Association of long non-coding RNA genes (H19, MEG3, MALAT1, Linc00305, Linc00261, Linc02227, and CDKN2B-AS1) polymorphic loci with chronic obstructive pulmonary disease

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Abstract

Chronic obstructive pulmonary disease (COPD) is a chronic lung disease resulting from dynamic, cumulative gene-environment interactions that cause lung tissue injury, alteration of its normal function and acceleration of cellular senescence. Long non-coding RNAs (lncRNAs) function as critical epigenetic regulators of various aspects of cellular senescence. The objective of the present study is to identify the association between polymorphic variants of H19 (rs3741219), MEG3 (rs7158663), MALAT1 (rs619586), LINC00305 (rs2850711), LINC00261 (rs6048205), CDKN2B-AS1 (rs4977574), and LINC02227 (rs2149954) lncRNAs genes with COPD. DNA samples from COPD patients (N = 703) and healthy individuals (N = 655) were studied in this study and polymorphic loci were analyzed by real–time PCR. Association with COPD was established with H19 (rs3741219), MEG3 (rs7158663), LINC02227 (rs2149954), MALAT1 (rs619586) and CDKN2B-AS1 (rs4977574). Polygenic analysis has allowed to identify informative gene-gene combinations that include polymorphic variants of the studied lncRNAs genes and genes encoding molecules of signaling cascades associated with cellular senescence and apoptosis. Multiple regression and ROC-analysis revealed a COPD risk predictive model, which included gene–gene combinations of lncRNAs genes and smoking index (P = 4.01 x 10-48, AUC = 0.87).

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About the authors

G. F. Korytina

Institute of Biochemistry and Genetics - Subdivision of the Ufa Federal Research Centre of the Russian Academy of Sciences; Bashkir State Medical University

Author for correspondence.
Email: guly_kory@mail.ru
Russian Federation, Ufa, 450054; Ufa, 450008

L. Z. Akhmadishina

Institute of Biochemistry and Genetics - Subdivision of the Ufa Federal Research Centre of the Russian Academy of Sciences; Ufa State Petroleum Technological University

Email: guly_kory@mail.ru
Russian Federation, Ufa, 450054; Ufa, 450064

V. A. Markelov

Institute of Biochemistry and Genetics - Subdivision of the Ufa Federal Research Centre of the Russian Academy of Sciences; Bashkir State Medical University

Email: guly_kory@mail.ru
Russian Federation, Ufa, 450054; Ufa, 450008

T. R. Nasibullin

Institute of Biochemistry and Genetics - Subdivision of the Ufa Federal Research Centre of the Russian Academy of Sciences

Email: guly_kory@mail.ru
Russian Federation, Ufa, 450054

Y. G. Aznabaeva

Bashkir State Medical University

Email: guly_kory@mail.ru
Russian Federation, Ufa, 450008

O. V. Kochetova

Institute of Biochemistry and Genetics - Subdivision of the Ufa Federal Research Centre of the Russian Academy of Sciences

Email: guly_kory@mail.ru
Russian Federation, Ufa, 450054

N. N. Khusnutdinova

Institute of Biochemistry and Genetics - Subdivision of the Ufa Federal Research Centre of the Russian Academy of Sciences

Email: guly_kory@mail.ru
Russian Federation, Ufa, 450054

A. P. Larkina

Institute of Biochemistry and Genetics - Subdivision of the Ufa Federal Research Centre of the Russian Academy of Sciences

Email: guly_kory@mail.ru
Russian Federation, Ufa, 450054

N. S. Zagidullin

Bashkir State Medical University

Email: guly_kory@mail.ru
Russian Federation, Ufa, 450008

T. V. Victorova

Bashkir State Medical University

Email: guly_kory@mail.ru
Russian Federation, Ufa, 450008

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2. Fig. 1. Study design.

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3. Fig. 2. Area under the curve (ROC analysis) for assessing the effectiveness of predictive regression models. AUC – area under the curve. Full characteristics of the models are presented in Table 5. Model 1 – AUC = 0.75 (sensitivity – 65.7%, specificity – 71.2%) includes only genetic markers; model 2 – AUC = 0.87 (sensitivity – 74.9%, specificity – 86.3%) includes genetic markers and smoking index.

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