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Several evidences support the idea that a small population of tumour cells representing self-renewal potential are involved in initiation, maintenance, metastasis, and outcomes of cancer therapy. Elucidation of microRNAs/genes regulatory networks activated in cancer stem cells (CSCs) is necessary for the identification of new targets for cancer therapy. The aim of the present study was to predict the miRNAs pattern, which can target both metastasis and self-renewal pathways using integration of literature and data mining. For this purpose, mammospheres derived from MCF-7, MDA-MB231, and MDA-MB468 were used as breast CSCs model. They had higher migration, invasion, and colony formation potential, with increasing in stemness- and EMT-related genes expression. Our results determined that miR-204, -200c, -34a, and -10b contemporarily could target both self-renewal and EMT pathways. This core regulatory of miRNAs could increase the survival rate of breast invasive carcinoma via up-regulation of OCT4, SOX2, KLF4, c-MYC, NOTCH1, SNAI1, ZEB1, and CDH2 and down-regulation of CDH1. The majority of those target genes were involved in the regulation of pluripotency, MAPK, WNT, Hedgehog, p53, and transforming growth factor β pathways. Hence, this study provides novel insights for targeting core regulatory of miRNAs in breast CSCs to target both self-renewal and metastasis potential and eradication of breast cancer.
Pubmed ID: 30710426
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View all literature mentionsWeb tool to predict biological targets of miRNAs by searching for presence of conserved 8mer, 7mer and 6mer sites that match seed region of each miRNA. Nonconserved sites are also predicted and sites with mismatches in seed region that are compensated by conserved 3' pairing. Used to search for predicted microRNA targets in mammals.
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
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