CYP39A1基因甲基化水平与中国人群高原肺水肿的关系

IF 1.8 4区医学 Q3 PHARMACOLOGY & PHARMACY

Pharmacogenomics & Personalized Medicine Pub Date : 2023-01-01 DOI:10.2147/PGPM.S397862

Pingyi Wang, Hongyan Lu, Hao Rong, Yuhe Wang, Li Wang, Xue He, Dongya Yuan, Yongjun He, Tianbo Jin

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引用次数: 0

摘要

背景:高原肺水肿(HAPE)仍然是高海拔地区最常见的致命性疾病。DNA甲基化在HAPE进展中起着重要作用。本研究旨在探讨CYP39A1甲基化与HAPE之间的关系。方法:收集106例HAPE患者(53例HAPE患者)外周血样本，研究CYP39A1甲基化与HAPE的关系。采用Sequenom MassARRAY EpiTYPER平台检测CYP39A1启动子区DNA甲基化位点。结果:概率分析显示，CYP39A1_1_CpG_5和CYP39A1_3_CpG_21的甲基化概率在病例与对照组之间差异有统计学意义(p< 0.05)。甲基化水平分析显示，CYP39A1_1_CpG_2.3.4、CYP39A1_5_CpG_6.7和CYP39A1_5_CpG_9.10在HAPE中甲基化水平高于对照组(p< 0.05)。CYP39A1_3_CpG_21和CYP39A1_4_CpG_3在HAPE中的甲基化水平低于对照组(p< 0.05)。关联分析显示，CYP39A1_1_CpG_2.3.4 (OR 2.56, p= 0.035)、CYP39A1_5_CpG_6.7 (OR 3.99, p= 0.003)、CYP39A1_5_CpG_9.10 (OR 3.99, p= 0.003)、CYP39A1_5_CpG_16.17.18 (OR 2.53, p= 0.033)、CYP39A1_5_CpG_20 (OR 3.05, p= 0.031)与HAPE风险增加相关。而CYP39A1_1_CpG_5 (OR 0.33, p= 0.016)和CYP39A1_3_CpG_21 (OR 0.18, p= 0.005)在HAPE中具有保护作用。此外，年龄分层分析显示CYP39A1_1_CpG_5 (OR 0.16, p= 0.014)和CYP39A1_3_CpG_21 (OR 0.08, p= 0.023)对≤32岁人群HAPE有保护作用。CYP39A1_5_CpG_6.7 (OR 6.70, p= 0.008)和CYP39A1_5_CpG_9.10 (OR 6.70, p= 0.008)与32岁以上HAPE易感性增加相关。CYP39A1_3_CpG_21的诊断价值(AUC = 0.712, p< 0.001)显著优于其他CpG位点。结论:CYP39A1甲基化水平与中国人群HAPE发生风险相关，为HAPE的预防和诊断提供了新的视角。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

The Association of Methylation Level in the CYP39A1 Gene with High Altitude Pulmonary Edema in the Chinese Population.

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The Association of Methylation Level in the CYP39A1 Gene with High Altitude Pulmonary Edema in the Chinese Population.

Background: High altitude pulmonary edema (HAPE) is still the most common fatal disease at high altitudes. DNA methylation proceeds with an important role in HAPE progression. This study was designed to investigate the association between CYP39A1 methylation and HAPE.

Methods: Peripheral blood samples were enrolled from 106 participants (53 HAPE patients and 53 healthy subjects) to study the association of CYP39A1 methylation with HAPE. DNA methylation site in the promoter region of CYP39A1 was detected by Sequenom MassARRAY EpiTYPER platform.

Results: Probability analysis showed that the methylation probabilities of CYP39A1_1_CpG_5 and CYP39A1_3_CpG_21 are significant differences between the cases and controls (p< 0.05). The methylation level analysis indicated that CYP39A1_1_CpG_2.3.4, CYP39A1_5_CpG_6.7, and CYP39A1_5_CpG_9.10 were higher methylation in HAPE compared to the controls (p< 0.05). CYP39A1_3_CpG_21 and CYP39A1_4_CpG_3 exhibited a lower methylation level in HAPE than that in the controls (p< 0.05). The association analysis given that CYP39A1_1_CpG_2.3.4 (OR 2.56, p= 0.035), CYP39A1_5_CpG_6.7 (OR 3.99, p= 0.003), CYP39A1_5_CpG_9.10 (OR 3.99, p= 0.003), CYP39A1_5_CpG_16.17.18 (OR 2.53, p= 0.033), and CYP39A1_5_CpG_20 (OR 3.05, p= 0.031) are associated with an increased risk of HAPE. Whereas CYP39A1_1_CpG_5 (OR 0.33, p= 0.016) and CYP39A1_3_CpG_21 (OR 0.18, p= 0.005) have a protective role in HAPE. Besides, age-stratification analysis showed that CYP39A1_1_CpG_5 (OR 0.16, p= 0.014) and CYP39A1_3_CpG_21 (OR 0.08, p= 0.023) had a protective impact on HAPE in people aged ≤32 years. CYP39A1_5_CpG_6.7 (OR 6.70, p= 0.008) and CYP39A1_5_CpG_9.10 (OR 6.70, p= 0.008) were related to an increased susceptibility to HAPE aged >32 years. Moreover, the diagnostic value of CYP39A1_3_CpG_21 (AUC = 0.712, p< 0.001) was significantly better than other CpG sites.

Conclusion: The methylation level of CYP39A1 was associated with a risk of HAPE in the Chinese population, which provided new perspective for preventing and diagnosing of HAPE.

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来源期刊

Pharmacogenomics & Personalized Medicine Biochemistry, Genetics and Molecular Biology-Molecular Medicine

CiteScore

3.30

自引率

5.30%

发文量

110

审稿时长

16 weeks

期刊介绍： Pharmacogenomics and Personalized Medicine is an international, peer-reviewed, open-access journal characterizing the influence of genotype on pharmacology leading to the development of personalized treatment programs and individualized drug selection for improved safety, efficacy and sustainability. In particular, emphasis will be given to: Genomic and proteomic profiling Genetics and drug metabolism Targeted drug identification and discovery Optimizing drug selection & dosage based on patient''s genetic profile Drug related morbidity & mortality intervention Advanced disease screening and targeted therapeutic intervention Genetic based vaccine development Patient satisfaction and preference Health economic evaluations Practical and organizational issues in the development and implementation of personalized medicine programs.