Literature DB >> 12877381

Cancer diagnosis using proteomic patterns.

Thomas P Conrads1, Ming Zhou, Emmanuel F Petricoin, Lance Liotta, Timothy D Veenstra.   

Abstract

The advent of proteomics has brought with it the hope of discovering novel biomarkers that can be used to diagnose diseases, predict susceptibility and monitor progression. Much of this effort has focused upon the mass spectral identification of the thousands of proteins that populate complex biosystems such as serum and tissues. A revolutionary approach in proteomic pattern analysis has emerged as an effective method for the early diagnosis of diseases such as ovarian cancer. Proteomic pattern analysis relies on the pattern of proteins observed and does not rely on the identification of a traceable biomarker. Hundreds of clinical samples per day can be analyzed utilizing this technology, which has the potential to be a novel, highly sensitive diagnostic tool for the early detection of cancer.

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Year:  2003        PMID: 12877381     DOI: 10.1586/14737159.3.4.411

Source DB:  PubMed          Journal:  Expert Rev Mol Diagn        ISSN: 1473-7159            Impact factor:   5.225


  18 in total

1.  A new approach for the analysis of mass spectrometry data for biomarker discovery.

Authors:  N Barbarini; P Magni; R Bellazzi
Journal:  AMIA Annu Symp Proc       Date:  2006

Review 2.  Quantitative matrix-assisted laser desorption/ionization mass spectrometry.

Authors:  Mark W Duncan; Heinrich Roder; Stephen W Hunsucker
Journal:  Brief Funct Genomic Proteomic       Date:  2008-09

3.  Combination of SELDI-TOF-MS and data mining provides early-stage response prediction for rectal tumors undergoing multimodal neoadjuvant therapy.

Authors:  Fraser M Smith; William M Gallagher; Edward Fox; Richard B Stephens; Elton Rexhepaj; Emanuel F Petricoin; Lance Liotta; M John Kennedy; John V Reynolds
Journal:  Ann Surg       Date:  2007-02       Impact factor: 12.969

Review 4.  Proteomic patterns as a diagnostic tool for early-stage cancer: a review of its progress to a clinically relevant tool.

Authors:  Thomas P Conrads; Brian L Hood; Haleem J Issaq; Timothy D Veenstra
Journal:  Mol Diagn       Date:  2004

5.  Link test--A statistical method for finding prostate cancer biomarkers.

Authors:  Xutao Deng; Huimin Geng; Dhundy R Bastola; Hesham H Ali
Journal:  Comput Biol Chem       Date:  2006-12       Impact factor: 2.877

6.  Candidate serological biomarkers for cancer identified from the secretomes of 23 cancer cell lines and the human protein atlas.

Authors:  Chih-Ching Wu; Chia-Wei Hsu; Chi-De Chen; Chia-Jung Yu; Kai-Ping Chang; Dar-In Tai; Hao-Ping Liu; Wen-Hui Su; Yu-Sun Chang; Jau-Song Yu
Journal:  Mol Cell Proteomics       Date:  2010-02-01       Impact factor: 5.911

7.  An LC-IMS-MS platform providing increased dynamic range for high-throughput proteomic studies.

Authors:  Erin Shammel Baker; Eric A Livesay; Daniel J Orton; Ronald J Moore; William F Danielson; David C Prior; Yehia M Ibrahim; Brian L LaMarche; Anoop M Mayampurath; Athena A Schepmoes; Derek F Hopkins; Keqi Tang; Richard D Smith; Mikhail E Belov
Journal:  J Proteome Res       Date:  2010-02-05       Impact factor: 4.466

8.  A list of candidate cancer biomarkers for targeted proteomics.

Authors:  Malu Polanski; N Leigh Anderson
Journal:  Biomark Insights       Date:  2007-02-07

9.  Dynamically weighted clustering with noise set.

Authors:  Yijing Shen; Wei Sun; Ker-Chau Li
Journal:  Bioinformatics       Date:  2009-12-09       Impact factor: 6.937

10.  Non-invasive proteomics-thinking about personalized breast cancer screening and treatment.

Authors:  Manuel Debald; Matthias Wolfgarten; Gisela Walgenbach-Brünagel; Walther Kuhn; Michael Braun
Journal:  EPMA J       Date:  2010-07-14       Impact factor: 6.543

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