基于生物信息学方法分析乳腺癌新辅助内分泌治疗相关差异基因表达及预后

Expression of different genes and their prognosis in breast cancer using bioinformatic analysis

【目的】利用生物信息学分析方法分析乳腺癌新辅助内分泌治疗前后的差异表达基因,分析其调控网络,评价其预后价值。【方法】在美国基因芯片数据库(gene expression omnibus,GEO)中获取乳腺癌新辅助内分泌治疗前后基因表达数据集,利用在线工具GEO2R筛选差异表达基因(differentially expressed genes,DEGs),并通过STRING在线数据库构建蛋白互相作用网络(protein-protein interaction,PPI),利用GEPIA数据和临床样本(乳腺癌和癌旁组织)qRT-PCR技术双重验证枢纽基因在乳腺癌组织与癌旁组织的表达,最后利用UalCan在线数据库分析枢纽基因在乳腺癌患者中的总生存期。【结果】共纳入2个GEO数据集(GSE111563和GSE59515),筛选出21个差异表达基因,其中上调差异基因17个,下调差异基因4个。对差异表达基因编码的蛋白质间的相互作用进行网络绘制,发现这些基因编码的蛋白间的相互作用主要集中在FOS,STC2,CYR61,EGR1,PTGS2,ATF3,FOSB,ZFP36,RGS2,DUSP110个蛋白。通过GEPIA分析乳腺癌组织1 085例,癌旁组织291例发现,FOS,CYR61,ATF3,FOSB在乳腺癌组织与癌旁组织中的表达具有显著差异;临床乳腺癌样本中这四个基因的mRNA也明显低于癌旁组织(P<0.001)。通过UalCan生存分析发现FOS高表达与乳腺癌生存期有相关性。【结论】利用生物信息学方法分析乳腺癌新辅助内分泌治疗的差异表达基因,有效发掘这些差异表达基因的数据信息,为乳腺癌新辅助内分泌治疗提供新的思路。

【Objective】To analyze the differentially expressed genes(DEGs) of the breast cancer before and after neoadjuvant endocrine therapy, regulatory networks and its prognositic information by bioinformatics analysis.【Methods】The gene microarray data about neoadjuvant endocrine therapy was from NCBI’s Gene Expression Omnibus(GEO) database. The differentially expressed genes(DEGs)were screened out with GEO2 R online software. Subsequently, the protein-protein interaction(PPI) network was constructed using String software. The node genes were bothverified by GEPIA analysisand clinical samples(breast cancer and paracancerous tissues)using qRT-PCR. The correlation between node genes and overall survival in breast cancer patients was analyzed using UalCan online database.【Results】We included two GEO databases(GSE111563 and GSE59515). A total of 21 differentially expressed genes were obtained, including 17 upregulated genes and 4 downregulated genes. 10 node genes with high degree of connectivity were selected,including FOS,STC2, CYR61, EGR1, PTGS2, ATF3, FOSB, ZFP36, RGS2, DUSP1. GEPIA analysis of 1085 cases of breast cancer tissues and 291 cases of para-cancer tissues demonstrated that there were four genes including FOS, CYR61, ATF3, FOSB are significantly difference between breast cancer tissues and paracancer tissues,m RNA levels of which were also significantly lower in breast cancer samples than in paracancerous tissues from clinicalpatients(P < 0.001).We further found that the high expression of FOS was related with overall survival in breast cancer by Ual Can survival analysis.【Conclusion】Using bioinformatic methods to analyze DEGs about neoadjuvant endocrine therapy in breast cancer can effectively explore their information, which provides new idea about the neoadjuvant endocrine therapy in breast cancer.