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Title 

Feature genes of hepatitis B virus-positive hepatocellular carcinoma, established by its molecular discrimination approach using prediction analysis of microarray

Authors 

B Y KimJ G LeeS ParkJ Y AhnY J JuJ H ChungC J HanS H JeongYoung Il YeomSang Soo KimY S LeeC M KimE M EomD H LeeK Y ChoiM H ChoK S SuhD W ChoiK H Lee

Publisher 

Elsevier

Issue Date 

2004

Citation 

Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, vol. 1739, no. 1, pp. 50-61

Keywords 

hepatocellular carcinomalearningmicroarraypredictionclinical featureDNA microarrayhepatitis Bhepatitis B virusliver cell carcinoma

Abstract 

Recent introduction of a learning algorithm for cDNA microarray analysis has permitted to select feature set to accurately distinguish human cancers according to their pathological judgments. Here, we demonstrate that hepatitis B virus-positive hepatocellular carcinoma (HCC) could successfully be identified from non-tumor liver tissues by supervised learning analysis of gene expression profiling. Through learning and cross-validating HCC sample set, we could identify an optimized set of 44 genes to discriminate the status of HCC from non-tumor liver tissues. In an analysis of other blind-tested HCC sample sets, this feature set was found to be statistically significant, indicating the reproducibility of our molecular discrimination approach with the defined genes. One prominent finding was an asymmetrical distribution pattern of expression profiling in HCC, in which the number of down-regulated genes was greater than that of up-regulated genes. In conclusion, the present findings indicate that application of learning algorithm to HCC may establish a reliable feature set of genes to be useful for therapeutic target of HCC, and that the asymmetric expression pattern may emphasize the importance of suppressed genes in HCC.

ISSN 

0925-4439

Link 

http://dx.doi.org/10.1016/j.bbadis.2004.07.004

Appears in Collections

1. Journal Articles > Journal Articles

Registered Date

2017-04-19


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