Literature DB >> 12687611

Use of serological proteomic methods to find biomarkers associated with breast cancer.

Zhao Rui1, Ji Jian-Guo, Tong Yuan-Peng, Pu Hai, Ru Bing-Gen.   

Abstract

New technologies for the detection and therapy of early stage breast cancer are urgently needed. Pathological changes in breast might be reflected in proteomic patterns in serum. A proteomic tool was used to identify proteomic patterns in serum that distinguishes neoplastic from non-neoplastic disease within the breast. Preliminary results derived from the serum analysis from 54 unaffected women and 76 patients with breast cancer were analyzed by two-dimensional (2-D) electrophoresis and matrix-assisted laser desorption/ionization-time of flight mass spectrometry, HSP27 was found up-regulated while 14-3-3 sigma was down-regulated in the serum of breast cancer patients. The two protein biomarkers were then used to classify an independent set of 104 masked serum samples. The results showed that the protein pattern on 2-D gels can completely segregate the serum of breast cancer from non-cancer. The discriminatory pattern correctly identified all 69 breast cancer cases in the masked set. Of the 35 cases of non-malignant disease, 34 were recognized as non-cancer. These findings justify a prospective population-based assessment of proteomic technology as a screening or diagnostic tool for breast cancer in high-risk and general populations. These two protein biomarkers could also be used as targets for further study in drug design and breast cancer therapy.

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Year:  2003        PMID: 12687611     DOI: 10.1002/pmic.200390058

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  23 in total

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Journal:  Proteomics       Date:  2006-12       Impact factor: 3.984

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Journal:  Biochim Biophys Acta       Date:  2009-05-04

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10.  Ovarian cancer detection from metabolomic liquid chromatography/mass spectrometry data by support vector machines.

Authors:  Wei Guan; Manshui Zhou; Christina Y Hampton; Benedict B Benigno; L Deette Walker; Alexander Gray; John F McDonald; Facundo M Fernández
Journal:  BMC Bioinformatics       Date:  2009-08-22       Impact factor: 3.169

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