Link test - A statistical method for finding prostate cancer biomarkers

被引：21
作者：
Deng, Xutao [1 ]
Geng, Huimin
Bastola, Dhundy R.
Ali, Hesham H.
机构：
[1] Univ Nebraska, Coll Informat Sci & Technol, Omaha, NE 68182 USA
[2] Univ Nebraska, Med Ctr, Dept Pathol & Microbiol, Omaha, NE 68198 USA
[3] Univ Nebraska, Med Ctr, Dept Pediat, Omaha, NE 68198 USA
来源：
COMPUTATIONAL BIOLOGY AND CHEMISTRY | 2006年 / 30卷 / 06期
基金：
美国国家卫生研究院;
关键词：
microarray; mass spectrometry; biomarker; prostate cancer; text mining;
D O I：
10.1016/j.compbiolchem.2006.09.002
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
We present a new method, link-test, to select prostate cancer biomarkers from SELDI mass spectrometry and microarray data sets. Biomarkers selected by link-test are supported by data sets from both mRNA and protein levels, and therefore results in improved robustness. Link-test determines the level of significance of the association between a microarray marker and a specific mass spectrum marker by constructing background mass spectra distributions estimated by all human protein sequences in the SWISS-PROT database. The data set consist of both microarray and mass spectrometry data from prostate cancer patients and healthy controls. A list of statistically justified prostate cancer biomarkers is reported by link-test. Cross-validation results show high prediction accuracy using the identified biomarker panel. We also employ a text-mining approach with OMIM database to validate the cancer biomarkers. The study with link-test represents one of the first cross-platform studies of cancer biomarkers. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码：425 / 433
页数：9
相关论文
共 29 条
  • [1] Toward a human blood serum proteome - Analysis by multidimensional separation coupled with mass spectrometry
    Adkins, JN
    Varnum, SM
    Auberry, KJ
    Moore, RJ
    Angell, NH
    Smith, RD
    Springer, DL
    Pounds, JG
    [J]. MOLECULAR & CELLULAR PROTEOMICS, 2002, 1 (12) : 947 - 955
  • [2] Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments
    Baggerly, KA
    Morris, JS
    Coombes, KR
    [J]. BIOINFORMATICS, 2004, 20 (05) : 777 - U710
  • [3] The universal protein resource (UniProt)
    Bairoch, Amos
    Bougueleret, Lydie
    Altairac, Severine
    Amendolia, Valeria
    Auchincloss, Andrea
    Puy, Ghislaine Argoud
    Axelsen, Kristian
    Baratin, Delphine
    Blatter, Marie-Claude
    Boeckmann, Brigitte
    Bollondi, Laurent
    Boutet, Emmanuel
    Quintaje, Silvia Braconi
    Breuza, Lionel
    Bridge, Alan
    deCastro, Edouard
    Coral, Danielle
    Coudert, Elisabeth
    Cusin, Isabelle
    Dobrokhotov, Pavel
    Dornevil, Dolnide
    Duvaud, Severine
    Estreicher, Anne
    Famiglietti, Livia
    Feuermann, Marc
    Gehant, Sebastian
    Farriol-Mathis, Nathalie
    Ferro, Serenella
    Gasteiger, Elisabeth
    Gateau, Alain
    Gerritsen, Vivienne
    Gos, Arnaud
    Gruaz-Gumowski, Nadine
    Hinz, Ursula
    Hulo, Chantal
    Hulo, Nicolas
    Ioannidis, Vassilios
    Ivanyi, Ivan
    James, Janet
    Jain, Eric
    Jimenez, Silvia
    Jungo, Florence
    Junker, Vivien
    Keller, Guillaume
    Lachaize, Corinne
    Lane-Guermonprez, Lydie
    Langendijk-Genevaux, Petra
    Lara, Vicente
    Lemercier, Philippe
    Le Saux, Virginie
    [J]. NUCLEIC ACIDS RESEARCH, 2007, 35 : D193 - D197
  • [4] CGMIM: Automated text-mining of Online Mendelian Inheritance in Man (OMIM) to identify genetically-associated cancers and candidate genes
    Bajdik, CD
    Kuo, B
    Rusaw, S
    Jones, S
    Brooks-Wilson, A
    [J]. BMC BIOINFORMATICS, 2005, 6 (1)
  • [5] Cancer diagnosis using proteomic patterns
    Conrods, TP
    Zhou, M
    Petricoin, EF
    Liotta, L
    Veenstra, TD
    [J]. EXPERT REVIEW OF MOLECULAR DIAGNOSTICS, 2003, 3 (04) : 411 - 420
  • [6] Cormen T. H., 2001, Introduction to Algorithms, V2nd
  • [7] Getting to the (c)ore of knowledge: mining biomedical literature
    de Bruijn, B
    Martin, J
    [J]. INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2002, 67 (1-3) : 7 - 18
  • [8] Mass Spectrometry as a diagnostic and a cancer biomarker discovery tool - Opportunities and potential limitations
    Diamandis, EP
    [J]. MOLECULAR & CELLULAR PROTEOMICS, 2004, 3 (04) : 367 - 378
  • [9] Dijkstra E.W., 1959, Numerische mathematik, V1, P269, DOI DOI 10.1007/BF01386390
  • [10] Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
    Golub, TR
    Slonim, DK
    Tamayo, P
    Huard, C
    Gaasenbeek, M
    Mesirov, JP
    Coller, H
    Loh, ML
    Downing, JR
    Caligiuri, MA
    Bloomfield, CD
    Lander, ES
    [J]. SCIENCE, 1999, 286 (5439) : 531 - 537
123