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Integrative RNA-sequencing analysis of COPD-related genes in association with individual PM2.5 exposure

Authors
 Kim, Jeeyoung  ;  Song, Ha Won  ;  Lee, Hyun Woo  ;  Lee, Ye Jin  ;  Sin, Sooim  ;  Lee, Ji Yeon  ;  Kim, Junghyun  ;  Choi, Sun Mi  ;  Kim, Kyoung-Nam  ;  Lee, Chang-Hoon  ;  Lee, Chang Hyun  ;  Kim, Woo Jin 
Citation
 ENVIRONMENTAL RESEARCH, Vol.285(Pt2), 2025-11 
Article Number
 122377 
Journal Title
ENVIRONMENTAL RESEARCH
ISSN
 0013-9351 
Issue Date
2025-11
MeSH
Aged ; Air Pollutants* / adverse effects ; Air Pollutants* / analysis ; Air Pollutants* / toxicity ; Air Pollution ; Environmental Exposure* ; Female ; Humans ; Male ; Middle Aged ; Particulate Matter* / adverse effects ; Particulate Matter* / analysis ; Particulate Matter* / toxicity ; Pulmonary Disease, Chronic Obstructive* / chemically induced ; Pulmonary Disease, Chronic Obstructive* / genetics ; Sequence Analysis, RNA
Keywords
COPD ; RNA sequencing ; Lung function ; Biomarker ; PM2.5
Abstract
Background: Airborne fine particulate matter (PM2.5) is associated with chronic obstructive pulmonary disease (COPD); however, the precise mechanism remains unclear. Here, we examined distinct gene and pathway characteristics across varying personal and ambient PM2.5 exposure durations within a prospective COPD cohort and the associations between differentially expressed genes (DEGs) and clinical phenotypes. Methods: Blood samples for RNA-sequencing were collected from 50 patients with COPD who underwent spirometry and quantitative computed tomography. We estimated personal and ambient PM2.5 exposure levels using hybrid and land use regression models. Associations between DEGs and PM2.5 exposure were examined in relation to lung function indicators (FEV1, FVC, and FEV1/FVC ratio) using Pearson correlation analysis adjusted for factors such as hospitalization, age, sex, season, Charlson Comorbidity Index score, and smoking status. Results: We analyzed DEGs across three cumulative PM2.5 exposure periods using personal and ambient exposure assessments. Gene ontology annotation and biological pathway analysis of the identified DEGs using the individual air pollution exposure prediction model revealed significant associations with gas transport, cellular processes related to cell cycle, cell proliferations, and neuron projection morphogenesis. The ambient air pollution prediction model revealed significant biological responses related to purine metabolism and antigen processing and presentation. EDAR, NKILA, HSD11B2, LOC100130027, LOC105378367, SENCR, CAMP, CEA-CAM6, CHIT1, EREG, HSD17B3, NPPA-AS1, and TRPV4 showed increased expression with higher PM2.5, correlating with reduced lung function. Conclusions: Our findings offer insights into the role of gene expression in patients with COPD in response to personal and ambient PM2.5 exposure, suggesting strategies to enhance respiratory conditions linked to air pollution.
Full Text
https://www.sciencedirect.com/science/article/pii/S0013935125016287
DOI
10.1016/j.envres.2025.122377
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Preventive Medicine (예방의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Kyoung-Nam(김경남)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/209102
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