A Bioinformatics Approach for Deciphering the Pathogenic Processes of COPD

Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory problem which is generally found to be associated with smoking and environmental factors. However, long term COPD escalate into cardiovascular complications, and infection. It pathophysiology involves lungs alveoli, small, and large airways and disease phenotypes resembles bronchitis asthma, making its clinical diagnosis a challenging task. It is therefore a pressing need to elucidate its pathogenic mechanisms at the molecular level to identify drug targets, and biomarkers for COPD. To meet this end, various DNA-micro array studies have been conducted and gene expression datasets are available at NCBI-GEO database. However, microarray technology suffers with inherent problem of low-reproducibility due to study design, and biological variability. Integrative analysis of multiple datasets is an attractive and cost effective approach to obtain a robust gene-signature of the disease that can be utilized to unravel new biological insights using pathway- or network-based enrichment approach. In this study, I have carried out a statistical meta-analysis across seven microarray studies, comprising a total of 122 individuals. These gene expression profiles were extracted from various portions of lungs. I identify a Meta signature of COPD and further validated its robustness through expression search using CREED. Gene set enrichment method was used to identify enriched hallmark gene-sets implicated by COPD gene signature; furthermore, leading edge analysis was carried out to identify core subset of genes for their potential role as biomarkers or drug targets. I found enrichment of various stress- and immune -related cellular pathways and literature search expose some of the known pathophysioloigcal aspects of COPD