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Background  Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field.    Methods  We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein–protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype.    Results  We identified 1114 differentially expressed genes in luminal A breast cancer and 1042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein–protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of  NMUR1  and  NCAM1  in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of  CDC7 ,  KIF18A ,  STIL , and  CKS2  in basal-like breast cancer statistically correlated with poor prognosis.    Conclusions   NMUR1  and  NCAM1  are novel key disease-causing genes for luminal A breast cancer, and  STIL  is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.

Identification of key genes unique to the luminal a and basal-like breast cancer subtypes via bioinformatic analysis