Literature DB >> 10952317

Molecular classification of cutaneous malignant melanoma by gene expression profiling.

M Bittner1, P Meltzer, Y Chen, Y Jiang, E Seftor, M Hendrix, M Radmacher, R Simon, Z Yakhini, A Ben-Dor, N Sampas, E Dougherty, E Wang, F Marincola, C Gooden, J Lueders, A Glatfelter, P Pollock, J Carpten, E Gillanders, D Leja, K Dietrich, C Beaudry, M Berens, D Alberts, V Sondak.   

Abstract

The most common human cancers are malignant neoplasms of the skin. Incidence of cutaneous melanoma is rising especially steeply, with minimal progress in non-surgical treatment of advanced disease. Despite significant effort to identify independent predictors of melanoma outcome, no accepted histopathological, molecular or immunohistochemical marker defines subsets of this neoplasm. Accordingly, though melanoma is thought to present with different 'taxonomic' forms, these are considered part of a continuous spectrum rather than discrete entities. Here we report the discovery of a subset of melanomas identified by mathematical analysis of gene expression in a series of samples. Remarkably, many genes underlying the classification of this subset are differentially regulated in invasive melanomas that form primitive tubular networks in vitro, a feature of some highly aggressive metastatic melanomas. Global transcript analysis can identify unrecognized subtypes of cutaneous melanoma and predict experimentally verifiable phenotypic characteristics that may be of importance to disease progression.

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Year:  2000        PMID: 10952317     DOI: 10.1038/35020115

Source DB:  PubMed          Journal:  Nature        ISSN: 0028-0836            Impact factor:   49.962


  427 in total

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2.  Chemosensitivity prediction by transcriptional profiling.

Authors:  J E Staunton; D K Slonim; H A Coller; P Tamayo; M J Angelo; J Park; U Scherf; J K Lee; W O Reinhold; J N Weinstein; J P Mesirov; E S Lander; T R Golub
Journal:  Proc Natl Acad Sci U S A       Date:  2001-09-11       Impact factor: 11.205

Review 3.  DNA microarrays in medical practice.

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4.  Correspondence analysis applied to microarray data.

Authors:  K Fellenberg; N C Hauser; B Brors; A Neutzner; J D Hoheisel; M Vingron
Journal:  Proc Natl Acad Sci U S A       Date:  2001-09-04       Impact factor: 11.205

5.  An assessment of Motorola CodeLink microarray performance for gene expression profiling applications.

Authors:  Ramesh Ramakrishnan; David Dorris; Anna Lublinsky; Allen Nguyen; Marc Domanus; Anna Prokhorova; Linn Gieser; Edward Touma; Randall Lockner; Murthy Tata; Xiaomei Zhu; Marcus Patterson; Richard Shippy; Timothy J Sendera; Abhijit Mazumder
Journal:  Nucleic Acids Res       Date:  2002-04-01       Impact factor: 16.971

6.  Expression profiling of human tumors: the end of surgical pathology?

Authors:  M Ladanyi; W C Chan; T J Triche; W L Gerald
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7.  Gene expression profiling of clear cell renal cell carcinoma: gene identification and prognostic classification.

Authors:  M Takahashi; D R Rhodes; K A Furge; H Kanayama ; S Kagawa; B B Haab; B T Teh
Journal:  Proc Natl Acad Sci U S A       Date:  2001-08-07       Impact factor: 11.205

8.  A highly reproducible, linear, and automated sample preparation method for DNA microarrays.

Authors:  David R Dorris; Ramesh Ramakrishnan; Dionisios Trakas; Frank Dudzik; Richard Belval; Connie Zhao; Allen Nguyen; Marc Domanus; Abhijit Mazumder
Journal:  Genome Res       Date:  2002-06       Impact factor: 9.043

9.  A high-throughput gene expression analysis technique using competitive PCR and matrix-assisted laser desorption ionization time-of-flight MS.

Authors:  Chunming Ding; Charles R Cantor
Journal:  Proc Natl Acad Sci U S A       Date:  2003-03-06       Impact factor: 11.205

10.  A classification-based machine learning approach for the analysis of genome-wide expression data.

Authors:  James Lyons-Weiler; Satish Patel; Soumyaroop Bhattacharya
Journal:  Genome Res       Date:  2003-03       Impact factor: 9.043

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