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已发表论文
慢性阻塞性肺疾病中内质网应激相关生物标志物：全面转录组、孟德尔随机化和机器学习分析
Authors Li J, Li G, Liu J, Li L, Zhou H, Fei X, Wen Y, Zhao D
Received 19 June 2025
Accepted for publication 10 November 2025
Published 26 November 2025 Volume 2025:20 Pages 3803—3818
DOI https://doi.org/10.2147/COPD.S548160
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Richard Russell
Jiajia Li,1 Guofeng Li,1 Junnan Liu,2 Lijie Li,2 Huiling Zhou,1 Xinru Fei,3 Yuhua Wen,3 Dongkai Zhao2
1School of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, Jilin, 130117, People’s Republic of China; 2Department of Respiration, Third Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, Jilin, 130022, People’s Republic of China; 3School of Rehabilitation Medicine, Changchun University of Chinese Medicine, Changchun, Jilin, 130117, People’s Republic of China
Correspondence: Dongkai Zhao, Department of Respiration, Third Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, Jilin, 130022, People’s Republic of China, Email dongkaizhao1229@163.com
Background: Chronic obstructive pulmonary disease (COPD) is a common respiratory disease; however, measures for preventing COPD and delaying disease progression are limited. Therefore, identifying genetic variations and novel biomarkers related to COPD incidence and progression is crucial for improving clinical outcomes. Here, we investigated the potential of the endoplasmic reticulum stress-related gene DNAJB1 as a risk gene in COPD and its clinical value via bioinformatics and Mendelian randomization.
Methods: We first performed differential gene analysis on single-cell sequencing datasets then identified candidate genes and genetic loci using Mendelian randomization analysis and co-localization analysis, respectively. Machine-learning analysis of microarray data was used to identify potential biomarkers. Subsequently, we explored the biological role of DNAJB1 through cellular communication, functional enrichment, and correlation analyses with inflammatory factors.
Results: DNAJB1 was identified as a risk gene for COPD that shares genetic variants with COPD. Nine key biological genes, including DNAJB1, were identified as potential diagnostic biomarkers. High DNAJB1 expression and high scores for the endoplasmic reticulum stress gene set were validated using the microarray dataset.
Conclusion: Our finding reveals DNAJB1 as a COPD risk gene and identifies a diagnostic genetic marker panel, providing useful perspectives for early diagnosis and the development of potential therapeutic targets.
Keywords: chronic obstructive pulmonary disease, endoplasmic reticulum stress, biomarker gene, Mendelian randomization, machine learning
1School of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, Jilin, 130117, People’s Republic of China; 2Department of Respiration, Third Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, Jilin, 130022, People’s Republic of China; 3School of Rehabilitation Medicine, Changchun University of Chinese Medicine, Changchun, Jilin, 130117, People’s Republic of China
Correspondence: Dongkai Zhao, Department of Respiration, Third Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, Jilin, 130022, People’s Republic of China, Email dongkaizhao1229@163.com
Background: Chronic obstructive pulmonary disease (COPD) is a common respiratory disease; however, measures for preventing COPD and delaying disease progression are limited. Therefore, identifying genetic variations and novel biomarkers related to COPD incidence and progression is crucial for improving clinical outcomes. Here, we investigated the potential of the endoplasmic reticulum stress-related gene DNAJB1 as a risk gene in COPD and its clinical value via bioinformatics and Mendelian randomization.
Methods: We first performed differential gene analysis on single-cell sequencing datasets then identified candidate genes and genetic loci using Mendelian randomization analysis and co-localization analysis, respectively. Machine-learning analysis of microarray data was used to identify potential biomarkers. Subsequently, we explored the biological role of DNAJB1 through cellular communication, functional enrichment, and correlation analyses with inflammatory factors.
Results: DNAJB1 was identified as a risk gene for COPD that shares genetic variants with COPD. Nine key biological genes, including DNAJB1, were identified as potential diagnostic biomarkers. High DNAJB1 expression and high scores for the endoplasmic reticulum stress gene set were validated using the microarray dataset.
Conclusion: Our finding reveals DNAJB1 as a COPD risk gene and identifies a diagnostic genetic marker panel, providing useful perspectives for early diagnosis and the development of potential therapeutic targets.
Keywords: chronic obstructive pulmonary disease, endoplasmic reticulum stress, biomarker gene, Mendelian randomization, machine learning