao, Xu Dan
The Third Xiangya Hospital of Central South University, No.138, Tongzipo Road, YueLu District, Changsha 410013, Hunan Province, China.
The Third Xiangya Hospital of Central South University, No.138, Tongzipo Road, YueLu District, Changsha 410013, Hunan Province, China.
Burns. 2024 Dec;50(9):107255. doi: 10.1016/j.burns.2024.08.021. Epub 2024 Sep 3.
If not accurately diagnosed and treated, postburn pathological scars, such as keloids and hypertrophic scars, can lead to negative clinical outcomes. However, differential diagnosis at the molecular level for postburn pathological scars remains limited. Using single-cell sequencing analysis, we investigated the genetic nuances of pathological scars at the cellular level. This study aimed to identify molecular diagnostic biomarkers to distinguish between postburn keloids and hypertrophic scars.
Single-cell sequencing, differential expression, and weighted co-expression network analyses were performed to identify potential key genes for discriminating between keloids and hypertrophic scars. Postburn clinical samples were collected from our centre to validate the expression levels of the identified key genes.
Single-cell sequencing analysis unveiled 29 and 30 cell clusters in keloids and hypertrophic scars, respectively, predominantly composed of fibroblasts. Bulk differential gene analysis showed 96 highly expressed genes and 69 lowly expressed genes in keloids compared to hypertrophic scars. By incorporating previous research, Gene Set Enrichment Analysis was conducted to select fibroblasts as the focus of research. According to the single-cell data, 301 genes were stably expressed in fibroblasts from both types of pathological scars. Consistently, Weighted Gene Co-expression Network Analysis revealed that the blue module genes were mostly hub genes associated with fibroblasts. After intersecting fibroblast-related genes in single-cell data, Weighted Gene Co-expression Network Analysis-hub module genes, and bulk differential expression genes, insulin-like growth factor binding protein 6 and tumour necrosis factor alpha-induced protein 6 were identified as key genes to distinguish keloids from hypertrophic scars, resulting in diagnostic accuracies of 1.0 and 0.75, respectively. Immunohistochemical Staining and Quantitative Reverse Transcription PCR revealed that the expression levels of tumour necrosis factor alpha induced protein 6 and insulin-like growth factor binding protein 6 were significantly lower in postburn keloids than in hypertrophic scars- CONCLUSIONS: Tumour necrosis factor alpha induced protein 6 and insulin-like growth factor binding protein 6, exhibiting high diagnostic accuracy, provide valuable guidance for the differential diagnosis and treatment of postburn pathological scars.
如果烧伤后病理性瘢痕，如瘢痕疙瘩和增生性瘢痕，未得到准确诊断和治疗，可能会导致不良临床后果。然而，烧伤后病理性瘢痕在分子水平上的鉴别诊断仍然有限。我们通过单细胞测序分析，在细胞水平上研究了病理性瘢痕的基因细微差别。本研究旨在识别分子诊断生物标志物，以区分烧伤后瘢痕疙瘩和增生性瘢痕。
进行单细胞测序、差异表达分析和加权共表达网络分析，以确定区分瘢痕疙瘩和增生性瘢痕的潜在关键基因。从我们中心收集烧伤后临床样本，以验证所鉴定关键基因的表达水平。
单细胞测序分析分别在瘢痕疙瘩和增生性瘢痕中揭示了29个和30个细胞簇，主要由成纤维细胞组成。与增生性瘢痕相比，批量差异基因分析显示瘢痕疙瘩中有96个高表达基因和69个低表达基因。通过整合先前的研究，进行基因集富集分析以选择成纤维细胞作为研究重点。根据单细胞数据，301个基因在两种病理性瘢痕的成纤维细胞中稳定表达。同样，加权基因共表达网络分析表明蓝色模块基因大多是与成纤维细胞相关的枢纽基因。在将单细胞数据中的成纤维细胞相关基因、加权基因共表达网络分析枢纽模块基因和批量差异表达基因进行交叉分析后，胰岛素样生长因子结合蛋白6和肿瘤坏死因子α诱导蛋白6被确定为区分瘢痕疙瘩和增生性瘢痕的关键基因，诊断准确率分别为1.0和0.75。免疫组织化学染色和定量逆转录PCR显示，烧伤后瘢痕疙瘩中肿瘤坏死因子α诱导蛋白6和胰岛素样生长因子结合蛋白6的表达水平明显低于增生性瘢痕。
肿瘤坏死因子α诱导蛋白6和胰岛素样生长因子结合蛋白6具有较高的诊断准确性，为烧伤后病理性瘢痕的鉴别诊断和治疗提供了有价值的指导。
