Identification and Verification of Five Potential Biomarkers Related to Skin and Thermal Injury Using Weighted Gene Co-Expression Network Analysis.
Identification and Verification of Five Potential Biomarkers Related to Skin and Thermal Injury Using Weighted Gene Co-Expression Network Analysis.
复制标题
使用加权基因共表达网络分析鉴定和验证与皮肤和热损伤相关的五种潜在生物标志物
DOI：
10.3389/fgene.2021.781589
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发表时间：
2021
影响因子：
3.7
通讯作者：
Zhou S
中科院分区：
文献类型：
--
作者：
Yang R;Wang Z;Li J;Pi X;Wang X;Xu Y;Shi Y;Zhou S
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。因此，本研究首次应用加权基因共表达网络分析（WGCNA）和差异表达基因（DEG）筛选方法来鉴定与皮肤烧伤过程相关的关键基因和诊断生物标志物。研究方法：在从基因表达综合数据库（GEO）获得烧伤患者皮肤和正常皮肤的转录组数据集并进行差异分析和功能富集之后，使用WGCNA鉴定烧伤患者外周血样本数据集中与烧伤皮肤过程相关的中枢基因模块，并确定模块与临床特征之间的相关性。进行富集分析以鉴定关键模块基因的功能和途径。利用差异分析、WGCNA、蛋白质-蛋白质相互作用分析和富集分析来筛选枢纽基因。Hub基因在另外两个GEO数据集中进行验证，通过免疫组织化学检测烧伤患者中Hub基因的表达，并进行受试者工作特征曲线分析。最后，我们构建了5个hub基因的特异性药物活性、转录因子和microRNA调控网络。结果如下：在GSE 8056中共获得1,373个DEG，并且前5个上调基因是S100 A12、CXCL 8、CXCL 5、MMP 3和MMP 1，而前5个下调基因是SCGB 1D 2、SCGB 2A 2、DCD、TSPAN 8和KRT 25。DEG在免疫、表皮发育和皮肤发育过程中显著富集。在WGCNA中，黄色模块被确定为与烧伤过程中的组织损伤最密切相关的模块，五个枢纽基因（ANXA 3，MCEMP 1，MMP 9，S100 A12和TCN 1）被确定为烧伤状态的关键基因，这些基因在GSE 37069和GSE 13902数据集中的烧伤患者血液样本中始终显示出高表达。此外，我们使用免疫组织化学验证了这五个新的枢纽基因在烧伤患者皮肤中也显著升高。此外，MCEMP 1、MMP 9和S100 A12在受试者操作特征分析中显示出完美的诊断性能。结论：总之，我们分析了烧伤过程中皮肤遗传过程的变化，并利用它们在烧伤患者的血液样本中鉴定了五种潜在的新诊断标志物，这对烧伤患者的诊断很重要。特别是，MCEMP 1，MMP 9和S100 A12是三种关键的血液生物标志物，可用于识别烧伤患者的皮肤损伤。
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.
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DOI：
10.1136/injuryprev-2015-041616
发表时间：
2016-02
期刊：
Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention
影响因子：
--
作者：
Haagsma JA;Graetz N;Bolliger I;Naghavi M;Higashi H;Mullany EC;Abera SF;Abraham JP;Adofo K;Alsharif U;Ameh EA;Ammar W;Antonio CA;Barrero LH;Bekele T;Bose D;Brazinova A;Catalá-López F;Dandona L;Dandona R;Dargan PI;De Leo D;Degenhardt L;Derrett S;Dharmaratne SD;Driscoll TR;Duan L;Petrovich Ermakov S;Farzadfar F;Feigin VL;Franklin RC;Gabbe B;Gosselin RA;Hafezi-Nejad N;Hamadeh RR;Hijar M;Hu G;Jayaraman SP;Jiang G;Khader YS;Khan EA;Krishnaswami S;Kulkarni C;Lecky FE;Leung R;Lunevicius R;Lyons RA;Majdan M;Mason-Jones AJ;Matzopoulos R;Meaney PA;Mekonnen W;Miller TR;Mock CN;Norman RE;Orozco R;Polinder S;Pourmalek F;Rahimi-Movaghar V;Refaat A;Rojas-Rueda D;Roy N;Schwebel DC;Shaheen A;Shahraz S;Skirbekk V;Søreide K;Soshnikov S;Stein DJ;Sykes BL;Tabb KM;Temesgen AM;Tenkorang EY;Theadom AM;Tran BX;Vasankari TJ;Vavilala MS;Vlassov VV;Woldeyohannes SM;Yip P;Yonemoto N;Younis MZ;Yu C;Murray CJ;Vos T
通讯作者：
Vos T
WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
14.9
作者：
Ritchie ME;Phipson B;Wu D;Hu Y;Law CW;Shi W;Smyth GK
通讯作者：
Smyth GK
starBase v2.0：从大规模 CLIP-Seq 数据中解码 miRNA-ceRNA、miRNA-ncRNA 和蛋白质-RNA 相互作用网络
影响因子：
14.9
作者：
Li JH;Liu S;Zhou H;Qu LH;Yang JH
通讯作者：
Yang JH
影响因子：
14.9
作者：
Karagkouni D;Paraskevopoulou MD;Chatzopoulos S;Vlachos IS;Tastsoglou S;Kanellos I;Papadimitriou D;Kavakiotis I;Maniou S;Skoufos G;Vergoulis T;Dalamagas T;Hatzigeorgiou AG
通讯作者：
Hatzigeorgiou AG