Literature DB >> 21113235

Quantitative proteomic analysis of ovarian cancer cells identified mitochondrial proteins associated with Paclitaxel resistance.

Yuan Tian1, Aik-Choon Tan, Xiaer Sun, Matthew T Olson, Zhi Xie, Natini Jinawath, Daniel W Chan, Ie-Ming Shih, Zhen Zhang, Hui Zhang.   

Abstract

Paclitaxel has been widely used as an anti-mitotic agent in chemotherapy for a variety of cancers and adds substantial efficacy as the first-line chemotherapeutic regimen for ovarian cancers. However, the frequent occurrence of paclitaxel resistance limits its function in long-term management. Despite abundant clinical and cellular demonstration of paclitaxel resistant tumors, the molecular mechanisms leading to paclitaxel resistance are poorly understood. Using genomic approaches, we have previously identified an association between a BTB/POZ gene, Nac1, and paclitaxel resistance in ovarian cancer. The experiments presented here have applied multiple quantitative proteomic methods to identify protein changes associated with paclitaxel resistance and Nac1 function. The SKOV-3 ovarian serous carcinoma cell line, which has inducible expression of dominant negative Nac1, was used to determine the paclitaxel treatment associated changes in the presence and absence of functional Nac1. Quantitative proteomic analyses were performed using iTRAQ labeling and mass spectrometry. Two label-free quantitative proteomic methods: LC-MS and spectral count were used to increase confidence of proteomic quantification. A total of 1371 proteins were quantified by at least one of the quantitative proteomic methods. Candidate proteins related to paclitaxel and NAC1 function were identified in this study. Go analysis of the protein changes identified upon paclitaxel resistance revealed that cell component enrichment related to mitochondria. Moreover, tubulin and mitochondrial proteins were the major cellular components with changes associated with paclitaxel treatment. This suggests that mitochondria may play a role in paclitaxel resistance.

Entities:  

Keywords:  Taxol; mass spectrometry; ovarian cancer; paclitaxel; proteomics

Year:  2009        PMID: 21113235      PMCID: PMC2989613          DOI: 10.1002/prca.200900005

Source DB:  PubMed          Journal:  Proteomics Clin Appl        ISSN: 1862-8346            Impact factor:   3.494


  36 in total

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Journal:  Proteomics       Date:  2007-03       Impact factor: 3.984

5.  The Paragon Algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra.

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6.  Proteolytic 18O labeling for comparative proteomics: model studies with two serotypes of adenovirus.

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7.  A BTB/POZ protein, NAC-1, is related to tumor recurrence and is essential for tumor growth and survival.

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Journal:  Proc Natl Acad Sci U S A       Date:  2006-11-27       Impact factor: 11.205

Review 8.  How Taxol stabilises microtubule structure.

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9.  Tubulin is an inherent component of mitochondrial membranes that interacts with the voltage-dependent anion channel.

Authors:  Manon Carré; Nicolas André; Gérard Carles; Hélène Borghi; Laetitia Brichese; Claudette Briand; Diane Braguer
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  14 in total

1.  Identification, prioritization, and evaluation of glycoproteins for aggressive prostate cancer using quantitative glycoproteomics and antibody-based assays on tissue specimens.

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2.  Glycoproteomic analysis of bronchoalveolar lavage (BAL) fluid identifies tumor-associated glycoproteins from lung adenocarcinoma.

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3.  Detection of aggressive prostate cancer associated glycoproteins in urine using glycoproteomics and mass spectrometry.

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6.  Proteomics Analysis of Ovarian Cancer Cell Lines and Tissues Reveals Drug Resistance-associated Proteins.

Authors:  Isa N Cruz; Helen M Coley; Holger B Kramer; Thumuluru Kavitah Madhuri; Nur A M Safuwan; Ana Rita Angelino; Min Yang
Journal:  Cancer Genomics Proteomics       Date:  2017-01-02       Impact factor: 4.069

7.  Mapping tissue-specific expression of extracellular proteins using systematic glycoproteomic analysis of different mouse tissues.

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8.  Proteomic analysis of temporally stimulated ovarian cancer cells for biomarker discovery.

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Review 9.  Prediction of individual response to anticancer therapy: historical and future perspectives.

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