Breast cancer (BC) is the most malignant tumor in women. The molecular mechanisms underlying tumorigenesis still need to be further elucidated. It is necessary to investigate novel candidate genes involved in breast cancer progression and prognosis. In this study, we commit to explore candidate genes that associate with prognosis and therapy in BC by a comprehensive bioinformatic analysis. Four GEO datasets (GSE5764, GSE7904, GSE20711, and GSE29431) and the BC-related transcriptome data in TCGA database were downloaded and used to identify the differently expressed genes (DEGs). The function of DEGs was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis. The protein–protein interaction (PPI) network of DEGs was constructed to identify hub genes. Prognostic candidate genes were identified through survival analysis. In addition, potential therapeutic targets were identified by constructed gene–drug interaction network through Comparative Toxicogenomics Database. A total of 547 DEGs (302 up and 245 down) were identified. Three core-subnetwork and 25 hub genes were identified in PPI network. Seven genes (namely COL12A1, QPRT, MRPL13, KRT14, KRT15, LAMB3, and MYBPC1) were identified as crucial prognostic candidate genes, which significantly associated with breast cancer overall survival. Furthermore, two representative candidate genes (COL12A1 and LAMB3) were optionally chosen for verification by reverse transcription and quantitative real-time polymerase chain reaction (RT-PCR). What's more, the gene–drugs interaction analysis indicates several antitumor drugs that could affect the expression of these prognostic markers, such as doxorubicin, cisplatin, and tamoxifen. These results identified seven crucial candidate genes that may serve as prognosis biomarkers and novel therapeutic targets of breast cancer, which may facilitate further understanding the molecular pathogenesis and providing potential therapeutic strategies for BC.

Search results

Identification of Candidate Genes Associated with Breast Cancer Prognosis

Reporting on Adverse Clinical Events