LS-CAP: an algorithm for identifying cytogenetic aberrations in hepatocellular carcinoma using microarray data.

Biological techniques such as Array-Comparative genomic hybridization (CGH), fluorescent in situ hybridization (FISH) and affymetrix single nucleotide pleomorphism (SNP) array have been used to detect cytogenetic aberrations. However, on genomic scale, these techniques are labor intensive and time consuming. Comparative genomic microarray analysis (CGMA) has been used to identify cytogenetic changes in hepatocellular carcinoma (HCC) using gene expression microarray data. However, CGMA algorithm can not give precise localization of aberrations, fails to identify small cytogenetic changes, and exhibits false negatives and positives. Locally un-weighted smoothing cytogenetic aberrations prediction (LS-CAP) based on local smoothing and binomial distribution can be expected to address these problems. LS-CAP algorithm was built and used on HCC microarray profiles. Eighteen cytogenetic abnormalities were identified, among them 5 were reported previously, and 12 were proven by CGH studies. LS-CAP effectively reduced the false negatives and positives, and precisely located small fragments with cytogenetic aberrations.

SNP genotyping by multiplex amplification and microarrays assay for forensic application.

OBJECTIVE: Research on the application feasibility of SNP genotyping for forensic identification by microarrays. METHODS: Oligonucleotide microarrays which could detect 34 different SNPs were used. After hybridization and washing, the arrays were scanned and fluorescence intensities analyzed using Microarray software. Population studies on 34 SNP loci were carried out in a sample of 109 unrelated Chinese Han individuals using oligonucleotide microarrays for genotype detection. The method was also applied to cases. RESULTS: According to the results of population studies, no deviations from Hardy-Weinberg equilibrium could be found. Among the 34 loci, 3 SNPs were low informative, 4 were medium informative and 27 were high informative. The combination discrimination power (CDP) of the 31 optimal polymorphic SNPs was 0.9999999999979. The matching probability was 2.13 x 10(-12). The average exclusion probability in paternity testing for duos was 0.9609. The average exclusion probability in paternity testing for trios was 0.9970. CONCLUSION: The data and case application demonstrated that SNP typing by oligonucleotide probe microarrays was a useful technique for paternity testing and individual identification. Combined with the 28 SNPs loci distributed on HLA-DRB1 and ABO genes, the combination discrimination power (CDP) was 0.9999999999999910. The matching probability was 9.02 x 10(-15). The average exclusion probabilities in duos and in trios were 0.9894 and 0.9992, respectively. It may be concluded that the 59 SNPs loci yield the same power in forensic identification as CODIS STRs currently used.

[Detecting genetic polymorphisms of CYP1 A1 and GSTM1 simultaneously with oligonucleotide microarray].

Cytochrome P4501A1 plays a major role in the bioactivation of a number of tobacco procarcinogens. Glutathione S-transferase( GSTM1), a member of the class of GST gene family, has been shown to be polymorphic because of gene deletion resulting in a failure to express the GSTM1 gene in 50% approximately 60% of individuals. Some CYP1 A1/GSTM1 null genotype combinations seem to predispose the lung, esophagus, and oral cavity of smokers to an even higher risk for cancer or DNA damage, requiring, however, confirmation. An easy and reliable oligonuleotide microarray approach validated through direct sequencing method is developed in order to accurately detect single nucleotide polymorphisms of CYP1 A1 gene and discriminate the presence and absence of GSTM1 gene. The m1 (Msp I) and m2 (Ile462Val) polymorphisms of CYP1 A1 gene and GSTM1 null genotype were also determined in a random population of 84 healthy, unrelated volunteers with developed microarray-based method. Of 84 cases, 47.6% were calssified as GSTM1 null, close to the published data. It's interesting that there lack three genotypes of m1 -m2 locus in the population: TT-AG, TT-GG and TC-GG. However, according to the data of the genotype frequencies independently happened at both m1 and m2 site, the combination frequencies of above three genotypes are 11.4%, 2.6%, and 3.1% respectively. Therefore we assume that the haplotypes of m1 -m2 are only T-A, C-A and C-G, but not T-G, as it were,there is no recombination happened between m1 site and m2 site. The frequencies of three haplotypes of T-A, C-A and C-G, calculated through corresponding genotypes, are 69.6%, 7.7% and 22.6% respectively.

[ABO genotyping by duplex amplification and oligonucleotide arrays assay].

