Screening of molecular markers in breast cancer based on GEO database / 中国肿瘤生物治疗杂志

ABSTRACT

@# 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.