ArticleWorld journal of surgical oncology2020
Identification of key genes unique to the luminal a and basal-like breast cancer subtypes via bioinformatic analysis.
Article in World journal of surgical oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 40 citations in OpenAlex.
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- GEMIN4, a potential therapeutic targets for patients with basal-like subtype breast cancer.BMC women's health · 2023Article
- Schlafen 12 Slows TNBC Tumor Growth, Induces Luminal Markers, and Predicts Favorable Survival.Cancers · 2023Article
- Advances with metal oxide-based nanoparticles as MDR metastatic breast cancer therapeutics and diagnostics.RSC advances · 2022Review
- Identification of Key Genes and Key Pathways in Breast Cancer Based on Machine Learning.Medical science monitor : international medical journal of experimental and clinical research · 2022Article
- Nano to rescue: repository of nanocarriers for targeted drug delivery to curb breast cancer.3 Biotech · 2022Review
- Kinesin Family Member 2A Serves as a Potential Biomarker Reflecting More Frequent Lymph Node Metastasis and Tumor Recurrence Risk in Basal-Like Breast Cancer Patients.Frontiers in surgery · 2022Article
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- Comprehensive analysis of the cancer driver genes in breast cancer demonstrates their roles in cancer prognosis and tumor microenvironment.World journal of surgical oncology · 2021Article
- Expression levels and clinical values of miR-92b-3p in breast cancer.World journal of surgical oncology · 2021Article
- In silico analysis of differentially expressed genesets in metastatic breast cancer identifies potential prognostic biomarkers.World journal of surgical oncology · 2021Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBreast 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.
methodsWe 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.
resultsWe 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.
conclusionsNMUR1 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.
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