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

Recent introduction of a learning algorithm for cDNA microarray analysis has permitted to select feature set to accurately distinguis: human cancers according to their pathological judgments. Here, we demonstrate that hepatitis B virus-positive hepatocellular carcinom (HCC) could successfully be identified from non-tumor liver tissues by supervised learning analysis of gene expression profiling. Throng learning and cross-validating HCC sample set, we could identify an optimized set of 44 genes to discriminate the status of HCC from nor tumor liver tissues. In an analysis of other blind-tested HCC sample sets, this feature set was found to be statistically significant, indicatin the reproducibility of our molecular discrimination approach with the defined genes. One prominent finding was an asymmetrical distributio pattern of expression profiling in HCC, in which the number of down-regulated genes was greater than that of up-regulated genes. I conclusion, the present findings indicate that application of learning algorithm to HCC may establish a reliable feature set of genes to b useful for therapeutic target of HCC, and that the asymmetric expression pattern may emphasize the importance of suppressed genes in HCC (C) 2004 Elsevier B.V. All rights reserved.