Browsing by Author "El Masry M.R."

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Study of microRNAs-21/221 as potential breast cancer biomarkers in Egyptian women
    (Elsevier B.V., 2016) Motawi T.M.K.; Sadik N.A.H.; Shaker O.G.; El Masry M.R.; Mohareb F.; Biochemistry Department; Faculty of Pharmacy; Cairo University; Kasr El-Einy; Cairo; Egypt; Medical Biochemistry and Molecular Biology Department; Faculty of Medicine; Cairo University; Egypt; Biochemistry Department; Faculty of Dentistry; October University for Modern Sciences & Arts (MSA); Giza; Egypt; The Bioinformatics Group; School of Energy; Environment and AgriFood; Cranfield University; Bedford; MK43 0AL; United Kingdom
    microRNAs (miRNAs) play an important role in cancer prognosis. They are small molecules, approximately 17�25 nucleotides in length, and their high stability in human serum supports their use as novel diagnostic biomarkers of cancer and other pathological conditions. In this study, we analyzed the expression patterns of miR-21 and miR-221 in the serum from a total of 100 Egyptian female subjects with breast cancer, fibroadenoma, and healthy control subjects. Using microarray-based expression profiling followed by real-time polymerase chain reaction validation, we compared the levels of the two circulating miRNAs in the serum of patients with breast cancer (n=50), fibroadenoma (n=25), and healthy controls (n=25). The miRNA SNORD68 was chosen as the housekeeping endogenous control. We found that the serum levels of miR-21 and miR-221 were significantly overexpressed in breast cancer patients compared to normal controls and fibroadenoma patients. Receiver Operating Characteristic (ROC) curve analysis revealed that miR-21 has greater potential in discriminating between breast cancer patients and the control group, while miR-221 has greater potential in discriminating between breast cancer and fibroadenoma patients. Classification models using k-Nearest Neighbor (kNN), Nave Bayes (NB), and Random Forests (RF) were developed using expression levels of both miR-21 and miR-221. Best classification performance was achieved by NB Classification models, reaching 91% of correct classification. Furthermore, relative miR-221 expression was associated with histological tumor grades. Therefore, it may be concluded that both miR-21 and miR-221 can be used to differentiate between breast cancer patients and healthy controls, but that the diagnostic accuracy of serum miR-21 is superior to miR-221 for breast cancer prediction. miR-221 has more diagnostic power in discriminating between breast cancer and fibroadenoma patients. The overexpression of miR-221 has been associated with the breast cancer grade. We also demonstrated that the combined expression of miR-21 and miR-221can be successfully applied as breast cancer biomarkers. 2016 Elsevier B.V.