Both genetic and epigenetic mutations contribute to human cancers. Mutations in oncogenes, tumor suppressor genes, and DNA repair genes have been identified in human cancers. Epigenetic mutations such as methylation of tumor suppressor genes and DNA repair genes and loss of imprinting of IGF2 gene are also associated with cancers. The major goal of my research is to identify cancer genes and epigenetic markers for human cancers. Rapid progress in human genome project has significantly speeded up the discovery in genetic research and disease research. Sequence analysis and bioinformatics play a vital role in understanding genetic basis of human diseases. One of my major interest is to apply bioinformatics to genetic research. Bioinformatics approach to cancer gene discovery. We have undertaken a computational approach to systematically search for all human imprinted genes. We have decided to search for imprinting genes from a single nucleotide polymorphism (SNP) database containing all SNPs in expressed sequence tag (EST). The Bayesian statistics was used to estimate the genotype frequency. Significant reduction in the frequency of these libraries expressing both alleles suggests that the SNP is located in an imprinted gene. We have identified about 100 candiddate imprinted genes and are validating these result by experiments. Similar approach is also used to identify both mutations in the cancer genes and SNP that associates with cancers. We are interested in identifying genetic elements important for genomic imprinting and genomic instability. We are taking a number of approaches including comparative genome analysis and motif analysis to search for genetic elements. Informatics solution for research. We are also developing several tools for automating positional cloning and mutational analysis. We have developed programs to parse genes into exons and generated database for exons and their flanking intron sequences and mapped SNPs to these genetic elements. We are also developing the tool for analyzing gene expression and to construct comprehensive genetic networks.

Agency
National Institute of Health (NIH)
Institute
Division of Cancer Epidemiology And Genetics (NCI)
Type
Intramural Research (Z01)
Project #
1Z01CP010155-02
Application #
6556740
Study Section
(HGP)
Project Start
Project End
Budget Start
Budget End
Support Year
2
Fiscal Year
2001
Total Cost
Indirect Cost
Name
Cancer Epidemiology and Genetics
Department
Type
DUNS #
City
State
Country
United States
Zip Code
Lee, Maxwell P; Dunn, Barbara K (2008) Influence of genetic inheritance on global epigenetic states and cancer risk prediction with DNA methylation signature: challenges in technology and data analysis. Nutr Rev 66 Suppl 1:S69-72
Riss, Joseph; Khanna, Chand; Koo, Seongjoon et al. (2006) Cancers as wounds that do not heal: differences and similarities between renal regeneration/repair and renal cell carcinoma. Cancer Res 66:7216-24
Hu, Nan; Wang, Chaoyu; Hu, Ying et al. (2005) Genome-wide association study in esophageal cancer using GeneChip mapping 10K array. Cancer Res 65:2542-6
Lin, Wei; Yang, Howard H; Lee, Maxwell P (2005) Allelic variation in gene expression identified through computational analysis of the dbEST database. Genomics 86:518-27
Lee, Maxwell P; Howcroft, Kevin; Kotekar, Aparna et al. (2005) ATG deserts define a novel core promoter subclass. Genome Res 15:1189-97
Lee, Maxwell P (2005) Genome-wide analysis of allele-specific gene expression using oligo microarrays. Methods Mol Biol 311:39-47
Yang, Howard H; Lee, Maxwell P (2004) Application of bioinformatics in cancer epigenetics. Ann N Y Acad Sci 1020:67-76
Wang, Zhining; Fan, Hongtao; Yang, Howard H et al. (2004) Comparative sequence analysis of imprinted genes between human and mouse to reveal imprinting signatures. Genomics 83:395-401
Yang, Howard H; Hu, Ying; Buetow, Kenneth H et al. (2004) A computational approach to measuring coherence of gene expression in pathways. Genomics 84:211-7
Wang, Zhining; Lo, H Shuen; Yang, Howard et al. (2003) Computational analysis and experimental validation of tumor-associated alternative RNA splicing in human cancer. Cancer Res 63:655-7

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