摘要：AbstractMetastasis Associated 1 (MTA1) chromatin modifier oncoprotein played a crucial role in both normal and genotoxic stress situations for genome maintenance. To investigates MTA1 regulatory pattern with drug resistance abilities in breast primary carcinoma to advanced invasive stages for identification of cancer adapting defense system. We design rationalein silicopipeline from data retrieval to analysis i.e. gene enrichment analysis performed by GeneCards Version 5.1: THE HUMAN GENE DATABASE (www.genecards.org), UALCAN database (www.ualcan.path.uab.edu) for analyzing MTA1 gene expression and promoter methylation in both breasts normal and cancerous tissue samples, cBioPortal for Cancer Genomics (www.cbioportal.org) database for MTA1 mutation analysis and finally analyzed MTA1 functional association with anticancer drugs in breast malignancy via online CCLE GDSC toolkit (www.public.tableau.com/CCLE_GDSC_Correlations). Our results revealed MTA1 overexpression aggressive behavior in stage II, stage III, TNBC-LAR, TNBC-M, TNBC-UNS, IDC, ILC, and post-menopause events of breast malignancy. MTA1 upregulation strongly promotes primary tumor transformation into invasive metastatic carcinoma by hijacking the host lymphatic system and cytokine signaling in both ductal and glandular breast cancers. MTA1 upregulation in the African-American population invites to design de novo model of cancer cell homeostasis under a reduced supply of vegetable nutrients, local-foreign stress, and replicative capacity for metastasis. MTA1 showed a hypomethylation profile that reflects regulatory strength under stress-mediated situations for higher events of transcription. In drug resistance analysis MTA1 has strong resistance towards 15 anti-cancer drugs that confirmed its previously reported behavior of genotoxic stress adaptation for metastasis. Ourin silicoevidence invites us to design a comprehensive strategy against MTA1 mediated stress managing proteome. In the future there is an urgent need to explore MTA1 shared stress coped protein networks for early diagnosis and better prognosis.
