Gene Expression Trajectories from Normal Nonsmokers to COPD Smokers and Disease Progression Discriminant Modeling in
Zili Zhang1, Sifan Chen2, Qiongqiong Li1
1State Key Laboratory of Respiratory Diseases, Guangdong Key Laboratory of Vascular Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
Background:
Cigarette smoking (CS) is considered to the predominant risk factor contributing to the etiopathogenesis of chronic obstructive pulmonary disease (COPD); meanwhile, genetic predisposition likely plays a role in determining disease susceptibility.
Objectives:
We aimed to investigate gene expression trajectories from normal nonsmokers to COPD smokers and disease progression discriminant modeling in response to cigarette smoking.
Methods:
Small airway epithelial samples of human with different smoking status using fiberoptic bronchoscopy and corresponding rat lung tissues following 0, 3, and 6 months of CS exposure were obtained. The expression of the significant overlapping genes between human and rats was confirmed in 16HBE cells, rat lung tissues, and human peripheral PBMC using qRT-PCR. Binary logistic regression analysis was carried out to establish discrimination models.
Results:
The integrated bioinformatic analysis of 8 human GEO datasets (293 individuals) and 9 rat transcriptome databases revealed 13 overlapping genes between humans and rats in response to smoking exposure during COPD progression. Of these, 5 genes (AKR1C3/Akr1c3, ERP27/Erp27, AHRR/Ahrr, KCNMB2/Kcnmb2, and MRC1/Mrc1) were consistently identified in both the human and rat and validated by qRT-PCR. Among them, ERP27/Erp27, KCNMB2/Kcnmb2, and MRC1/Mrc1 were newly identified. On the basis of the overlapping gene panel, discriminant models were established with the receiver operating characteristic curve (AUC) of 0.98 (AKR1C3/Akr1c3 + ERP27/Erp27) and 0.99 (AHRR/Ahrr + KCNMB2/Kcnmb2) in differentiating progressive COPD from normal nonsmokers. In addition, we also found that DEG obtained from each expression profile dataset was better than combined analysis as more genes could be identified.
Conclusion:
This study identified 5 DEG candidates of COPD progression in response to smoking and developed effective and convenient discriminant models that can accurately predict the disease progression.
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