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.
|Number of pages||12|
|Journal||Biochimica et Biophysica Acta - Molecular Basis of Disease|
|Publication status||Published - 2004 Dec 24|
Bibliographical noteFunding Information:
We thank Dr. Woon-Ki Paik for critical reading of the manuscript. This work was supported by grant FG00-0101-001-2-1-0 Frontier Functional Human Genome Project from the Ministry of Science and Technology of Korea. K.H.L. is supported by a grant from Nuclear National R&D program from the Ministry of Science and Technology of Korea. K.Y. Choi is supported by a grant from The Korea Science and Engineering Foundation through the Protein Network Research Center at Yonsei University.
All Science Journal Classification (ASJC) codes
- Molecular Medicine
- Molecular Biology