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A bioinformatics approach to analyzing breast cancer genes among the African American and White/Caucasian populations
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1  Department of Engineering Technology, University of Houston, Sugar Land, TX 77479, USA
Academic Editor: Thomas Caulfield

Abstract:

Breast cancer is the most common type of cancer in women. Breast cancer affects 200,000 women in the United States each year. Some of its treatments include chemotherapy, radiation, and surgery. In the present study, we aim to analyze the race-specific differences in gene expression profiles of breast cancer subtypes among the African American and White/Caucasian populations. We used The Cancer Genome Atlas (TCGA) to study invasive breast carcinoma tumors and the correlated genes, TACSTD2, PIK3CA, and NTRK1. Cbioportal was used to find the expression levels of the three genes (NTRK1, PIK3CA, TACSTD2) and further understand their potential roles as DNA biomarkers for breast cancer. The Gene Cards database helped determine the genes with a direct correlation to breast cancer and identify which of them are its promoters or enhancers. Genes PIK3CA and TACSTD2 were shown to be directly correlated. UALCAN was used to analyze the different gene expression levels between the two races, White/Caucasian and African American. Additionally, the KEGG database found gene pathways related to breast cancer. Gene Ontology identified the phenotype of the PIK3CA gene concerning breast cancer. Reference sequencing analysis was conducted on the genes to find mRNA expressions relating to breast cancer. The findings showed genes NTRK1 and TACSTD2 were negatively correlated and gene PIK3CA was positively correlated. The purpose of this study was to measure the correlation of the genetic biomarkers of the African American and White/Caucasian ethnicities with breast cancer subtypes. The genes identified in our study could be potential targets to address different probable causes of and treatments for breast cancer in the African American and White/Caucasian ethnicities.

Keywords: Breast cancer; Race; Bioinformatics

 
 
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