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.
Array-based hybridization and the serial anMys~s of gene expression (SAGE) are the most common approaches for highthroughput transcript anMysls. Each has advantages and disadvantages. The cDNA array a]i[ows rapid screening of a large number of samples but cannot detect unknown genes. ~n contrasL SAGE can detect those unknown genes or transcripts but is restricted to fewer samples. Combining these two methods could provide better highothroughput analysis that allows rapid screening of both prevlousiy known and unknown genes. For this, we have generated two cDNA microarrays (from human and plant systems) based on SAGE dMa. The results from both of these were anaHyzed for thei~ correlation and accuracy. One specialized cDNA mlcroarray, putatively named Gastrficchlp, was constructed with ~744 probes, h~ciuding 858 cDNA fragments based on SAGE data from gastr[ocancer tissues. The ,ether microarray, putMive[y named Cotdst~esschlp, was constructed with 1482 probes, including 1209 cDNA fragmen% based on SAGE dMa from cold-stressed Arabidopsls. In particular, identity of the genes on both sets of data is assured and hyb~idizMion for cDNA microarray is efficienL
The genetic background of the garlic (AlBum sativum L) is not well understood, since it is cultivated exclusively by vegetative propagation. To understand its genetic background , a local cultivar, Danyans, w&s chosen, and several basic characteristics of its chromosomal DNA were examined. Its G + C content was 40.6%, and the relative proportion of fast reassociated sequences, intermediate reassociated sequences, and slow reassociated sequences were 12%, 40%, and 48%, respectively. The genome size, calculated based on reassociation kinetic experiments, was 1.11 x 10 ~~ bp or 12.16 P8 per haploid genome. To compare the genetic variation among four local cultivars, Munkyuns, Seosan, Euiseon8, and Danyang, random amplified polymorphic DNA (RAPD) analysis was performed. By using slightly longer primers, 18-24 nucleotides in size, than the traditional primers used for such analysis, more reliable RAPD results were obtained. 15 primers gave rise to amplified bandst and the results could be grouped into two catesories. The patterns of amplified products produced by 12 primers, group A, were polymorphic. These results were analyzed using a NTSYS-PC (Numerical Taxonomy and Multivariate Analysis Systemb and a dendrosram grouping the four local cultivars was produced. The three primers of group B gave rise h~ a monomorphic band pattern from four local garlic clutivars, indicating that these primers possibly recognize garlic specific sequences. These primers were useful in identifying genetic variations among the Allium species.
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