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

Madankumar Ghatge, Jiny Nair, Ankit Sharma, Rajani Kanth Vangala
2018 Molecular Medicine Reports  
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
more » ... 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.
doi:10.3892/mmr.2018.8393 pmid:29328373 pmcid:PMC5802197 fatcat:2yejuhn6lraxnkavuxfichamgq