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- W3192385481 abstract "Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype generally associated with younger women. Due to the lack of suitable drugable targets in TNBC, the microRNAs are considered as a better hope as therapeutic agents for the management of the disease. In this study, we identified differentially expressed miRNAs (DEMs) and associated hub genes in TNBC microarray data (GSE38167, GSE60714, and GSE10833) using bioinformatics tools. The identified miRNAs and genes were validated in the TNBC cell line model (MDA-MB-231) compared with the normal breast cells (MCF-10A) using the qRT-PCR technique. False-positive DEMs were avoided by comparing the DEMs profile of TNBC and triple positive breast cancer (TPBC) cell line model (BT474) compared with the MCF-10A cells data. In addition, we studied the effect of anticancer phytochemicals on the differential expression of miRNAs and genes in MDA-MB-231 cells. Furthermore, target predictions, functional enrichment and KEGG pathway analysis, mutation and copy number alterations, and overall survival analysis of DEMs in TNBC sample was investigated using standard computational tools. The study identifies first time the association of hsa-miR-1250, has-miR-1273, and has-miR-635 with the TNBC. DEMs showed significant association with the Wnt, ErbB, PI3-Akt and cAMP signaling pathways having clinical implications in TNBC tumorigenesis. The DEMs and hub genes (HOXC6 and ACVR2B) showed survival disadvantages in TNBC patients. In summary, the identified miRNAs and hub genes show important implications in TNBC tumorigenesis and patient survival. We recommend further experimental studies on pathophysiological mechanism of the identified miRNAs and hub genes in TNBC.Communicated by Ramaswamy H. Sarma." @default.
- W3192385481 created "2021-08-16" @default.
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- W3192385481 date "2021-08-13" @default.
- W3192385481 modified "2023-10-16" @default.
- W3192385481 title "Identification of miRNAs and related hub genes associated with the triple negative breast cancer using integrated bioinformatics analysis and <i>in vitro</i> approach" @default.
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- W3192385481 doi "https://doi.org/10.1080/07391102.2021.1961869" @default.
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