目的：运用生物信息学方法筛选与乳腺癌发生发展有关的差异表达基因，并对其进行相关生物学分析以获得乳腺癌 相关分子标志物。方法： 从GEO数据库中寻找出3个乳腺癌相关芯片数据集，使用 GEO2R 筛选差异表达基因并作 Venn 图 获取交集差异共表达基因。通过 DAVID 进行 GO 功能富集分析和 KEGG信号通路分析。在STRING网站中对差异表达基 因构建蛋白质-蛋白质相互用（protein-protein interaction，PPI）网络图，并通过MCODE分析PPI中最重要的模块，其中评分≥10的 鉴定为枢纽基因（Hub基因）。利用UCSC对Hub基因进行层次聚类分析，利用cBioPortal构建Hub基因的共表达网络和生存曲 线。结果：筛选出 3 个数据集的差异共表达基因 65 个。通过分析获得 CTNNB1、CDKN1A、CXCR4、RUNX3、CASP8、TNFRSF10B、CFLAR和NRG1等共8个Hub基因，这些基因在细胞黏附、细胞增殖、凋亡调控等方面有重要作用。聚类分析表明， 基 因CTNNB1、CFLAR、NRG1和CXCR4表达的改变与乳腺癌的发生有关。生存曲线分析表明，CDKN1A表达的升高使得乳腺癌 患者总体生存率显著降低（P<0.01）。结论：本研究中鉴定的Hub基因可作为乳腺癌分子标志物，为乳腺癌的诊断和治疗靶点选 择及预后判断提供参考。

Objective: To investigate the differentially expressed genes (DEGs) associated with the occurrence and development of breast cancer and to screen the molecular markers for breast cancer by bioinformatic analysis. Methods: Three breast cancer microarray datasets were downloaded from Gene Expression Omnibus (GEO) database. GEO2R was used to identify DEGs. The differentially co-expressed genes in the three datasets were screened by Venn diagram. GO function enrichment analysis and KEGG signal pathway analysis were performed using DAVID. The protein-protein interaction (PPI) network of DEGs was constructed using STRING. The most important modules in the PPI network were analyzed using Molecular Complex Detection (MCODE), and the genes with degree≥10 were identified as Hub genes. Hierarchical clustering analysis of hub genes was conducted using UCSC Cancer Genomics Brower. The survival curve and the co-expression network of hub genes were constructed using cBioPortal. Results: A total of 65 DEGs were screened from the three data sets. Eight hub genes, CTNNB1, CDKN1A, CXCR4, RUNX3, CASP8, TNFRSF10B, CFLAR and NRG1, were finally obtained, which exerted important roles in cell adhesion, proliferation and apoptosis regulation etc. Clustering analysis showed that the differential expression levels of CTNNB1, CFLAR, NRG1 and CXCR4 were associated with the occurrence of breast cancer. The overall survival analysis indicated that the patients with elevated CDKN1Aexpression had significantly shorter overall survival time (P<0.01). Conclusion: The hub genes identified in the present study can be used as molecular markers for breast cancer, providing candidate targets for diagnosis, treatment and prognostic prediction of breast cancer.

山西省“136”兴医工程资助项目（No.2019XY010）