Bioinformatics analysis to identify breast cancer-related potential targets and candidate small molecule drugs

Objective

The purpose of this study is to identify potential targets associated with breast cancer and screen potential small molecule drugs using bioinformatics analysis.

Methods

DEGs analysis of breast cancer tissues and normal breast tissues was performed using R language limma analysis on the GSE42568 and GSE205185 datasets. Functional enrichment analysis was conducted on the intersecting DEGs. The STRING analysis platform was used to construct a PPI network, and the top 10 core nodes were identified using Cytoscape software. QuartataWeb was utilized to build a target-drug interaction network and identify potential drugs. Cell survival and proliferation were assessed using CCK8 and colony formation assays. Cell cycle analysis was performed using flow cytometry. Western blot analysis was conducted to assess protein levels of PLK1, MELK, AURKA, and NEK2.

Results

A total of 54 genes were consistently upregulated in both datasets, which were functionally enriched in mitotic cell cycle and cell cycle-related pathways. The 226 downregulated genes were functionally enriched in pathways related to hormone level regulation and negative regulation of cell population proliferation. Ten key genes, namely CDK1, CCNB2, ASPM, AURKA, TPX2, TOP2A, BUB1B, MELK, RRM2, and NEK2 were identified. The potential drug Fostamatinib was predicted to target AURKA, MELK, CDK1, and NEK2. In vitro experiments demonstrated that Fostamatinib inhibited the proliferation of breast cancer cells, induced cell arrest in the G2/M phase, and down-regulated MELK, AURKA, and NEK2 proteins.

Conclusion

In conclusion, Fostamatinib shows promise as a potential drug for the treatment of breast cancer by regulating the cell cycle and inhibiting the proliferation of breast cancer cells.