Genome wide in silico SNP-tumor association analysis

Application of a New Probabilistic Model for Mining Implicit Associated Cancer Genes from OMIM and Medline

An important issue in current medical science research is to find the genes that are strongly related to an inherited disease. A particular focus is placed on cancer-gene relations, since some types of cancers are inherited. As bio-medical databases have grown speedily in recent years, an informatics approach to predict such relations from currently available databases should be developed. Our objective is to find implicit associated cancer-genes from biomedical databases including the literature database. Co-occurrence of biological entities has been shown to be a popular and efficient technique in biomedical text mining. We have applied a new probabilistic model, called mixture aspect model (MAM) [ 48 ], to combine different types of co-occurrences of genes and cancer derived from Medline and OMIM (Online Mendelian Inheritance in Man). We trained the probability parameters of MAM using a learning method based on an EM (Expectation and Maximization) algorithm. We examined the performance of MAM by predicting associated cancer gene pairs. Through cross-validation, prediction accuracy was shown to be improved by adding gene-gene co-occurrences from Medline to cancer-gene co-occurrences in OMIM. Further experiments showed that MAM found new cancer-gene relations which are unknown in the literature. Supplementary information can be found at http://www.bic.kyotou.ac.jp/pathway/zhusf/CancerInformatics/Supplemental2006.html

The Association between Gene-Environment Interactions and Diseases Involving the Human GST Superfamily with SNP Variants

Exposure to environmental hazards has been associated with diseases in humans. The identification of single nucleotide polymorphisms (SNPs) in human populations exposed to different environmental hazards, is vital for detecting the genetic risks of some important human diseases. Several studies in this field have been conducted on glutathione S-transferases (GSTs), a phase II detoxification superfamily, to investigate its role in the occurrence of diseases. Human GSTs consist of cytosolic and microsomal superfamilies that are further divided into subfamilies. Based on scientific search engines and a review of the literature, we have found a large amount of published articles on human GST super- and subfamilies that have greatly assisted in our efforts to examine their role in health and disease. Because of its polymorphic variations in relation to environmental hazards such as air pollutants, cigarette smoke, pesticides, heavy metals, carcinogens, pharmaceutical drugs, and xenobiotics, GST is considered as a significant biomarker. This review examines the studies on gene-environment interactions related to various diseases with respect to single nucleotide polymorphisms (SNPs) found in the GST superfamily. Overall, it can be concluded that interactions between GST genes and environmental factors play an important role in human diseases.

Intraindividual somatic variations in MTHFR gene polymorphisms in relation to colon cancer

AIM

MTHFR mediates the one carbon metabolism pathway. Two common genetic variants, C677T and A1298C, of MTHFR are associated with number of human diseases, including cancer, as well as being involved in the modulation of therapy outcome to antifolate drugs. To understand the distribution pattern of SNPs among different tissues of an individual, we examined MTHFR polymorphisms in normal and colon cancer tissues and compared the genotype frequencies in peripheral blood samples.

MATERIALS & METHODS

DNA was isolated from tumor tissue and matched normal tissues from 155 colon cancer patients. These samples as well as DNA from blood samples of the control group (n = 294) were analyzed for MTHFR polymorphisms by PCR-RFLP and confirmed by a direct DNA sequencing method.

RESULTS

Our data suggest that the allele and genotype frequencies of C677T and A1298C were significantly different between tumor tissues and both types of normal tissues. We have established that MTHFR variants that exist in tumor and matched normal tissues of colon cancer patients differ suggesting somatic variation in MTHFR polymorphisms among different tissues of an individual. The MTHFR A1298C polymorphism was associated with risk of colon cancer.

CONCLUSION

Different MTHFR variants may exist in different tissues to maintain physiological functions and may have implications for disease susceptibility and pharmacogenomics based therapies. Original submitted 21 January 2013; Revision submitted 3 January 2014.

Post-transcriptional exon shuffling events in humans can be evolutionarily conserved and abundant

In silico analyses have established that transcripts from some genes can be processed into RNAs with rearranged exon order relative to genomic structure (post-transcriptional exon shuffling, or PTES). Although known to contribute to transcriptome diversity in some species, to date the structure, distribution, abundance, and functional significance of human PTES transcripts remains largely unknown. Here, using high-throughput transcriptome sequencing, we identify 205 putative human PTES products from 176 genes. We validate 72 out of 112 products analyzed using RT-PCR, and identify additional PTES products structurally related to 61% of validated targets. Sequencing of these additional products reveals GT-AG dinucleotides at >95% of the splice junctions, confirming that they are processed by the spliceosome. We show that most PTES transcripts are expressed in a wide variety of human tissues, that they can be polyadenylated, and that some are conserved in mouse. We also show that they can extend into 5' and 3' UTRs, consistent with formation via trans-splicing of independent pre-mRNA molecules. Finally, we use real-time PCR to compare the abundance of PTES exon junctions relative to canonical exon junctions within the transcripts from seven genes. PTES exon junctions are present at <0.01% to >90% of the levels of canonical junctions, with transcripts from MAN1A2, PHC3, TLE4, and CDK13 exhibiting the highest levels. This is the first systematic experimental analysis of PTES in human, and it suggests both that the phenomenon is much more widespread than previously thought and that some PTES transcripts could be functional.

