Brief. Bioinformatics - Similarity of markers identified from cancer gene expression studies: observations from GEO.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ method(1969) cluster(1462) data(1082) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ studi(2440) review(1878) systemat(933) }
{ data(1714) softwar(1251) tool(1186) }
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{ system(1050) medic(1026) inform(1018) }
{ motion(1329) object(1292) video(1091) }
{ general(901) number(790) one(736) }
{ research(1218) medic(880) student(794) }
{ activ(1138) subject(705) human(624) }
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{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
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{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
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{ intervent(3218) particip(2042) group(1664) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Gene expression profiling has been extensively conducted in cancer research. The analysis of multiple independent cancer gene expression datasets may provide additional information and complement single-dataset analysis. In this study, we conduct multi-dataset analysis and are interested in evaluating the similarity of cancer-associated genes identified from different datasets. The first objective of this study is to briefly review some statistical methods that can be used for such evaluation. Both marginal analysis and joint analysis methods are reviewed. The second objective is to apply those methods to 26 Gene Expression Omnibus (GEO) datasets on five types of cancers. Our analysis suggests that for the same cancer, the marker identification results may vary significantly across datasets, and different datasets share few common genes. In addition, datasets on different cancers share few common genes. The shared genetic basis of datasets on the same or different cancers, which has been suggested in the literature, is not observed in the analysis of GEO data.

Resumo Limpo

gene express profil extens conduct cancer research analysi multipl independ cancer gene express dataset may provid addit inform complement singledataset analysi studi conduct multidataset analysi interest evalu similar cancerassoci gene identifi differ dataset first object studi briefli review statist method can use evalu margin analysi joint analysi method review second object appli method gene express omnibus geo dataset five type cancer analysi suggest cancer marker identif result may vari signific across dataset differ dataset share common gene addit dataset differ cancer share common gene share genet basi dataset differ cancer suggest literatur observ analysi geo data

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