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    董姝颖, 芦桂青. 基于公共数据库挖掘并分析雄激素性脱发的关键基因[J]. 蚌埠医科大学学报, 2024, 49(6): 763-766. DOI: 10.13898/j.cnki.issn.1000-2200.2024.06.014
    引用本文: 董姝颖, 芦桂青. 基于公共数据库挖掘并分析雄激素性脱发的关键基因[J]. 蚌埠医科大学学报, 2024, 49(6): 763-766. DOI: 10.13898/j.cnki.issn.1000-2200.2024.06.014
    shu
    DONG Shuying, LU Guiqing. Mining and analyzing key genes of androgenic alopecia based on public database[J]. Journal of Bengbu Medical University, 2024, 49(6): 763-766. DOI: 10.13898/j.cnki.issn.1000-2200.2024.06.014
    Citation: DONG Shuying, LU Guiqing. Mining and analyzing key genes of androgenic alopecia based on public database[J]. Journal of Bengbu Medical University, 2024, 49(6): 763-766. DOI: 10.13898/j.cnki.issn.1000-2200.2024.06.014
    shu

    基于公共数据库挖掘并分析雄激素性脱发的关键基因

    Mining and analyzing key genes of androgenic alopecia based on public database

      摘要
    • 摘要:
      目的 利用生物信息学方法探讨雄激素性脱发(AGA)的潜在分子机制。
      方法 从美国国立生物技术信息中心公共数据平台的基因表达数据库中下载基因芯片数据集(GSE45512、GSE36169)，利用GEO2R在线分析工具对其进行分析并获得各自的差异基因(DEGs)，采用DAVID线上工具对差异基因进行GO、KEGG富集分析。利用STRING数据库对DEGs进行蛋白网络的构建，利用Cytoscape软件中的Cytohubba插件计算，并根据MCC评分由高到低排序，根据评分结果，选取排名前三的基因作为Hub基因，并在临床样本中进一步验证。
      结果 从2个芯片数据集中分别筛选出与AGA相关DEGs，并进一步取交集，得出共有的DEGs 29个，其中上调13个，下调16个。对29个DEGs进行GO富集分析，提示蛋白质加工、淀粉样蛋白代谢等生物学过程与疾病的发病相关。KEGG富集分析提示扩张型心肌病、阿尔兹海默病等相关的通路与其相关。利用STRING数据库构建蛋白网络，结果上传Cytoscape软件，根据MCC评分筛选出3个评分最高的关键基因MYH11、TPM4、SORBS1作为Hub基因。Hub基因在临床样本中验证显示，与健康对照组相比，MYH11在AGA病人中表达明显下调(P < 0.01)，TPM4、SORBS1表达均明显上调(P < 0.01)。
      结论 利用生物信息学方法筛选AGA关键基因TPM4、SORBS1、MYH11，对探寻AGA的发病机制和治疗方案具有潜在价值。

       

      Abstract:
      Objective To explore the potential molecular mechanism of androgenetic alopecia (AGA) by using bioinformatics methods.
      Methods The gene chip data sets (GSE45512, GSE36169) were downloaded from the gene expression database of the public data platform of the National Center for Biotechnology Information, and the GEO2R online analysis tool was used to analyze them and obtain their respective differential expressed genes (DEGs). GO and KEGG enrichment analysis of differential genes were performed using DAVID online tool. The protein network of DEGs was constructed using STRING database, calculated by Cytohubba plug-in in Cytoscape software, and ranked from high to low according to MCC score. According to the score results, the top three genes were selected as Hub genes and further verified in clinical samples.
      Results The DEGs related to AGA were selected from the two chip data sets and further intersected. A total of 29 DEGs were obtained, of which 13 were up-regulated and 16 were down-regulated. GO enrichment analysis of 29 DEGs indicated that biological processes such as protein processing and amyloid metabolism were related to the pathogenesis of the disease. KEGG enrichment analysis suggested that dilated cardiomyopathy, Alzheimer's disease and other related pathways were associated with it. The protein network was constructed using STRING database. As a result, Cytoscape software was uploaded to screen out three key genes with the highest scores: MYH11, TPM4 and SORBS1 according to MCC scores, which were taken as Hub genes. The subsequent validation of Hub gene in clinical samples suggested that compared with healthy control group, the expression of MYH11 in patients was significantly down-regulated (P < 0.01), while the expression of TPM4 and SORBS1 in patients was significantly up-regulated (P < 0.01).
      Conclusions The key genes TPM4, SORBS1 and MYH11 in AGA are screened by bioinformatics method, which brings new ideas for the pathogenesis of AGA and has potential value for exploring new treatment schemes.

       

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