| 摘要: |
| 目的 筛选多发性骨髓瘤（MM）患者与正常人群之间的差异表达基因（DEGs），探究MM的发病机制，为MM的基因诊断和治疗提供导向。方法 从GEO数据库中检索获取MM患者的芯片数据，通过Morpheus在线工具进行芯片数据质量控制和DEGs的筛选，运用DAVID数据库对筛选获得的DEGs行基因富集和通路分析，通过STRING数据库构建蛋白相互作用网络，并采用Cytoscape软件行模块分析。结果 共获得16 211个DEGs，包括7 586个上调基因和8 625个下调基因（P<0.05）。基因本体（GO）分析结果表明，生物学过程中上调DEGs主要涉及鞘糖脂代谢等30个功能簇，下调DEGs涉及细胞分裂等163个功能簇；分子功能中上调DEGs主要涉及蛋白质结合等29个功能簇，下调DEGs主要涉及组蛋白结合等59个功能簇；细胞成分中上调DEGs主要集中在细胞溶质等27个功能簇中，而下调DEGs主要集中在核质等78个功能簇中。京都基因与基因组百科全书（KEGG）分析结果表明，上调DEGs主要涉及溶酶体相关通路等26条通路，下调DEGs主要涉及DNA复制等27条通路。蛋白互作网络分析示CDK1、TOP2A、AURKB、BRCA1、CHEK1、PTEN、RAD51、GMPS、CDC45和CDKN2A 10个基因为富集程度最高的核心DEGs，模块分析显示得分最高的3个基因模块主要与核分裂、DNA复制和核酸代谢过程相关。结论 通过多种生物信息学方法筛选获得了MM患者和健康对照组的DEGs，并从不同角度阐释了MM发病机制的相关基因及其表达特征，为MM特异性诊断标志和靶向治疗等提供了依据。 |
| 关键词: 多发性骨髓瘤 差异表达基因 高通量基因芯片 生物信息学 |
| DOI：10.16781/j.0258-879x.2017.07.0877 |
| 投稿时间：2017-02-25修订日期：2017-06-26 |
| 基金项目:无 |
|
| Excavation of multiple myeloma associated differential genes based on high-throughput microarray and bioinformatics |
| WU Sui-yi1,ZHOU Xiao-yi2,WANG Fei2,YANG Yi-lin2,WEI Xian-zhao2* |
(1. 3rd Students Team, Faculty of Naval Medicine, Second Military Medical University, Shanghai 200433, China;
2. Department of Spine Surgery, Changhai Hospital, Second Military Medical University, Shanghai 200433, China
*Corresponding author) |
| Abstract: |
| Objective To screen the differentially expressed genes (DEGs) between multiple myeloma (MM) patients and the healthy controls, to explore the pathogenesis of MM, and to provide a theoretical basis for gene diagnosis and gene therapy. Methods Gene expression profiles of MM patients were obtained from GEO database. Morpheus Online (https://software.broadinstitute.org/morpheus/) was used to determine the chip-data quality control and DEGs screening; DAVID Online (https://david.ncifcrf.gov/) was used to perform the gene ontology and pathway enrichment analysis; STRING Online (https://string-db.org/) was used to integrate the protein-protein interaction (PPI) network, and Cytoscape was used to screen the modules of PPI. Results A total of 16 211 DEGs (7 586 up-regulated genes and 8 625 down-regulated genes) were identified (P<0.05). The up-regulated DEGs enriched in biological process terms mainly involved 30 functional categories, like glycosphingolipid metabolic process, and the down-regulated mainly involved 163 functional categories, like cell division; the up-regulated DEGs enriched in molecular function terms mainly involved 29 functional categories, like protein binding, and the down-regulated mainly involved 59 functional categories, like histone binding; the up-regulated DEGs enriched in cellular component terms mainly involved 27 functional categories, like cytosol, and the down-regulated mainly involved 78 functional categories, like nucleoplasm. The KEGG analysis showed that the up-regulated DEGs mainly involved 26 pathways, like lysosome related pathway, and the down-regulated were mainly related to 27 pathways, like DNA replication. Ten genes, including CDK1, TOP2A, AURKB, BRCA1, CHEK1, PTEN, RAD51, GMPS, CDC45 and CDKN2A, were the hub DEGs with highest enrichment. Module analysis showed that the three most significant DEGs were mainly related to nuclear division, DNA replication and nucleic acid metabolism process. Conclusion A series of DEGs between MM patients and the healthy controls have been screened with a variety of bioinformatics methods, and gene and expression features of MM pathogenesis have been explained in various perspectives, which may provide the basis for targeted diagnosis therapy of MM. |
| Key words: multiple myeloma differentially expressed genes high-throughput microarray bioinformatics |