Genetic variations in plasma circulating DNA of HBV-related hepatocellular carcinoma patients predict recurrence after liver transplantation

BACKGROUND

Recurrence prediction of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients undergoing liver transplantation (LT) present a great challenge because of a lack of biomarkers. Genetic variations play an important role in tumor development and metastasis.

METHODS

Oligonucleotide microarrays were used to evaluate the genetic characteristics of tumor DNA in 30 HBV-related HCC patients who were underwent LT. Recurrence-related single-nucleotide polymorphism were selected, and their prognostic value was assessed and validated in two independent cohorts of HCC patients (N = 102 and N = 77), using pretransplant plasma circulating DNA. Prognostic significance was assessed by Kaplan-Meier survival estimates and log-rank tests. Multivariate analyses were performed to evaluate prognosis-related factors.

RESULTS

rs894151 and rs12438080 were significantly associated with recurrence (P = .003 and P = .004, respectively). Multivariate analyses demonstrated that the co-index of the 2 SNPs was an independent prognostic factor for recurrence (P = .040). Similar results were obtained in the third cohort (N = 77). Furthermore, for HCC patients (all the 3 cohorts) exceeding Milan criteria, the co-index was a prognostic factor for recurrence and survival (P<.001 and P = .002, respectively).

CONCLUSIONS

Our study demonstrated first that genetic variations of rs894151 and rs12438080 in pretransplant plasma circulating DNA are promising prognostic markers for tumor recurrence in HCC patients undergoing LT and identify a subgroup of patients who, despite having HCC exceeding Milan criteria, have a low risk of post-transplant recurrence.

Digital candidate gene approach (DigiCGA) for identification of cancer genes

The candidate gene approach is one of the most commonly used methods for identifying genes underlying disease traits. Advances in genomics have greatly contributed to the development of this approach in the past decade. More recently, with the explosion of genomic resources accessible via the public Web, digital candidate gene approach (DigiCGA) has emerged as a new development in this field. DigiCGA, an approach still in its infancy, has already achieved some primary success in cancer gene discovery. However, a detailed discussion concerning the applications of DigiCGA in cancer gene identification has not been addressed. This chapter will focus on discussing DigiCGA in a generalized sense and its applications to the identification of cancer genes, including the cancer gene resources, application status, platform and tools, challenges, and prospects.

Surface-enhanced Raman scattering based ligase detection reaction

Genomics provides a comprehensive view of the complete genetic makeup of an organism. Individual sequence variations, as manifested by single nucleotide polymorphisms (SNPs), can provide insight into the basis for a large number of phenotypes and diseases including cancer. The ability rapidly screen for SNPs will have a profound impact on a number of applications, most notably personalized medicine. Here we demonstrate a new approach to SNP detection through the application of surface-enhanced Raman scattering (SERS) to the ligase detection reaction (LDR). The reaction uses two LDR primers, one of which contains a Raman enhancer and the other a reporter dye. In LDR, one of the primers is designed to interrogate the SNP. When the SNP being interrogated matches the discriminating primer sequence, the primers are ligated and the enhancer and dye are brought into close proximity enabling the dye's Raman signature to be detected. By detecting the Raman signature of the dye rather than its fluorescence emission, our technique avoids the problem of spectral overlap which limits number of reactions which can be carried out in parallel by existing systems. We demonstrate the LDR-SERS reaction for the detection of point mutations in the human K-ras oncogene. The reaction is implemented in an electrokinetically active microfluidic device that enables physical concentration of the reaction products for enhanced detection sensitivity and quantization. We report a limit of detection of 20 pM of target DNA with the anticipated specificity engendered by the LDR platform.

Genome wide identification of recessive cancer genes by combinatorial mutation analysis

We devised a novel procedure to identify human cancer genes acting in a recessive manner. Our strategy was to combine the contributions of the different types of genetic alterations to loss of function: amino-acid substitutions, frame-shifts, gene deletions. We studied over 20,000 genes in 3 Gigabases of coding sequences and 700 array comparative genomic hybridizations. Recessive genes were scored according to nucleotide mismatches under positive selective pressure, frame-shifts and genomic deletions in cancer. Four different tests were combined together yielding a cancer recessive p-value for each studied gene. One hundred and fifty four candidate recessive cancer genes (p-value < 1.5 x 10(-7), FDR = 0.39) were identified. Strikingly, the prototypical cancer recessive genes TP53, PTEN and CDKN2A all ranked in the top 0.5% genes. The functions significantly affected by cancer mutations are exactly overlapping those of known cancer genes, with the critical exception for the absence of tyrosine kinases, as expected for a recessive gene-set.

