A correlational meta-analytical study of transforming growth factor-β genetic polymorphisms as a risk factor for chronic obstructive pulmonary disease

BACKGROUND

Several studies have examined the association between transforming growth factor-β (TGF-β) genetic polymorphisms and chronic obstructive pulmonary disease (COPD) risk, but the results remained inconclusive and controversial.

AIMS

We aimed to examine the correlation between TGF-β genetic polymorphisms and COPD risk through a comprehensive meta-analysis. Additionally, changes in circulating TGF-β concentrations across genotypes of TGF-β genetic polymorphisms were analyzed.

METHODS

Literature search, quality assessment, and data extraction were completed independently and in duplicate. Data are expressed in odds ratio (OR) or weighted mean difference (WMD) with 95% confidence interval (CI).

RESULTS

A total of 12 articles, involving 14 independent studies and 7 170 participants, were meta-analyzed for the correlation of five polymorphisms (rs2241712, rs1800469, rs1982073, rs6957, and rs2241718) in TGF-β gene with COPD risk. Under the allele model, no statistical significance was observed for all polymorphisms associated with COPD risk. Subsidiary analyses indicated that country, COPD stage, and diagnosis of COPD were potential sources of between-study heterogeneity. Filled full plots revealed no missing studies for all studied polymorphisms, except rs1982073. Genotype-phenotype analyses showed that carriers of rs1800469 CT genotype had significantly higher concentrations of circulating TGF-β than those with CC genotype in COPD patients (WMD: 0.28 pg/ml, 95% CI: 0.01 to 0.56).

CONCLUSION

Our findings failed to support the candidacy of TGF-β gene in the development of COPD, whereas the contribution of TGF-β gene to COPD might be ethnicity- and stage-dependent.

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

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.