ArticleOncoTargets and therapy2018
Identification of potential core genes in triple negative breast cancer using bioinformatics analysis.
Article in OncoTargets and therapy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.
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- Integrated analysis of hub genes and intrinsically disordered regions in triple-negative breast cancer.Journal, genetic engineering & biotechnology · 2024Article
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- Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer.Medicina (Kaunas, Lithuania) · 2023Article
- MMR Deficiency Defines Distinct Molecular Subtype of Breast Cancer with Histone Proteomic Networks.International journal of molecular sciences · 2023Article
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- Prognostic score model based on six m6A-related autophagy genes for predicting survival in esophageal squamous cell carcinoma.Journal of clinical laboratory analysis · 2022Article
- Research on correlations of miR-585 expression with progression and prognosis of triple-negative breast cancer.Clinical and experimental medicine · 2022Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTriple-negative breast cancer (TNBC) is a subtype of breast cancer with poor clinical outcome and limited treatment options. Lacking molecular targets, chemotherapy is the main adjuvant treatment for TNBC patients. MATERIALS AND
methodsTo explore potential therapeutic targets for TNBC, we analyzed three microarray datasets (GSE38959, GSE45827, and GSE65194) derived from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) between TNBC and normal tissue. Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery to identify the pathways and functional annotation of DEGs. Protein-protein interaction of these DEGs was analyzed based on the Search Tool for the Retrieval of Interacting Genes database and visualized by Cytoscape software. In addition, we used the online Kaplan-Meier plotter survival analysis tool to evaluate the prognostic value of hub genes expression in breast cancer patients.
resultsA total of 278 upregulated DEGs and 173 downregulated DEGs were identified. Among them, ten hub genes with a high degree of connectivity were picked out. Overexpression of these hub genes was associated with unfavorable prognosis of breast cancer, especially,
conclusionOur study suggests that
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