浆液性卵巢癌预后生物标志物的筛选及免疫细胞浸润的分析

Screening of prognostic biomarkers and analysis of immune cell infiltration in serous ovarian cancer

目的利用生物信息学方法筛选浆液性卵巢癌的预后生物标志物并分析免疫细胞的浸润模式。方法从GEO数据库中下载GSE40595、GSE36668及GSE69428数据集,筛选浆液性卵巢癌和正常卵巢上皮组织的差异基因,进行GO和KEGG富集分析。通过构建PPI网络,鉴定浆液性卵巢癌的Hub基因,并利用GEPIA和Kaplan-Meier Plotter分析Hub基因的表达情况及与预后的相关性,利用CIBERSORT算法进行免疫细胞浸润分析并绘制相关图形。结果本研究挖掘出217个差异表达基因,富集后发现主要涉及细胞周期及细胞分裂;发现UBE2C、NCAPG、CDK1、KIF15、DLGAP5、KIF2C、TOP2A、RRM2、NUSAP1在浆液性卵巢癌中呈高表达且与预后差显著相关;发现B记忆细胞、滤泡辅助T细胞、M1型巨噬细胞在浆液性卵巢癌组织中上调。结论本研究挖掘出9个与浆液性卵巢癌预后密切相关的基因,细胞周期和细胞分裂在浆液性卵巢癌发病中具有重要意义,B记忆细胞、滤泡辅助T细胞、M1型巨噬细胞在浆液性卵巢癌发病中也起一定作用。

Objective To screen prognostic biomarkers and analyze the infiltration patterns of immune cells of serous ovarian cancer with bioinformatics methods.Methods GSE40595,GSE36668 and GSE69428 data sets were downloaded from GEO database to screen the differential genes between serous ovarian cancer and normal ovarian epithelial tissue.The differential genes were analyzed by GO and KEGG enrichment.The Hub genes of serous ovarian cancer were identified by constructing PPI network.GEPIA and Kaplan-Meier Plotter were used to analyze the expression and prognostic correlation of Hub genes in serious ovarian cancer.CIBERSORT algorithm was used to analyze the immune cell infiltration of serious ovarian cancer and draw the correlation graph.Results 217 differentially expressed genes were found,which were mainly involved in cell cycle and cell division after enrichment.UBE2C,NCAPG,CDK1,KIF15,DLGAP5,KIF2C,TOP2A,RRM2 and NUSAP1 were found to be highly expressed in serous ovarian cancer with poor prognosis.B memory cells,follicular helper T cells and M1 macrophages were up-regulated in serous ovarian cancer tissue.Conclusions 9 genes were found to be closely related to the prognosis of serous ovarian cancer.Cell cycle and cell division play an important role in pathogenesis of serous ovarian cancer.B memory cells,follicular helper T cells and M1 macrophages also play a certain role in the pathogenesis of serous ovarian cancer.