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Browsing by Author "Pradham, Meeta"
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Item Bridging the Phenotype-Genotype Gap for disease prognosis(Office of the Vice Chancellor for Research, 2013-04-05) Palakal, Mathew; Pradham, Meeta; Sakhare, ShrutiA well-known question we are trying to solve since past two decades is “What is the relationship between genotypes and phenotypes?”. Currently, methods such as Genome Wide Association Studies (GWAS) and Gene Regulatory Networks (GRNs) are used to find these phenotype and genotype relationships using statistics and molecular biology respectively. These studies mainly focus on studying limited phenotypes for direct mapping. However it has been reported that disease traits are outcome of many interdependent changes in phenotype. Our study aims to use the extensive clinical and genotype data from publicly available databases to study this interdependency of clinical outcomes and the corresponding changes at gene expression pattern. The present work of understanding genotype-phenotype relationship across different stages is designed based on the available TCGA data for breast cancer. The clinical features were identified and classified based on the laboratory and other clinical parameters. We selected 60 phenotypes based on their importance reported in literature and these were clustered for their significance for cancer prognosis and their expression at different stages. Multivariate statistical analysis is performed for the outliers from the clusters to identify the interdependency of their expression. An expression profile of these outliers is obtained based on the analysis performed. The analysis shows the significant phenotypes expressed in different stages of breast cancer. Some of these significant phenotypes are the ones, previously reported for breast cancer prognosis. However, the clustering analysis identified new phenotypes that may play a significant role in breast cancer prognosis. Correlation study for these parameters can then identify relational expression of multiple clinical traits. Following this study, these genotype features will be analyzed for their SNP, CNV variants for these parameters to bridge the genotype-phenotype gap. By successfully identifying the molecular changes at gene level for such phenotypic diversity of clinical traits it can be made possible to predict the onset of disease at an early stage. Current methodology can then be extended for other disease studies.Item A Systematic Analysis of Epigenetic Genes across Different Stages of Lung Adenocarcinoma(Office of the Vice Chancellor for Research, 2013-04-05) Desai, Akshay; Pradham, Meeta; Palakal, MathewIntroduction: Epigenetic refers to the reversible functional modifications of the genome that do not correlate to changes in the DNA sequence. Hence identifying these epigenetic targets contributing to the cancers and modifying them might provide a new approach to successful drug therapies. The aim of our study is to understand DNA methylation patterns across different stages of lung adenocarcinoma (LUAD). Method: An integrative system biology approach was developed to combine gene-expression, DNA methylation and protein-protein interaction data to obtain the targets for LUAD. The expression and methylation data was downloaded from TCGA. Statistical analysis was performed to further obtain the differentially expressed and significant methylated genes. An integrated network of these significant genes was constructed using BioGRID. Seed and expand approach was then used to identify and analyze epigenetically relevant subnetworks. Results: Our study identified 72, 93 and 170 significant methylated genes in Stage I, II and III respectively of LUAD. Variable methylation patterns were found for the significant genes across the different stages. Chromosomal analysis discovered that most of the methylated genes were distributed across chromosomes 7, 8, and 7 for Stage I, II and III respectively. Functionally conserved subnetworks of DNA methylation were obtained and compared across stages. This comparison showed a pattern of seven functionally conserved genes, mostly belonging to the KRAS pathway. Validation of the results was based on literature review which identified NEFM (beta value 0.36), NMUR2 (beta value 0.28), NEUROG1 (beta value -0.26) and IVL (beta value -0.26) as novel methylated LUAD genes. Conclusion: A distinct methylation pattern exists across stages which can help to characterize LUAD. Several tumor oncogenes and transcription factors were identified in the epigenetically relevant subnetworks, indicating that methylation affects the tumor progression. Methylated genes identified in this study can be further evaluated for their use as potential drug targets.