Identification of biomarkers and potential therapeutic targets of breast cancer using bioinformatics analysis

Le résumé fourni par la source

Breast cancer (BRCA) is one of the most common neoplastic diseases among the female population. There were 43,780 BRCA-related deaths, with a mortality rate of 15% in the United States in 2022. Therefore, it is necessary to screen and validate potential diagnostic and prognostic biomarkers related to BRCA through bioinformatics technology. The GSE42568 and GSE5364 datasets were obtained from the Gene Expression Omnibus database, and the Cancer Genome Atlas BRCA dataset was sourced from the Xena functional genomics platform. Differentially expressed genes were identified across these 3 BRCA datasets using Venn diagram software. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were carried to investigate the role of these genes and potential underlying mechanisms in BRCA. A protein-protein interaction network was constructed using the STRING database, and key genes were identified using the CytoHubba plugin in Cytoscape software. Hub genes were determined via Kaplan-Meier survival analysis and were validated using the GEPIA, UALCAN platforms. In addition, the expression of these genes in tumor and adjacent tissues of BRCA patients was validated by reverse transcription quantitative polymerase chain reaction. A total of 84 overlapping differentially expressed genes were preliminarily screened at the initial stage, including 33 upregulated and 51 downregulated genes. The top 20 highly connected key genes were identified subsequently using Cytoscape software. These hub genes show good consistency with the progression of the BRCA. In addition, the results showed that the expression levels of these 5 hub genes are significantly different among BRCA subtypes, which may provide support for identifying BRCA subtypes and optimizing treatment strategies. Four hub genes (AURKA, DTL, NUSAP1, and RRM2) were finally identified through reverse transcription quantitative polymerase chain reaction. When compared to normal tissues, all of these genes exhibit high expression levels in BRCA tissues. The expression levels of AURKA, DTL, and RRM2 are positively correlated with NUSAP1, indicating a common regulatory relationship among these genes. Our present study demonstrated that AURKA, DTL, NUSAP1, and RRM2 are potential diagnostic and prognostic biomarkers for BRCA, and may also be potential treatment targets for clinical implication in the future.

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