Gene expression microarray public dataset reanalysis in chronic obstructive pulmonary disease

Abstract

Chronic obstructive pulmonary disease (COPD) was classified by the Centers for Disease Control and Prevention in 2014 as the 3rd leading cause of death in the United States (US). The main cause of COPD is exposure to tobacco smoke and air pollutants. Problems associated with COPD include under-diagnosis of the disease and an increase in the number of smokers worldwide. The goal of our study is to identify disease variability in the gene expression profiles of COPD subjects compared to controls, by reanalyzing pre-existing, publicly available microarray expression datasets. Our inclusion criteria for microarray datasets selected for smoking status, age and sex of blood donors reported. Our datasets used Affymetrix, Agilent microarray platforms (7 datasets, 1,262 samples). We re-analyzed the curated raw microarray expression data using R packages, and used Box-Cox power transformations to normalize datasets. To identify significant differentially expressed genes we used generalized least squares models with disease state, age, sex, smoking status and study as effects that also included binary interactions, followed by likelihood ratio tests (LRT). We found 3,315 statistically significant (Storey-adjusted q-value <0.05) differentially expressed genes with respect to disease state (COPD or control). We further filtered these genes for biological effect using results from LRT q-value <0.05 and model estimates’ 10% two-tailed quantiles of mean differences between COPD and control), to identify 679 genes. Through analysis of disease, sex, age, and also smoking status and disease interactions we identified differentially expressed genes involved in a variety of immune responses and cell processes in COPD. We also trained a logistic regression model using the common array genes as features, which enabled prediction of disease status with 81.7% accuracy. Our results give potential for improving the diagnosis of COPD through blood and highlight novel gene expression disease signatures.

A resource for exploring the understudied human kinome for research and therapeutic opportunities

“…A similar analysis was performed for Chronic obstructive pulmonary disease (COPD), a common disease that progressively impairs a patients” ability to breathe and is the third leading cause of death in the US 65 . A study by Rogers et al 66 analyzed five COPD microarray datasets from Gene Expression Omnibus (GEO) and two COPD datasets from ArrayExpress to identify genes with significant differential expression in COPD. By comparing the expression of genes in COPD patients to gene expression in healthy individuals, Rogers et al identified genes significantly up and down regulated in COPD patients (adjusted p-value < 0.05).…”

“…To study possible roles for understudied kinases in pathophysiology, we mined associations between diseases and either gene mutations or changes in gene expression. We mined The Cancer Genome Atlas (TCGA), Accelerating Medicines Partnership - Alzheimer’s Disease (AMP-AD) dataset and a microarray dataset on changes in gene expression associated with chronic obstructive pulmonary disease (COPD; (Rogers et al, 2019)). COPD progressively impairs a patient’s ability to breathe and is the third leading cause of death in the US (National Center for Health Statistics (US), 2016).…”

“…We compared mRNA expression at the earliest stages of AD to late-stage disease in age matched samples (Figure 6C) and found that the understudied kinases ITPKB and PKN3 were among the five most upregulated kinases while NEK10 was substantially downregulated. A similar analysis was performed for COPD, based on a study by Rogers et al (Rogers et al, 2019) of five COPD microarray datasets from Gene Expression Omnibus (GEO) and two COPD datasets from ArrayExpress that aimed to identify genes with significant differential expression in COPD. By comparing the expression of genes in COPD patients to gene expression in healthy individuals, Rogers et al identified genes significantly up and down regulated in patients (adjusted p-value < 0.05).…”

“…Preprocessed dataset combining 5 datasets from GEO and 2 from ArrayExpress was downloaded from https://figshare.com/articles/Meta-analysis_of_Gene_Expression_Microarray_Datasets_in_Chronic_Obstructive_Pulmonary_Disease/8233175. Data was preprocessed as described in Rogers et al (Rogers et al, 2019). Raw expression data was processed by generalized least squares (GLS) weighted models to account for heterogeneity between datasets.…”

Recent evidence from omic analysis for redox signalling and mitochondrial oxidative stress in COPD

“…The other module correlated strongly with CD4+, CD8 + T cell-specific gene expression together with pathways related to DNA transcription and translation [71]. In addition, reanalysis of 7 publicly available blood microarray expression datasets from COPD patients identified 3,315 statistically significant DEGs and key pathways associated with Wnt signaling, cytokine-cytokine interactions, PI3K and MAPK signalling [72] (Fig. 2).…”