OBJECTIVE: ABO genotyping for forensic identification by oligonucleotide chip. METHODS: Oligonucleotide microarrays which could detect 3 different SNPs in exon 6 and exon 7 for ABO genotyping were used. Population studies on ABO was carried out in a sample of 115 unrelated Chinese Han individuals. The method was also applied to cases. RESULTS: The technique could identify 6 genotypes of ABO system. According to the results of population studies, no significant deviations from Hardy-Weinberg equilibrium could be found. The observed and expected heterozygosities were 0.591 and 0.616 respectively. The polymorphic information content was 0.544. The average exclusion probabilities in buos and trios was 0.188 and 0.344 respectively. The discrimination power is 0.777. CONCLUSION: The data and case application demonstrated that ABO typing by oligonucleotide probe arrays was a useful technique for paternity testing and individual identification.

AIG1 is a novel Pirh2-interacting protein that activates the NFAT signaling pathway.

Pirh2 is an E3 ligase that negatively regulates p53 through both direct physical interaction and ubiquitin-mediated proteolysis. Here, we identified a novel Pirh2-interacting protein, AIG1, by yeast two-hybrid screening and confirmed its interaction with p53 both in vitro and in vivo. Quantitative real-time reverse transcription-PCR analysis showed that AIG1 expression levels were reduced in 50 out of 79 (63%) human hepatocellular carcinomas (HCCs) when compared to matched, non-cancerous liver tissue; levels were significantly different between HCCs with or without lymph node metastasis. Kaplan-Meier analysis indicated that the survival time of HCC patients down-regulated for AIG1 is much shorter than it is for patients up-regulated for AIG1 expression (p = 0.0313 as determined by the Log-rank test). Finally, AIG1 activated the nuclear factor of activated T cells (NFAT) signaling pathway in a dose-dependent manner when over-expressed in HEK293T cells. Our results suggest AIG1 could serve as a new biomarker for the diagnosis and prognostic evaluation of HCCs.

A novel hPirh2 splicing variant without ubiquitin protein ligase activity interacts with p53 and is down-regulated in hepatocellular carcinoma.

A novel splice variant of hPirh2, named hPirh2b, was isolated from human fetal liver cDNA library. hPirh2b has a 38-nucleotide deletion and encodes a 188-amino acid protein with a truncated RING-H2 domain. It shows no ubiquitin protein ligase activity. A low level of expression of hPirh2 was found both at transcriptional and translational level in human hepatocellular carcinoma (HCC) when compared to non-cancerous tissue. Statistical analysis showed that the low expression is associated with lack of differentiation of HCC. In direct binding studies hPirh2b bound p53 indicating that RING-H2 domain is not needed for this interaction.

Expression profile reveals novel prognostic biomarkers in hepatocellular carcinoma.

The purpose of this study was to identify and validate novel prognostic biomarkers in human hepatocellular carcinoma (HCC). We analyzed gene expression profiles not only between 33 HCCs and their corresponding noncancerous liver tissues, but also between 25 HCCs and pooled normal liver tissues using cDNA microarrays containing 12800 genes. Functional analysis of differentially expressed genes involved in HCC carcinogenesis and tumor progression revealed that up-regulated and down-regulated genes are mainly associated with cell cycle and immune response, respectively. We detected two regions of cytogenetic changes only in poorly-differentiated HCCs using the expression data. We identified a 9-gene expression signature, which was able to predict differentiation degree and survival of HCC samples. Among the 9 most discriminatory genes, minichromosome maintenance protein 2 (MCM2), a significantly up-regulated gene involved in cell cycle pathway, was selected for further analysis. Overexpression of MCM2 protein related to poor-differentiation in HCC was validated using tissue microarray-based immunohistochemistry containing 96 HCCs. Our studies show that the 9-gene expression signature may serve as promising prognostic biomarkers involved in hepatocarcinogenesis and tumor progression.

System biology analysis of cell cycle pathway involved in hepatocellular carcinoma.

To investigate genetic mechanisms of hepatocarcinogenesis and identify potential anticancer targets in hepatocellular carcinoma (HCC), we analyzed microarray gene expression profiles between 33 HCCs and their corresponding noncancerous liver tissues. Functional analysis of differentially-expressed genes in HCC indicated that cell cycle dysregulation plays an important role in hepatocarcinogenesis. Based on 14 differentially-expressed genes involved in cell cycle in HCC, we applied Structural Equation Modeling (SEM) to establish a potential genetic network which could assist understanding of HCC molecular mechanisms. siRNA-mediated knock-down of two significantly up-regulated genes, minichromosome maintenance protein 2 (MCM2) and cyclin B1 (CCNB1), in HCC cells (SMMC-7721 and QGY-7703) induced G2/M-phase arrest, apoptosis and antiproliferation in HCC. Some up-regulated cell cycle-related genes in HCC were down-regulated following specific depletion of MCM2 or/and CCNB1 in HCC cells, which might well validate and complement the reconstructed cell cycle network. This study may contribute to further disclose hepatocarcinogenesis mechanism through systematically analyzed the HCC-related-cell-cycle pathway. This study also shows that MCM2 and CCNB1 could be promising prognostic and therapeutic targets for HCC.