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已发表论文
通过整合机器学习和WGCNA分析识别COPD和IPF中的糖基化相关生物标志物
Authors Yin XL, Zhai Y, Wang L
Received 1 May 2025
Accepted for publication 26 November 2025
Published 22 January 2026 Volume 2026:19 537967
DOI https://doi.org/10.2147/JIR.S537967
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 5
Editor who approved publication: Dr Tara Strutt
Xiao Ling Yin,1 Ying Zhai,2 Lei Wang3
1Department of Respiratory, Zibo Hospital of Integrated Traditional Chinese and Western Medicine, Zibo City, Shandong Province, 255022, People’s Republic of China; 2Department of Neurology, Zibo Hospital of Integrated Traditional Chinese and Western Medicine, Zibo City, Shandong Province, 255022, People’s Republic of China; 3Department of Oncology, Zibo Hospital of Integrated Traditional Chinese and Western Medicine, Zibo City, Shandong Province, 255022, People’s Republic of China
Correspondence: Lei Wang, Zibo Hospital of Integrated Traditional Chinese and Western Medicine, No. 8, Jinjing Avenue, Zhangdian District, Zibo City, Shandong Province, 255022, People’s Republic of China, Email WangleiZXYJH@163.com
Objective: This research aimed to explore key glycosylation-related genes (signature genes) and associated molecular mechanism on chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF), which further providing new perspectives for disease prognosis and diagnose.
Patients and Methods: The gene expression profiles were obtained from the public GEO database. The glycosylation-related genes were identified based co-DEGs from COPD vs normal samples and IPF vs normal samples, module genes by weighted gene co-expression network analysis (WGCNA), as well as glycosylation genes from database. Signature genes were screened using machine learning methods, followed by immune infiltration, function analysis, drug-gene and transcriptional regulatory network analysis. Finally, validation analysis based on tissue samples from COPD/IPF patients were performed to test the expression of signature genes.
Results: A total of 35 differentially expressed glycosylation-related genes for both COPD and IPF were explored. By three kinds of machine learning analyses, totally three signature genes including SULF1, ST8SIA1 and FCN3 were explored. In COPD, the AUC values for FCN3, ST8SIA1, and SULF1 were 0.643, 0.722, and 0.719, respectively; while in IPF, they were 0.955, 0.792, and 0.943, respectively. Immune infiltration and GSEA analysis showed that signature genes were dramatically correlated with activated B cell and extracellular matrix (ECM)-associated functions (P < 0.05). Bisphenol A and Valproic acid were common drugs for both three signature genes. Validation analysis found that the ST8SIA1 expression was related to the disease state (P < 0.001). Functional experiments revealed that it affected cell behaviors, including proliferation, apoptosis, migration, and invasion (all, P < 0.01), and drug treatments could regulate its expression and glycosylation levels (all, P < 0.05), providing crucial evidence for biomarker research and pathogenesis exploration of the two diseases.
Conclusion: We identified SULF1, ST8SIA1 and FCN3 as shared glycosylation-related biomarkers in COPD and IPF. These genes bridge fibrosis, inflammation, and immune dysregulation, offering potential diagnostic and therapeutic targets.
Keywords: chronic obstructive pulmonary disease, glycosylation-related gene, idiopathic pulmonary fibrosis, immune cell infiltration, machine learning, validation analysis
1Department of Respiratory, Zibo Hospital of Integrated Traditional Chinese and Western Medicine, Zibo City, Shandong Province, 255022, People’s Republic of China; 2Department of Neurology, Zibo Hospital of Integrated Traditional Chinese and Western Medicine, Zibo City, Shandong Province, 255022, People’s Republic of China; 3Department of Oncology, Zibo Hospital of Integrated Traditional Chinese and Western Medicine, Zibo City, Shandong Province, 255022, People’s Republic of China
Correspondence: Lei Wang, Zibo Hospital of Integrated Traditional Chinese and Western Medicine, No. 8, Jinjing Avenue, Zhangdian District, Zibo City, Shandong Province, 255022, People’s Republic of China, Email WangleiZXYJH@163.com
Objective: This research aimed to explore key glycosylation-related genes (signature genes) and associated molecular mechanism on chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF), which further providing new perspectives for disease prognosis and diagnose.
Patients and Methods: The gene expression profiles were obtained from the public GEO database. The glycosylation-related genes were identified based co-DEGs from COPD vs normal samples and IPF vs normal samples, module genes by weighted gene co-expression network analysis (WGCNA), as well as glycosylation genes from database. Signature genes were screened using machine learning methods, followed by immune infiltration, function analysis, drug-gene and transcriptional regulatory network analysis. Finally, validation analysis based on tissue samples from COPD/IPF patients were performed to test the expression of signature genes.
Results: A total of 35 differentially expressed glycosylation-related genes for both COPD and IPF were explored. By three kinds of machine learning analyses, totally three signature genes including SULF1, ST8SIA1 and FCN3 were explored. In COPD, the AUC values for FCN3, ST8SIA1, and SULF1 were 0.643, 0.722, and 0.719, respectively; while in IPF, they were 0.955, 0.792, and 0.943, respectively. Immune infiltration and GSEA analysis showed that signature genes were dramatically correlated with activated B cell and extracellular matrix (ECM)-associated functions (P < 0.05). Bisphenol A and Valproic acid were common drugs for both three signature genes. Validation analysis found that the ST8SIA1 expression was related to the disease state (P < 0.001). Functional experiments revealed that it affected cell behaviors, including proliferation, apoptosis, migration, and invasion (all, P < 0.01), and drug treatments could regulate its expression and glycosylation levels (all, P < 0.05), providing crucial evidence for biomarker research and pathogenesis exploration of the two diseases.
Conclusion: We identified SULF1, ST8SIA1 and FCN3 as shared glycosylation-related biomarkers in COPD and IPF. These genes bridge fibrosis, inflammation, and immune dysregulation, offering potential diagnostic and therapeutic targets.
Keywords: chronic obstructive pulmonary disease, glycosylation-related gene, idiopathic pulmonary fibrosis, immune cell infiltration, machine learning, validation analysis