DataSheet6_Identification and Verification of Five Potential Biomarkers Related to Skin and Thermal Injury Using Weighted Gene Co-Expression Network Analysis.ZIP

Background: Burn injury is a life-threatening disease that does not have ideal biomarkers. Therefore, this study first applied weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening methods to identify pivotal genes and diagnostic biomarkers associated with the skin burn process.
Methods: After obtaining transcriptomic datasets of burn patient skin and normal skin from Gene Expression Omnibus (GEO) and performing differential analysis and functional enrichment, WGCNA was used to identify hub gene modules associated with burn skin processes in the burn patient peripheral blood sample dataset and determine the correlation between modules and clinical features. Enrichment analysis was performed to identify the functions and pathways of key module genes. Differential analysis, WGCNA, protein-protein interaction analysis, and enrichment analysis were utilized to screen for hub genes. Hub genes were validated in two other GEO datasets, tested by immunohistochemistry for hub gene expression in burn patients, and receiver operating characteristic curve analysis was performed. Finally, we constructed the specific drug activity, transcription factors, and microRNA regulatory network of the five hub genes.
Results: A total of 1,373 DEGs in GSE8056 were obtained, and the top 5 upregulated genes were S100A12, CXCL8, CXCL5, MMP3, and MMP1, whereas the top 5 downregulated genes were SCGB1D2, SCGB2A2, DCD, TSPAN8, and KRT25. DEGs were significantly enriched in the immunity, epidermal development, and skin development processes. In WGCNA, the yellow module was identified as the most closely associated module with tissue damage during the burn process, and the five hub genes (ANXA3, MCEMP1, MMP9, S100A12, and TCN1) were identified as the key genes for burn injury status, which consistently showed high expression in burn patient blood samples in the GSE37069 and GSE13902 datasets. Furthermore, we verified using immunohistochemistry that these five novel hub genes were also significantly elevated in burn patient skin. In addition, MCEMP1, MMP9, and S100A12 showed perfect diagnostic performance in the receiver operating characteristic analysis.
Conclusion: In conclusion, we analyzed the changes in genetic processes in the skin during burns and used them to identify five potential novel diagnostic markers in blood samples from burn patients, which are important for burn patient diagnosis. In particular, MCEMP1, MMP9, and S100A12 are three key blood biomarkers that can be used to identify skin damage in burn patients.

背景：烧伤是一种危及生命的疾病，目前尚无理想的生物标志物。为此，本研究率先采用加权基因共表达网络分析（WGCNA）与差异表达基因（DEG）筛选方法，挖掘与皮肤烧伤进程相关的关键基因及诊断生物标志物。
方法：本研究从基因表达汇编（GEO）数据库获取烧伤患者皮肤与正常皮肤的转录组数据集，完成差异分析与功能富集后，利用WGCNA在烧伤患者外周血样本数据集里筛选与烧伤皮肤进程相关的核心基因模块，并分析模块与临床特征的相关性。通过富集分析明确关键模块基因的功能与通路。综合采用差异分析、WGCNA、蛋白质相互作用分析及富集分析筛选核心基因。随后在另外两个GEO数据集内对核心基因进行验证，并通过免疫组化检测核心基因在烧伤患者组织中的表达水平，同时开展受试者工作特征曲线分析。最终构建了5个核心基因的特异性药物活性、转录因子及微小RNA（miRNA）调控网络。
结果：本研究从GSE8056数据集中共获得1373个差异表达基因，其中上调排名前五的基因为S100A12、CXCL8、CXCL5、MMP3及MMP1，下调排名前五的基因为SCGB1D2、SCGB2A2、DCD、TSPAN8及KRT25。差异表达基因显著富集于免疫应答、表皮发育及皮肤发育进程。在WGCNA分析中，黄色模块被鉴定为与烧伤过程中组织损伤关联性最强的模块；同时筛选得到的5个核心基因（ANXA3、MCEMP1、MMP9、S100A12及TCN1）被确定为反映烧伤损伤状态的关键基因，在GSE37069与GSE13902数据集的烧伤患者血液样本中均呈现高表达趋势。进一步通过免疫组化验证发现，这5个新型核心基因在烧伤患者皮肤组织中同样显著高表达。此外，在受试者工作特征曲线分析中，MCEMP1、MMP9及S100A12展现出极佳的诊断效能。
结论：综上，本研究解析了烧伤过程中皮肤的遗传进程变化，并以此从烧伤患者血液样本中鉴定出5个潜在新型诊断标志物，其对烧伤患者的临床诊断具有重要意义。其中，MCEMP1、MMP9及S100A12这3种关键血液生物标志物可用于识别烧伤患者的皮肤损伤情况。