Identification of Novel Genes in Human Airway Epithelial Cells associated with Chronic Obstructive Pulmonary Disease (COPD) using Machine-Based Learning Algorithms

Abstract

The aim of this project was to identify candidate novel therapeutic targets to facilitate the treatment of COPD using machine-based learning (ML) algorithms and penalized regression models. In this study, 59 healthy smokers, 53 healthy non-smokers and 21 COPD smokers (9 GOLD stage I and 12 GOLD stage II) were included (n = 133). 20,097 probes were generated from a small airway epithelium (SAE) microarray dataset obtained from these subjects previously. Subsequently, the association between gene expression levels and smoking and COPD, respectively, was assessed using: AdaBoost Classification Trees, Decision Tree, Gradient Boosting Machines, Naive Bayes, Neural Network, Random Forest, Support Vector Machine and adaptive LASSO, Elastic-Net, and Ridge logistic regression analyses. Using this methodology, we identified 44 candidate genes, 27 of these genes had been previously been reported as important factors in the pathogenesis of COPD or regulation of lung function. Here, we also identified 17 genes, which have not been previously identified to be associated with the pathogenesis of COPD or the regulation of lung function. The most significantly regulated of these genes included: PRKAR2B, GAD1, LINC00930 and SLITRK6. These novel genes may provide the basis for the future development of novel therapeutics in COPD and its associated morbidities.

m6A RNA methylation regulators could contribute to the occurrence of chronic obstructive pulmonary disease

“…This study identified 27 key genes associated with the occurrence of COPD by WGCNA and differential expression analysis in three groups of data sets. These findings are consistent with the previous results that MUCL1, UCHL1, CABYR, CYP1B1, AHRR, AKR1B10, SLC7A11, ST3GAL4‐AS1, GPX2, LOC344887, EGF, CLEC5A, CCL2, MMP12, PLA2G7, GAD1, CYP1A1 and SPP1 were up‐regulated in a variety of samples from COPD or COPD‐related mice model, including peripheral blood mononuclear cells, large and small airway epithelium, quadriceps, blood, and the lung of mouse and human, as well as involved in the occurrence of COPD (Table ) 15,34–48 . GO and KEGG pathway enrichment analyses showed that CYP1B1, CYP1A1, SLC7A11, AKR1B10, AHRR, ALDH3A1 and GPX2 were involved in multiple biological processes related to metabolism of exogenous and endogenous stimulus, and metabolism of xenobiotics by cytochrome P450 signalling pathway, suggesting that the dysregulated expression of these genes might affect the development processes of COPD by influencing the above biological processes and signalling pathway.…”

“…23 as well as involved in the occurrence of COPD (Table S2). 15,[34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] GO and KEGG pathway enrichment analyses showed that CYP1B1, In conclusion, for the first time, our study systematically demonstrated the expression and potential functions of m6A RNA methylation regulators in COPD. The expressions of IGF2BP3, FTO, METTL3…”

COVID‐19 Susceptibility in chronic obstructive pulmonary disease

“…Among the most significant, the authors stressed the novel PRKAR2B gene, which encodes an important protein kinase in cAMP signalling, a protective factor in the lung and COPD. Interestingly, PRKAR2B expression was significantly downregulated in COPD patients who smoked more than 50 packs per year 32 …”

“…Although the molecular basis for the amplification and ‘chronification’ of these alterations remains obscure, it is accepted that both genetic and epigenetic factors are involved 28 . In this sense, a recent study from Mostafaei and colleagues used machine‐based learning algorithms to find novel genes associated with COPD 32 . They identified 44 candidate genes whose expression was significantly regulated by smoking and/or COPD.…”

The identification of co-expressed gene modules in Streptococcus pneumonia from colonization to infection to predict novel potential virulence genes

“…Generally, Sample Progression Discovery (SPD) is a computational approach traditionally used to decipher biological progression trends and their corresponding gene module clusters in different clinical samples underlying a microarray dataset. This approach is used in progression-based diseases, including cancer, chronic pulmonary obstructive disease (COPD), and basic cellular processes, including cell differentiation [5]. The SPD framework tries to cluster genes into modules of co-expressed genes, construct modules' minimum spanning tree (MST), select modules corresponding to common MSTs, and, according to all genes of all selected modules, reconstruct a general MST [6].…”