Browse

Protein expression profiling and molecular classification of gastric cancer by the tissue array method

DC Field Value Language
dc.contributor.authorLee, Hye Seung-
dc.contributor.authorCho, Sung-Bum-
dc.contributor.authorLee, Hee Eun-
dc.contributor.authorKim, Min A-
dc.contributor.authorKim, Ji Hun-
dc.contributor.authorPark, Do Joong-
dc.contributor.authorKim, Ju Han-
dc.contributor.authorYang, Han-Kwang-
dc.contributor.authorLee, Byung Lan-
dc.contributor.authorKim, Woo Ho-
dc.date.accessioned2009-09-20T09:16:54Z-
dc.date.available2009-09-20T09:16:54Z-
dc.date.issued2007-07-20-
dc.identifier.citationClin Cancer Res 2007;13:4154-63en
dc.identifier.issn1078-0432-
dc.identifier.urihttp://hdl.handle.net/10371/9670-
dc.description.abstractPURPOSE: Gastric cancer is heterogeneous clinically and histologically, and prognosis prediction by tumor grade or type is difficult. Although previous studies have suggested that frozen tissue-based molecular classifications effectively predict prognosis, prognostic classification on formalin-fixed tissue is needed, especially in early gastric cancer. EXPERIMENTAL DESIGN: We immunostained 659 consecutive gastric cancers using 56 tumor-associated antibodies and the tissue array method. Hierarchical cluster analyses were done before and after feature selection. To optimize classifier number and prediction accuracy for prognosis, a supervised analysis using a support vector machine algorithm was used. RESULTS: Of 56 gene products, 27 survival-associated proteins were selected (feature selection), and hierarchical clustering identified two clusters: cluster 1 and cluster 2. Cluster 1 cancers were more likely to have intestinal type, earlier stage, and better prognosis than cluster 2 (P<0.05). In 187 early gastric cancers (pT1), cluster 2 was associated with the presence of metastatic lymph nodes (P=0.026). Kaplan-Meier survival curves stratified by pathologic tumor-lymph node metastasis revealed that cluster 2 was associated with poor prognosis in stage I or II cancer (P<0.05). Support vector machines and genetic algorithms selected nine classifiers from the whole data set, another nine classifiers for stage I and II, and eight classifiers for stage III and IV. The prediction accuracies for patient outcome were 73.1%, 88.1%, and 76%, respectively. CONCLUSIONS: Protein expression profiling using the tissue array method provided a useful means for the molecular classification of gastric cancer into survival-predictive subgroups. The molecular classification predicted lymph node metastasis and prognosis in early stage gastric cancer.en
dc.description.sponsorshipGrant support: 21CFrontierFunctionalHumanGenomeProjectgrant FG06-11-03
fromtheMinistry of Science andTechnology of Korea.
The costs of publication of this articlewere defrayed in part by the payment of page
charges.This article must therefore be hereby marked advertisement in accordance
with18U.S.C. Section1734 solely to indicate this fact.
en
dc.language.isoenen
dc.publisherAmerican Association for Cancer Researchen
dc.titleProtein expression profiling and molecular classification of gastric cancer by the tissue array methoden
dc.typeArticleen
dc.contributor.AlternativeAuthor이혜승-
dc.contributor.AlternativeAuthor조성범-
dc.contributor.AlternativeAuthor이희은-
dc.contributor.AlternativeAuthor김민아-
dc.contributor.AlternativeAuthor김지훈-
dc.contributor.AlternativeAuthor박도중-
dc.contributor.AlternativeAuthor김주한-
dc.contributor.AlternativeAuthor양한광-
dc.contributor.AlternativeAuthor김우호-
dc.identifier.doi10.1158/1078-0432.CCR-07-0173-
Appears in Collections:
College of Medicine/School of Medicine (의과대학/대학원)Pathology (병리학전공)Journal Papers (저널논문_병리학전공)
Files in This Item:
There are no files associated with this item.
  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse