卵巢癌中枢纽基因和治疗药物的筛选的综合生物信息学分析。

BACKGROUND: Ovarian cancer (OC) ranks fifth as a cause of gynecological cancer-associated death globally. Until now, the molecular mechanisms underlying the tumorigenesis and prognosis of OC have not been fully understood. This study aims to identify hub genes and therapeutic drugs involved in OC. METHODS: Four gene expression profiles (GSE54388, GSE69428, GSE36668, and GSE40595) were downloaded from the Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) in OC tissues and normal tissues with an adjusted P-value < 0.05 and a |log fold change (FC)| > 1.0 were first identified by GEO2R and FunRich software. Next, Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses were performed for functional enrichment analysis of these DEGs. Then, the hub genes were identified by the cytoHubba plugin and the other bioinformatics approaches including protein-protein interaction (PPI) network analysis, module analysis, survival analysis, and miRNA-hub gene network construction was also performed. Finally, the GEPIA2 and DGIdb databases were utilized to verify the expression levels of hub genes and to select the candidate drugs for OC, respectively. RESULTS: A total of 171 DEGs were identified, including 114 upregulated and 57 downregulated DEGs. The results of the GO analysis indicated that the upregulated DEGs were mainly involved in cell division, nucleus, and protein binding, whereas the biological functions showing enrichment in the downregulated DEGs were mainly negative regulation of transcription from RNA polymerase II promoter, protein complex and apicolateral plasma membrane, and glycosaminoglycan binding. As for the KEGG-pathway, the upregulated DEGs were mainly associated with metabolic pathways, biosynthesis of antibiotics, biosynthesis of amino acids, cell cycle, and HTLV-I infection. Additionally, 10 hub genes (KIF4A, CDC20, CCNB2, TOP2A, RRM2, TYMS, KIF11, BIRC5, BUB1B, and FOXM1) were identified and survival analysis of these hub genes showed that OC patients with the high-expression of CCNB2, TYMS, KIF11, KIF4A, BIRC5, BUB1B, FOXM1, and CDC20 were statistically more likely to have poorer progression free survival. Meanwhile, the expression levels of the hub genes based on GEPIA2 were in accordance with those based on GEO. Finally, DGIdb database was used to identify 62 small molecules as the potentially targeted drugs for OC treatment. CONCLUSIONS: In summary, the data may produce new insights regarding OC pathogenesis and treatment. Hub genes and candidate drugs may improve individualized diagnosis and therapy for OC in future.

背景：卵巢癌（OC）在全球妇科癌症相关死亡原因中排名第五。迄今为止，OC 的肿瘤发生和预后的分子机制尚未完全阐明。本研究旨在鉴定 OC 中涉及的枢纽基因和治疗药物。

方法：从基因表达综合数据库（GEO）中下载了四个基因表达谱（GSE54388、GSE69428、GSE36668 和 GSE40595），通过 GEO2R 和 FunRich 软件首先鉴定 OC 组织和正常组织中差异表达基因（DEGs），其调整后 P 值 < 0.05，|log 倍变化（FC）| > 1.0。接下来，对这些 DEGs 进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析，以进行功能富集分析。然后，通过 cytoHubba 插件和其他生物信息学方法（包括蛋白质-蛋白质相互作用（PPI）网络分析、模块分析、生存分析和 miRNA-枢纽基因网络构建）鉴定枢纽基因。最后，利用 GEPIA2 和 DGIdb 数据库分别验证枢纽基因的表达水平，并选择 OC 的候选药物。

结果：共鉴定出 171 个 DEGs，包括 114 个上调 DEGs 和 57 个下调 DEGs。GO 分析结果表明，上调的 DEGs 主要参与细胞分裂、核和蛋白质结合，而下调的 DEGs 的生物学功能富集主要是 RNA 聚合酶 II 启动子转录的负调控、蛋白质复合物和顶端侧质膜、糖胺聚糖结合。至于 KEGG 途径，上调的 DEGs 主要与代谢途径、抗生素生物合成、氨基酸生物合成、细胞周期和 HTLV-I 感染有关。此外，鉴定出 10 个枢纽基因（KIF4A、CDC20、CCNB2、TOP2A、RRM2、TYMS、KIF11、BIRC5、BUB1B 和 FOXM1），这些枢纽基因的生存分析表明，OC 患者的 CCNB2、TYMS、KIF11、KIF4A、BIRC5、BUB1B、FOXM1 和 CDC20 表达水平较高，其无进展生存期更有可能较差。同时，基于 GEPIA2 的枢纽基因表达水平与基于 GEO 的表达水平一致。最后，DGIdb 数据库用于鉴定 62 种小分子作为 OC 治疗的潜在靶向药物。

结论：综上所述，这些数据可能为 OC 的发病机制和治疗提供新的见解。枢纽基因和候选药物可能会提高未来 OC 的个体化诊断和治疗水平。