Application of a new probabilistic model for mining implicit associated cancer genes from OMIM and medline

An important issue in current medical science research is to find the genes that are strongly related to an inherited disease. A particular focus is placed on cancer-gene relations, since some types of cancers are inherited. As biomedical databases have grown speedily in recent years, an informatics approach to predict such relations from currently available databases should be developed. Our objective is to find implicit associated cancer-genes from biomedical databases including the literature database. Co-occurrence of biological entities has been shown to be a popular and efficient technique in biomedical text mining. We have applied a new probabilistic model, called mixture aspect model (MAM) [48], to combine different types of co-occurrences of genes and cancer derived from Medline and OMIM (Online Mendelian Inheritance in Man). We trained the probability parameters of MAM using a learning method based on an EM (Expectation and Maximization) algorithm. We examined the performance of MAM by predicting associated cancer gene pairs. Through cross-validation, prediction accuracy was shown to be improved by adding gene-gene co-occurrences from Medline to cancer-gene cooccurrences in OMIM. Further experiments showed that MAM found new cancer-gene relations which are unknown in the literature. Supplementary information can be found at http://www.bic.kyotou.ac.jp/pathway/zhusf/CancerInformatics/Supplemental2006.html.

In silico whole-genome screening for cancer-related single-nucleotide polymorphisms located in human mRNA untranslated regions

BACKGROUND

A promising application of the huge amounts of genetic data currently available lies in developing a better understanding of complex diseases, such as cancer. Analysis of publicly available databases can help identify potential candidates for genes or mutations specifically related to the cancer phenotype. In spite of their huge potential to affect gene function, no systematic attention has been paid so far to the changes that occur in untranslated regions of mRNA.

RESULTS

In this study, we used Expressed Sequence Tag (EST) databases as a source for cancer-related sequence polymorphism discovery at the whole-genome level. Using a novel computational procedure, we focused on the identification of untranslated region (UTR)-localized non-coding Single Nucleotide Polymorphisms (UTR-SNPs) significantly associated with the tumoral state. To explore possible relationships between genetic mutation and phenotypic variation, bioinformatic tools were used to predict the potential impact of cancer-associated UTR-SNPs on mRNA secondary structure and UTR regulatory elements. We provide a comprehensive and unbiased description of cancer-associated UTR-SNPs that may be useful to define genotypic markers or to propose polymorphisms that can act to alter gene expression levels. Our results suggest that a fraction of cancer-associated UTR-SNPs may have functional consequences on mRNA stability and/or expression.

CONCLUSION

We have undertaken a comprehensive effort to identify cancer-associated polymorphisms in untranslated regions of mRNA and to characterize putative functional UTR-SNPs. Alteration of translational control can change the expression of genes in tumor cells, causing an increase or decrease in the concentration of specific proteins. Through the description of testable candidates and the experimental validation of a number of UTR-SNPs discovered on the secreted protein acidic and rich in cysteine (SPARC) gene, this report illustrates the utility of a cross-talk between in silico transcriptomics and cancer genetics.

Bioinformatic screening of human ESTs for differentially expressed genes in normal and tumor tissues

Background

Owing to the explosion of information generated by human genomics, analysis of publicly available databases can help identify potential candidate genes relevant to the cancerous phenotype. The aim of this study was to scan for such genes by whole-genome in silico subtraction using Expressed Sequence Tag (EST) data.

Methods

Genes differentially expressed in normal versus tumor tissues were identified using a computer-based differential display strategy. Bcl-xL, an anti-apoptotic member of the Bcl-2 family, was selected for confirmation by western blot analysis.

Results

Our genome-wide expression analysis identified a set of genes whose differential expression may be attributed to the genetic alterations associated with tumor formation and malignant growth. We propose complete lists of genes that may serve as targets for projects seeking novel candidates for cancer diagnosis and therapy. Our validation result showed increased protein levels of Bcl-xL in two different liver cancer specimens compared to normal liver. Notably, our EST-based data mining procedure indicated that most of the changes in gene expression observed in cancer cells corresponded to gene inactivation patterns. Chromosomes and chromosomal regions most frequently associated with aberrant expression changes in cancer libraries were also determined.

Conclusion

Through the description of several candidates (including genes encoding extracellular matrix and ribosomal components, cytoskeletal proteins, apoptotic regulators, and novel tissue-specific biomarkers), our study illustrates the utility of in silico transcriptomics to identify tumor cell signatures, tumor-related genes and chromosomal regions frequently associated with aberrant expression in cancer.

