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Integrative gene ontology and network analysis of coronary artery disease associated genes suggests potential role of ErbB pathway gene EGFR.

ABSTRACT: Coronary artery disease (CAD) is a major cause of mortality in India, more importantly the young Indians. Combinatorial and integrative approaches to evaluate pathways and genes to gain an improved understanding and potential biomarkers for risk assessment are required. Therefore, 608 genes from the CADgene database version 2.0, classified into 12 functional classes representing the atherosclerotic disease process, were analyzed. Homology analysis of the unique list of gene ontologies (GO) from each functional class gave 8 GO terms represented in 11 and 10 functional classes. Using disease ontology analysis 80 genes belonging to 8 GO terms, using FunDO suggested that 29 of them were identified to be associated with CAD. Extended network analysis of these genes using STRING version 9.1 gave 328 nodes and 4,525 interactions of which the top 5% had a node degree of ?75 associated with pathways including the ErbB signaling pathway with epidermal growth factor receptor (EGFR) gene as the central hub. Evaluation of EFGR protein levels in age and gender?matched 342 CAD patients vs. 342 control subjects demonstrated significant differences [controls=149.76±2.47 pg/ml and CAD patients stratified into stable angina (SA)=161.65±3.40 pg/ml and myocardial infarction (MI)=171.51±4.26 pg/ml]. Logistic regression analysis suggested that increased EGFR levels exhibit 3?fold higher risk of CAD [odds ratio (OR) 3.51, 95% confidence interval [CI] 1.96?6.28, P?0.001], upon adjustment for hypertension, diabetes and smoking. A unit increase in EGFR levels increased the risk by 2?fold for SA (OR 2.58, 95% CI 1.25?5.33, P=0.01) and 3.8?fold for MI (OR 3.82, 95% CI 1.94?7.52, P?0.001) following adjustment. Thus, the use of ontology mapping and network analysis in an integrative manner aids in the prioritization of biomarkers of complex disease.

SUBMITTER: Ghatge M

PROVIDER: S-EPMC5802197 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Project description:BackgroundAtopic dermatitis (AD) is a prevalent chronic inflammatory skin disease whose pathophysiology involves the interplay between genetic and environmental factors, ultimately leading to dysfunction of the epidermis. While several treatments are effective in symptom management, many existing therapies offer only temporary relief and often come with side effects. For this reason, the formulation of an effective therapeutic plan is challenging and there is a need for more effective and targeted treatments that address the root causes of the condition. Here, we hypothesise that modelling the complexity of the molecular buildup of the atopic dermatitis can be a concrete means to drive drug discovery.MethodsWe preprocessed, harmonised and integrated publicly available transcriptomics datasets of lesional and non-lesional skin from AD patients. We inferred co-expression network models of both AD lesional and non-lesional skin and exploited their interactional properties by integrating them with a priori knowledge in order to extrapolate a robust AD disease module. Pharmacophore-based virtual screening was then utilised to build a tailored library of compounds potentially active for AD.ResultsIn this study, we identified a core disease module for AD, pinpointing known and unknown molecular determinants underlying the skin lesions. We identified skin- and immune-cell type signatures expressed by the disease module, and characterised the impaired cellular functions underlying the complex phenotype of atopic dermatitis. Therefore, by investigating the connectivity of genes belonging to the AD module, we prioritised novel putative biomarkers of the disease. Finally, we defined a tailored compound library by characterising the therapeutic potential of drugs targeting genes within the disease module to facilitate and tailor future drug discovery efforts towards novel pharmacological strategies for AD.ConclusionsOverall, our study reveals a core disease module providing unprecedented information about genetic, transcriptional and pharmacological relationships that foster drug discovery in atopic dermatitis.

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Integrative gene ontology and network analysis of coronary artery disease associated genes suggests potential role of ErbB pathway gene EGFR.

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