Browsing by Subject "Lung Adenocarcinoma"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Identification of Immuno-Oncology Crosstalk Pathways in Lung Adenocarcinoma
    (Office of the Vice Chancellor for Research, 2016-04-08) Sudha, Parvathi; Pradhan, Meeta; Palakal, Mathew
    Identifying dysregulated pathways from the high throughput data for biomarker detection is the rate limiting step in the complex diseases cure. Pathways don’t perform alone; they interact with each other through the overlapping genes. This phenomenon is known as crosstalk of pathways. The aim of the study is develop a methodology to find the highly interacting (cross-talk) immuneoncological pathways and their drug-gene-pathway modules which can be further validated invivo using Lung Adenocarcinoma (LUAD) as a case study. The reference pathway cross-talk matrix is built using the KEGG Knowledgebase, which consists of the 302 KEGG pathways associated with 6996 genes. The LUAD gene expression data available in The Cancer Genome Atlas (TCGA) is used for the study. The data of 32 patients was used in the study and of these, 9 patients were treated with immunotherapy drugs. A set of 3018 significant genes associated with 296 pathways [C.I. =95%, p-value <=0.05] are identified in this dataset, and a disease crosstalk matrix is constructed. Each cell in the matrix gives the cross-talk score of the pathways computed using the formula: 􀵫􀯉􀳔∩􀯉􀳕􀵯􀳎􀳚􀳘􀳘􀳚􀳙 􀳒􀳐􀳙􀳐􀳞 􀵫􀯉􀳔∪􀯉􀳕􀵯􀳠􀳙􀳔􀳜􀳠􀳐􀳞 􀳒􀳐􀳙􀳐􀳞. The interaction among the significant genes (3018 genes) in the crosstalk pathways were identified using the BioGrid physical gene-gene interaction map and a gene interaction network (10102 interaction) is generated. The significant genes in the network are annotated to their drugs as given in the clinical data of TCGA. The drug-genepathway modules of LUAD are identified using Seed-Based-Network Propagation Algorithm. These modules give the profile of the highest cross-talk pathways of LUAD that can be studied further for alternative drug targets. The study identified T-cell receptor signaling pathway and B cell receptor signaling pathway of LUAD have high crosstalk scores with Erbb Signaling pathway (18.67, 15.15) Vegf signaling pathway (17.77, 22.45); Osteoclast differentiation (16.35, 14.89).
  • Thumbnail Image
    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, Mathew
    Introduction: 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.