Literature DB >> 17786328

Differential expression of acidic proteins with progression in the MCF10 model of human breast disease.

Nathan S Buchanan1, Jia Zhao, Kan Zhu, Tasneem H Patwa, Fred R Miller, David M Lubman.   

Abstract

A proteomic characterization of one premalignant (MCF10AT1) and two malignant (MCF10CA1a and MCF10 CA1d) human breast cancer cell lines has been performed using a combination of two-dimensional liquid separations and mass spectrometry. Chromatofocusing (CF) and NPS-RP-HPLC are combined with ESI-TOF-MS to resolve and detect intact proteins. Simultaneously, fractions are collected and digested for protein identification using MALDI-MS peptide mass fingerprinting. Following protein identification a small database was compiled for use in comparison between IDs and measured masses taking into account variables such as pI, hydrophobicity and potential modifications. Out of 239 mass bands detected between pH 4.6 and 6.0, 133 have been definitively associated with identified proteins and 67 show consistent up/down regulation in two malignant breast cancer cell lines relative to the precursor premalignant cell line. Of these, 8 are also altered in the premalignant MCF10AT1 cell line by treatment with estradiol. Differentially expressed proteins indicate significant changes to the cytoskeleton, cellular metabolism, and adaptation to environmental stressors in malignant cell lines.

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Year:  2007        PMID: 17786328

Source DB:  PubMed          Journal:  Int J Oncol        ISSN: 1019-6439            Impact factor:   5.650


  4 in total

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Authors:  Andrew M Mattei; Jonathan D Smailys; Emma Marie Wilber Hepworth; Shantá D Hinton
Journal:  Int J Mol Sci       Date:  2021-06-28       Impact factor: 5.923

3.  Integrated proteomic and metabolic analysis of breast cancer progression.

Authors:  Patrick G Shaw; Raghothama Chaerkady; Tao Wang; Shauna Vasilatos; Yi Huang; Bennett Van Houten; Akhilesh Pandey; Nancy E Davidson
Journal:  PLoS One       Date:  2013-09-27       Impact factor: 3.240

4.  Three-dimensional modelling identifies novel genetic dependencies associated with breast cancer progression in the isogenic MCF10 model.

Authors:  Sarah L Maguire; Barrie Peck; Patty T Wai; James Campbell; Holly Barker; Aditi Gulati; Frances Daley; Simon Vyse; Paul Huang; Christopher J Lord; Gillian Farnie; Keith Brennan; Rachael Natrajan
Journal:  J Pathol       Date:  2016-11       Impact factor: 7.996

  4 in total

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