ArticleLife (Basel, Switzerland)2023
In Silico Analysis of Publicly Available Transcriptomic Data for the Identification of Triple-Negative Breast Cancer-Specific Biomarkers.
Article in Life (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Expression Analysis ofIranian journal of medical sciences · 2026Article
- Low tumoral Trefoil Factor 1 expression relates to aggressive tumor features and poor survival in young women with breast cancer.Scientific reports · 2026Article
- Network-based insights into miR-30a-5p-mediated regulation and EGCG targeting in triple-negative breast cancer.Frontiers in bioinformatics · 2025Article
- Bridging Discoveries and Treatments: The New Landscape of Breast Cancer Research.Life (Basel, Switzerland) · 2024Article
- Machine learning-guided differential gene expression analysis identifies a highly-connected seven-gene cluster in triple-negative breast cancer.BioMedicine · 2024Article
- Omics-Based Investigations of Breast Cancer.Molecules (Basel, Switzerland) · 2023Review
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBreast cancer is the most common type of cancer among women and is classified into multiple subtypes. Triple-negative breast cancer (TNBC) is the most aggressive subtype, with high mortality rates and limited treatment options such as chemotherapy and radiation. Due to the heterogeneity and complexity of TNBC, there is a lack of reliable biomarkers that can be used to aid in the early diagnosis and prognosis of TNBC in a non-invasive screening method.
aimThis study aims to use in silico methods to identify potential biomarkers for TNBC screening and diagnosis, as well as potential therapeutic markers.
methodsPublicly available transcriptomic data of breast cancer patients published in the NCBI's GEO database were used in this analysis. Data were analyzed with the online tool GEO2R to identify differentially expressed genes (DEGs). Genes that were differentially expressed in more than 50% of the datasets were selected for further analysis. Metascape, Kaplan-Meier plotter, cBioPortal, and the online tool TIMER were used for functional pathway analysis to identify the biological role and functional pathways associated with these genes. Breast Cancer Gene-Expression Miner v4.7 was used to validify the obtained results in a larger cohort of datasets.
resultsA total of 34 genes were identified as differentially expressed in more than half of the datasets. The DEG GATA3 had the highest degree of regulation, and it plays a role in regulating other genes. The estrogen-dependent pathway was the most enriched pathway, involving four crucial genes, including GATA3. The gene FOXA1 was consistently down-regulated in TNBC in all datasets.
conclusionsThe shortlisted 34 DEGs will aid clinicians in diagnosing TNBC more accurately as well as developing targeted therapies to improve patient prognosis. In vitro and in vivo studies are further recommended to validate the results of the current study.
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