Weighted Gene Co-expression Network Analysis in COPD patients : Finding a Molecular Signature for Smoking

The effects of cigarette exposure on gene expression have been studied, showing that anywhere from tens to hundreds of genes are significantly altered by smoking. PBMCs stimulated with cigarette smoke extract were found to have 80 genes upregulated and 37 genes downregulated by 1.5-fold or greater after 8 hours; these genes were largely related to cell survival, including antioxidants, chaperones, folding proteins, and ubiquitin/proteosome pathway genes [1]. Charlesworth, et al. studied lymphocytes’ response to smoking in 297 individuals in the San Antonio Family Heart study, identifying 323 unique genes and 342 transcripts that were significantly correlated with smoking behavior at a FDR of 5% [2]. Other studies have been performed on buccal mucosa [3], B cells in women [4], small airway epithelium, and alveolar macrophages in healthy individuals [5]. Gene coexpression network analysis, a means for looking at the relationship between different gene transcripts, has also been used to study smoking, but mostly in lung cancer as opposed to other types of pulmonary pathology. Recently, these networks have been constructed in smoking patients with lung adenocarcinoma [6], finding a 7-gene signature for diagnosis and prognosis. Likewise, after defining candidate genes related to lung cancer survival, a coexpression network based on prediction logic for smoker group and non-smoker group was used to find a 6-gene signature for prognosis in lung cancer that had an accurate diagnosis with an accuracy of 73% [7]. These studies postulate that understanding the molecular signature may help with patient selection for adjuvant chemotherapy, thus providing an option for intervention. However, relatively less human expression studies have been published regarding COPD, another lungrelated injury. Ezzie, et al., compared subjects with COPD and smokers without COPD, finding differential expression of 70 miRNAs and 2667 mRNAs [8]. These results solely represent differential expression. Gene network studies are even more rare. Acquaah-Mensah, et al. studied human airway cells restricting to posited ontological categories: apoptosis, response to oxidative stress, and inflammatory response; the genes involved in these categories were used to generate a transcriptional regulatory network. These genes and previously studied differential expressed genes in COPD were used to find COPD susceptibility loci. Not surprisingly, they found a number of senescence-related genes were related to COPD, considering they restrict their analysis to sensecence-related concepts [9]. Gene networking has been used to study COPD in muscle, with small sample sizes of 12 and 18 healthy and COPD subjects [10]. However, no well-powered studies to our knowledge have used data from solely COPD patients to construct a gene network. In our study, we aim to use weighted gene coexpression network analysis to study smoking in COPD patients. By using PBMCs, we aim to identify a network based signature for smoking in those known to be critically affected. The rationale behind using PBMCs is that these cells can be easily harvested from future patients. Using network analysis provides two benefits: 1) numerically, this serves as a means for decreasing the inherent problem of multiple comparisons, as it is a data reduction strategy. 2) Using a systems biology perspective in identifying groups of coexpressed genes ideally would, in turn, identify pathways of genes that interact with one another, thereby being functionally linked.