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Publication:
In Silico Analysis of Publicly Available Transcriptomic Data for the Identification of Triple-Negative Breast Cancer-Specific Biomarkers

dc.contributor.authorKaddoura, Rachid
dc.contributor.authorAlqutami, Fatma
dc.contributor.authorAsbaita, Mohamed
dc.contributor.authorHachim, Mahmood Yaseen
dc.date.accessioned2023-07-17T09:44:29Z
dc.date.available2023-07-17T09:44:29Z
dc.date.issued2023
dc.description.abstractBackground: Breast 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. Aim: This study aims to use in silico methods to identify potential biomarkers for TNBC screening and diagnosis, as well as potential therapeutic markers. Methods: Publicly 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. Results: A 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. Conclusions: The 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.en_US
dc.identifier.other204-2023.22
dc.identifier.urihttps://repository.mbru.ac.ae/handle/1/1266
dc.language.isoenen_US
dc.subjectTriple-negative Breast Canceren_US
dc.subjectIn Silico Analysisen_US
dc.subjectDifferentially Expressed Geneen_US
dc.subjectBiomarkersen_US
dc.subjectGATA 3en_US
dc.subjectFOXA1en_US
dc.subjectTumor Microenvironmenten_US
dc.titleIn Silico Analysis of Publicly Available Transcriptomic Data for the Identification of Triple-Negative Breast Cancer-Specific Biomarkersen_US
dc.typeArticleen_US
dspace.entity.typePublication

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