Differentially expressed genes and key molecules of -mutant breast cancer: evidence from bioinformatics analyses.

BACKGROUND

and genes are currently proven to be closely related to high lifetime risks of breast cancer. To date, the closely related genes to mutations in breast cancer remains to be fully elucidated. This study aims to identify the gene expression profiles and interaction networks influenced by mutations, so as to reflect underlying disease mechanisms and provide new biomarkers for breast cancer diagnosis or prognosis.

METHODS

Gene expression profiles from The Cancer Genome Atlas (TCGA) database were downloaded and combined with cBioPortal website to identify exact breast cancer patients with mutations. Gene set enrichment analysis (GSEA) was used to analyze some enriched pathways and biological processes associated mutations. For -mutant breast cancer, wild-type breast cancer and corresponding normal tissues, three independent differentially expressed genes (DEGs) analysis were performed to validate potential hub genes with each other. Protein-protein interaction (PPI) networks, survival analysis and diagnostic value assessment helped identify key genes associated with mutations.

RESULTS

The regulation process of cell cycle was significantly enriched in mutant group compared with wild-type group. A total of 294 genes were identified after analysis of DEGs between mutant patients and wild-type patients. Interestingly, by the other two comparisons, we identified 43 overlapping genes that not only significantly expressed in wild-type breast cancer patients relative to normal tissues, but more significantly expressed in -mutant breast patients. Based on the STRING database and cytoscape software, we constructed a PPI network using 294 DEGs. Through topological analysis scores of the PPI network and 43 overlapping genes, we sought to select some genes, thereby using survival analysis and diagnostic value assessment to identify key genes pertaining to -mutant breast cancer. , , , and displayed good prognostic/diagnostic value for breast cancer and -mutant breast cancer.

CONCLUSION

Our research provides comprehensive and new insights for the identification of biomarkers connected with mutations, availing diagnosis and treatment of breast cancer and -mutant breast cancer patients.