卵巢癌关键基因的筛选及其与免疫细胞浸润的相关性分析

Screening of key genes in ovarian cancer and their correlation with immune cell infiltration

摘要:

目的 利用生物信息学方法筛选卵巢癌 (Ovarian Cancer，OV) 关键基因并分析及其与免疫细胞浸润的相关性。 方法 从 GEO 检索并下载 3 组 OV 数据集，通过 GEO2R 在线分析工具筛选出 OV 组织中差异基因 (Differentially Expressed Genes，DEGs)，并利用Metascape数据库对DEGs进行基因本体论 (Gene Ontology，GO) 功能分析和京都基因与基因组百科全书 (Kyoto Encyclopedia of Genes and Genomes，KEGG) 通路富集分析；使用 STRING 数据库分析出 DEGs 的 PPI 网络后，再把结果导入 Cytoscape软件，利用其中的 CytoHubba插件提供的 MCC 算法最终得到排名前十的关键基因；然后利用 Kaplan-Meier Plotter数据库和 GEPIA 对关键基因进行生存分析和共表达分析，得出相关性最高的一组基因再分析其 mRNA 和蛋白表达情况；最后利用TISIDB数据库探讨关键基因与OV免疫细胞浸润之间的关联。结果 本研究共得到161个DEGs， 其GO功能主要与细胞对转化生长因子β刺激的反应、P53类介导的DNA损伤反应及因信号转导造成的细胞周期阻滞和蛋白激酶结合等相关；KEGG通路主要富集在癌症通路、P53信号通路、补体和凝血级联以及PI3K-Akt等信号通路上。通过Cy‐ toscape筛选的 10个关键基因中，仅 CCNB1、HMMR、KIAA0101、AURKA、CEP55、ECT2和 TOP2A对 OV患者预后有显著影响，而其中 CCNB1 和 AURKA 在 OV 中表达相关性最高，且其 mRNA 和蛋白均高表达，并不利于患者预后；CCNB1 与 AURKA的表达与Th2细胞、Act-CD4细胞的表达丰度呈正相关，与嗜酸性粒细胞呈负相关。结论 CCNB1和AURKA是OV的关键基因，且与OV微环境中免疫细胞相关，可作为OV的早期确诊和治疗生物标志物。

Abstract:

Objective Bioinformatics method was used to screen the key genes of ovarian cancer (OV) and analyze their correlation with immune cell infiltration. Methods We retrieved and downloaded three OV datasets from GEO, and screened DEGs in OV tissues by the GEO2R online analysis tool, then performed gene ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on DEGs using the Metascape database; after analyzing the PPI network of DEGs using the STRING database, the results were imported into Cytoscape software, and the MCC algorithm provided by the CytoHubba plugin was used to obtain the top 10 key genes; then we used the Kaplan-Meier Plotter database and GEPIA to perform survival analysis and co-expression analysis of key genes to derive the most highly correlated group of genes before doing their mRNA and protein expression; finally, the TISIDB database was used to explore the association between key genes and OV immune infiltration cell. Results A total of 161 DEGs were obtained in this study, and their GO functions were mainly related to the response of cells to TGF- β stimulation, P53-mediated DNA damage, cell cycle arrest and protein kinase binding caused by signal transduction, etc. KEGG pathway is mainly concentrated in cancer pathway, P53 signaling pathway, complement and coagulation cascade and PI3K-Akt signaling pathway. Among the 10 key genes screened by Cytoscape, only CCNB1, HMMR, KIAA0101, AURKA, CEP55, ECT2 and TOP2A have significant effects on the prognosis of OV patients, among which CCNB1 and AURKA have the highest expression correlation in OV, and their mRNA and protein are highly expressed, which is not conducive to the prognosis of patients. The expression of CCNB1 and AURKA was positively correlated with the abundance of Th2 cells and Act-CD4 cells, but negatively correlated with eosinophils. Conclusion CCNB1 and AURKA are the key genes of OV, which are related to immune cells in OV microenvironment, and can be used as biomarkers for early diagnosis and treatment of OV.