已发表论文

利用生物信息学筛选和验证慢性阻塞性肺疾病 - 阻塞性睡眠呼吸暂停重叠综合征核心基因

 

Authors Qiang S , Wan R , Wu J , Wang C , Cui X , Zhang Y

Received 17 March 2025

Accepted for publication 18 July 2025

Published 26 July 2025 Volume 2025:20 Pages 2601—2614

DOI https://doi.org/10.2147/COPD.S528703

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

Editor who approved publication: Dr Jill Ohar

Shihao Qiang,* Rongrong Wan,* Jingyi Wu, Chao Wang, Xiaochuan Cui, Yunyun Zhang

Department of General Medicine, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi People’s Hospital, Wuxi, Jiangsu Province, 214023, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Xiaochuan Cui, Department of General Medicine, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi People’s Hospital, Wuxi, Jiangsu Province, 214023, People’s Republic of China, Email cuixiaochuan@njmu.edu.cn Yunyun Zhang, Department of General Medicine, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi People’s Hospital, Wuxi, Jiangsu Province, 214023, People’s Republic of China, Email zhangyunyun026133@njmu.edu.cn

Background: When obstructive sleep apnea (OSA) and chronic obstructive pulmonary disease (COPD) coexist in a patient, it is called overlap syndrome (OS). However, the molecular mechanisms underpinning OS are unclear. To address this, we explored potential OS mechanisms using bioinformatics.
Methods: OSA and COPD gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. Differential expression and weighted gene co-expression network analyses (WGCNA) were performed to identify common differentially expressed genes (DEGs) in OSA and COPD, and perform functional enrichment analysis. DEGs were validated in an external COPD gene expression dataset using receiver operating characteristic (ROC) curves and box plots. Positive results were initially identified as core genes, and were then validated by analyzing core genes in healthy controls, patients with OSA alone and patients with OS using RT-qPCR.
Results: Through differential expression gene analysis, 9 common DEGs for OSA and COPD were identified. Through WGCNA analysis, 128 common key module genes for OSA and COPD were identified. By taking the intersection of the identified 9 DEGs and the 128 common key module genes from WGCNA, 5 key genes were determined. Preliminary validation in the external gene expression dataset for COPD revealed that GRM8 was a potential hub gene for OS. Compared with the control group, the expression of GRM8 was significantly downregulated in the COPD group (P = 0.019). The diagnostic value was evaluated using the ROC curve, and the results showed that the AUC was 0.857 (95% CI: 0.614– 1.000). Finally, RT-qPCR confirmed that the expression levels of GRM8 in OSA and OS were significantly lower than those in the healthy control group (P < 0.05), and it was a hub gene significantly associated with OS.
Conclusion: Our research identified hub gene that may provide new directions for further mechanistic research on OS.

Keywords: overlap syndrome, chronic obstructive pulmonary disease, obstructive sleep apnea, bioinformatics, glutamate metabotropic receptor 8, GRM8