Signs of positive selection of somatic mutations in human cancers detected by EST sequence analysis

BACKGROUND

Carcinogenesis typically involves multiple somatic mutations in caretaker (DNA repair) and gatekeeper (tumor suppressors and oncogenes) genes. Analysis of mutation spectra of the tumor suppressor that is most commonly mutated in human cancers, p53, unexpectedly suggested that somatic evolution of the p53 gene during tumorigenesis is dominated by positive selection for gain of function. This conclusion is supported by accumulating experimental evidence of evolution of new functions of p53 in tumors. These findings prompted a genome-wide analysis of possible positive selection during tumor evolution.

METHODS

A comprehensive analysis of probable somatic mutations in the sequences of Expressed Sequence Tags (ESTs) from malignant tumors and normal tissues was performed in order to access the prevalence of positive selection in cancer evolution. For each EST, the numbers of synonymous and non-synonymous substitutions were calculated. In order to identify genes with a signature of positive selection in cancers, these numbers were compared to: i) expected numbers and ii) the numbers for the respective genes in the ESTs from normal tissues.

RESULTS

We identified 112 genes with a signature of positive selection in cancers, i.e., a significantly elevated ratio of non-synonymous to synonymous substitutions, in tumors as compared to 37 such genes in an approximately equal-sized EST collection from normal tissues. A substantial fraction of the tumor-specific positive-selection candidates have experimentally demonstrated or strongly predicted links to cancer.

CONCLUSION

The results of EST analysis should be interpreted with extreme caution given the noise introduced by sequencing errors and undetected polymorphisms. Furthermore, an inherent limitation of EST analysis is that multiple mutations amenable to statistical analysis can be detected only in relatively highly expressed genes. Nevertheless, the present results suggest that positive selection might affect a substantial number of genes during tumorigenic somatic evolution.

In silico whole-genome scanning of cancer-associated nonsynonymous SNPs and molecular characterization of a dynein light chain tumour variant

Last decade has led to the accumulation of large amounts of data on cancer genetics, opening an unprecedented access to the mapping of cancer genes in the human genome. Single-nucleotide polymorphisms (SNPs), the most common form of DNA variation in humans, emerge as an invaluable tool for cancer association studies. These genotypic markers can be used to assay how alleles of candidate genes correlate with the malignant phenotype, and may provide new clues into the genetic modifications that characterize cancer onset. In this cancer-oriented study, we detail an SNP mining strategy based on the analysis of expressed sequence tags among publicly available databases. Our whole-genome approach provides a comprehensive and unbiased description of nonsynonymous SNPs (nsSNPs) in tumoral versus normal tissues. To gain further insights into the possible relationships between genetic variation and altered phenotype, locations of a subset of nsSNPs were mapped onto protein domains known to be critical for protein function. Computational methods were also used to predict the potential impact of these cancer-associated nsSNPs on protein structure and function. We illustrate our approach through the detailed biochemical and structural characterization of a previously unknown cancer-associated mutation (G79C) affecting the 8 kDa dynein light chain (DNCL1).

Venn analysis as part of a bioinformatic approach to prioritize expressed sequence tags from cardiac libraries

OBJECTIVES

We needed to sort expressed sequence tags (ESTs) from human cardiac expression libraries.

DESIGN AND METHODS

We annotated DNA sequence text files of 35,152 cardiac ESTs using our search and annotation tool called Multiblast.pl. We generated lists of the most prevalent ESTs in each library, and using a novel Venn tool, we grouped ESTs that were common to all or exclusive to particular libraries.

RESULTS

Hypothetical protein KIAA0553 was expressed 120 times among 917 ESTs from an adult cardiac library (13.1%) compared only once among 8075 ESTs from fetal cardiac libraries (P < 10(-114)), this was confirmed using Northern analysis. We collated biochemical features of KIAA0553 and determined DNA polymorphism frequencies. We also used the Venn tool to specify genes that were uniquely expressed in hypertrophic cardiomyocytes.

CONCLUSIONS

Annotating ESTs and sorting them using Venn analysis can help specify new candidate disease genes from the current lists of "hypothetical proteins".

Bioinformatics and cancer target discovery

The convergence of genomic technologies and the development of drugs designed against specific molecular targets provides many opportunities for using bioinformatics to bridge the gap between biological knowledge and clinical therapy. Identifying genes that have properties similar to known targets is conceptually straightforward. Additionally, genes can be linked to cancer via recurrent genomic or genetic abnormalities. Finally, by integrating large and disparate datasets, gene-level distinctions can be made between the different biological states that the data represents. These bioinformatics approaches and their associated methodologies, which can be applied across a range of technologies, facilitate the rapid identification of new target leads for further experimental validation.