Browsing by Author "Pradhan, Meeta P."

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    CLIQUES FOR IDENTIFICATION OF GENE SIGNATURES FOR COLORECTAL CANCER ACROSS POPULATION
    (Office of the Vice Chancellor for Research, 2012-04-13) Pradhan, Meeta P.; Nagulapalli, Kshithija; Palakal, Mathew J.
    Introduction: Colorectal cancer (CRC) is one of the most common cancers diagnosed worldwide. Studies have correlated CRC with dietary habits and environmental conditions. We developed a novel network based approach where cliques and their connectivity profiles explained the variation and similarity in CRC across four populations- China, Germany, Saudi Arabia and USA. Methods: Networks generated after data preprocessing were analyzed individually based on topological and biological features. Using greedy algorithm, cliques of various sizes were identified in each network and size 7 cliques were further analyzed based on their clique connectivity profile (CCP). Our algorithm considered the interaction of cliques based on two parameters: (i) Identification of common (links) genes; (ii) CliqueStrength. The cliques were evaluated by two conditions (a) Maximum number of common genes across cliques and highest CliqueStrength and (b) Minimum number of common genes across cliques and highest CliqueStrength. Results: Large numbers of genes are found to be common between USA, China and Germany. Highly scored nodes based on topological parameters are TP53, SRC, ESR1, SMAD3, GRB2, CREBBP, EGFR, SMAD2, and CSN2KA1. Signal transduction, protein phosphorylation etc., were found to be important GO biological processes. Number of unique size 7 cliques identified in all the population is 650. 49 common cliques identified included genes- EGFR, GRB2, PIK3R1, PTPN6, BRCA1, SMAD2, TP53, CSN2 etc. We found 20 cliques that are uniquely identified for USA, 10 for Germany and one for China. Cliques include genes that are both well studied, less-studied in CRC; but are targets in other cancers. Conclusion: With CCP, we were able to identify commonality, uniqueness and divergence among the populations. Furthermore, comparing all cliques (their CCP) as gene-signatures across populations can help to identify efficient drug targets. Results were consistent with other studies and demonstrate the power of cliques to study CRC across populations.
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    Systems biology approach to stage-wise characterization of epigenetic genes in lung adenocarcinoma
    (Springer Nature, 2013-12-26) Pradhan, Meeta P.; Desai, Akshay; Palakal, Mathew J.; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Background: Epigenetics refers to the reversible functional modifications of the genome that do not correlate to changes in the DNA sequence. The aim of this study is to understand DNA methylation patterns across different stages of lung adenocarcinoma (LUAD). Results: Our study identified 72, 93 and 170 significant DNA methylated genes in Stages I, II and III respectively. A set of common 34 significant DNA methylated genes located in the promoter section of the true CpG islands were found across stages, and these were: HOX genes, FOXG1, GRIK3, HAND2, PRKCB, etc. Of the total significant DNA methylated genes, 65 correlated with transcription function. The epigenetic analysis identified the following novel genes across all stages: PTGDR, TLX3, and POU4F2. The stage-wise analysis observed the appearance of NEUROG1 gene in Stage I and its re-appearance in Stage III. The analysis showed similar epigenetic pattern across Stage I and Stage III. Pathway analysis revealed important signaling and metabolic pathways of LUAD to correlate with epigenetics. Epigenetic subnetwork analysis identified a set of seven conserved genes across all stages: UBC, KRAS, PIK3CA, PIK3R3, RAF1, BRAF, and RAP1A. A detailed literature analysis elucidated epigenetic genes like FOXG1, HLA-G, and NKX6-2 to be known as prognostic targets. Conclusion: Integrating epigenetic information for genes with expression data can be useful for comprehending in-depth disease mechanism and for the ultimate goal of better target identification.