2719

GPC3

DGSX|GTR2-2|MXR7|OCI-5|SDYS|SGB|SGBS|SGBS1;glypican 3;GPC3;glypican 3

glypican proteoglycan 3|glypican-3|heparan sulphate proteoglycan|intestinal protein OCI-5|secreted glypican-3

Xq26.1

protein-coding

Using Affymetrix oligonucleotide microarrays, we analyzed mRNA gene expression patterns of 12 primary pediatric rhabdomyosarcomas (RMS) and 11 Ewing's sarcomas(EWS), which belong to the small round blue cell tumors (SRBCTs). Diagnostic classification of these cancers is frequently complicated by the highly similar appearance in routine histology, and additional molecular markers could significantly improve tumor classification. A combination of three independent statistical approaches (t-test, SAM, k-nearest neighborhood analysis) resulted in 101 highly significant probe sets that clearly discriminate between EWS and RMS.We identified novel marker transcripts that have not been previously associated with either RMS or EWS yet, including CITED2, glypican 3 (GPC3), and cyclin D1 (CCND1). expression levels for selected candidate genes were validated by quantitative real-time reverse-transcription PCR. Furthermore, to identify biologically meaningful trends, functional annotations were assigned to 946 genes differentially expressed between EWS and RMS (t-test). Genes involved in proteinbiosynthesis (n = 28) and complex assembly (n = 9), lipid metabolism (n = 23), energy generation (n = 22), and mRNA processing (n = 11) were expressed significantly higher in EWS. Thus, functional annotation of tumor-specific genesreveals detailed insights into tumor biology and differentiation-specific expression patterns and gives important clues related to the possible cellular origin of these pediatric tumors. Supplementary material for this article is available at the International Journal of cancer website at http://www.interscience.wiley.com/jpages/0020-7136/suppmat/index.html.#CI- Copyright 2004 Wiley-Liss, Inc.

BACKGROUND: Molecular profiling generates abundance measurements for thousands of gene transcripts in biological samples such as normal and tumor tissues (data points). Given such two-class high-dimensional data, many methods have been proposed for classifying data points into one of the two classes. However, finding very small sets of features able to correctly classify the data is problematic as the fundamental mathematical proposition is hard. Existing methods can find "small" feature sets, but give no hint how close this is to the true minimum size. Without fundamental mathematical advances, finding true minimum-size sets will remain elusive, and more importantly for the microarray community there will be no methods for finding them. RESULTS: We use the brute force approach of exhaustive search through ALL genes, gene pairs (and for some data sets gene triples). Each unique gene combination is analyzed with a few-parameter linear-hyperplane classification method looking for those combinations that form training error-free classifiers. ALL 10 published data sets studied are found to contain predictive small feature sets. Four contain thousands of gene pairs and 6 have single genes that perfectly discriminate. CONCLUSION: This technique discovered small sets of genes (3 or less) in published data that form accurate classifiers, yet were not reported in the prior publications. This could be a common characteristic of microarray data, thus making looking for them worth the computational cost. Such small gene sets couldindicate biomarkers and portend simple medical diagnostic tests. We recommend checking for small gene sets routinely. We find 4 gene pairs and many gene triples in the large hepatocellular carcinoma (HCC, Liver cancer) data set of Chen et al. The key component of these is the "placental gene of unknown function", PLAC8. Our HMM modeling indicates PLAC8 might have a domain like partof lP59's crystal structure (a Non-Covalent Endonuclease lii-Dna Complex). The previously identified HCC biomarker gene, glypican 3 (GPC3), is part of an accurate gene triple involving MT1E and ARHE. We also find small gene sets that distinguish leukemia subtypes in the large pediatric acute lymphoblastic leukemia cancer set of Yeoh et al.

OBJECTIVE: To investigate the expression features of glypican-3 (GPC-3) and its diagnostic and differential values in hepatocellular carcinoma (HCC). METHODS: Rat hepatoma models were made and the dynamic expression features of GPC-3 protein and its gene were investigated by Western blotting and RT-PCR respectively. Liver specimens from 36 HCC patients were collected by self-control method and the expression and clinicopathological features of GPC-3 were analyzed by immunohistochemistry. Serum GPC-3 levels were quantitatively detected by ELISA and its efficiency for HCC diagnosis was evaluated in patients with liver diseases. RESULTS: The incidence of GPC-3 was 0% in control, 83.3% in degeneration, 100% in precancerosis and 100% in canceration during dynamic formation of rat hepatoma, respectively. The positive GPC-3 was brown granule- like staining localized in membrane and cytoplasm in human HCC. CONCLUSIONS: TheGPC-3 positive rates were 80.6% in HCC, 41.7% in surrounding tissues and none indistal tissues (P < 0.01), respectively. No positive relationship presented between GPC-3 and differentiation grade or the number of tumor except of tumor size (Z = 2.941, P < 0.01). The incidence of serum GPC-3 was 52.8% in HCC patients except of one patient with cirrhosis. No significant differences were found between GPC-3 and sex, age, AFP, tumor number, child classification or extrahepatic metastasis except of tumor size (chi(2) = 6.318, P < 0.05) and HBV infection (chi(2) = 23.362, P < 0.01). Combined detection of GPC-3 and AFP couldrise up diagnosis of HCC. GPC-3 expression closely associated with HCC and mightbe useful for early diagnosis of HCC.