Liang Qianqian, Wang Yide, Li Zheng
Department of Integrated Pulmonology, Fourth Clinical Medical College of Xinjiang Medical University, Urumqi, Xinjiang, China.
Xinjiang National Clinical Research Base of Traditional Chinese Medicine, The Affiliated Hospital of Xinjiang University of Traditional Chinese Medicine, Urumqi, Xinjiang, China.
Front Endocrinol (Lausanne). 2025 Jan 8;15:1475958. doi: 10.3389/fendo.2024.1475958. eCollection 2024.
Diabetes and chronic obstructive pulmonary disease (COPD) are prominent global health challenges, each imposing significant burdens on affected individuals, healthcare systems, and society. However, the specific molecular mechanisms supporting their interrelationship have not been fully defined.
We identified the differentially expressed genes (DEGs) of COPD and diabetes from multi-center patient cohorts, respectively. Through cross-analysis, we identified the shared DEGs of COPD and diabetes, and investigated alterations of signaling pathways using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). By using weighted gene correlation network analysis (WGCNA), key gene modules for COPD and diabetes were identified, and various machine learning algorithms were employed to identify shared biomarkers. Using xCell, we investigated the relationship between shared biomarkers and immune infiltration in diabetes and COPD. Single-cell sequencing, clinical samples, and animal models were used to confirm the robustness of shared biomarkers.
Cross-analysis identified 186 shared DEGs between diabetes and COPD patients. Functional enrichment results demonstrate that metabolic and immune-related pathways are common features altered in both diabetes and COPD patients. WGCNA identified 526 genes from key gene modules in COPD and diabetes. Multiple machine learning algorithms identified 4 shared biomarkers for COPD and diabetes, including CADPS, EDNRB, THBS4 and TMEM27. Finally, the 4 shared biomarkers were validated in single-cell sequencing data, clinical samples, and animal models, and their expression changes were consistent with the results of bioinformatic analysis.
Through comprehensive bioinformatics analysis, we revealed the potential connection between diabetes and COPD, providing a theoretical basis for exploring the common regulatory genes.
糖尿病和慢性阻塞性肺疾病（COPD）是全球突出的健康挑战，各自给受影响的个体、医疗系统和社会带来重大负担。然而，支持它们相互关系的具体分子机制尚未完全明确。
我们分别从多中心患者队列中鉴定出COPD和糖尿病的差异表达基因（DEG）。通过交叉分析，我们确定了COPD和糖尿病的共同DEG，并使用基因本体论（GO）、京都基因与基因组百科全书（KEGG）和基因集富集分析（GSEA）研究信号通路的改变。通过加权基因共表达网络分析（WGCNA），确定了COPD和糖尿病的关键基因模块，并采用多种机器学习算法来识别共同的生物标志物。使用xCell，我们研究了共同生物标志物与糖尿病和COPD中免疫浸润之间的关系。利用单细胞测序、临床样本和动物模型来证实共同生物标志物的稳健性。
交叉分析确定了糖尿病和COPD患者之间的186个共同DEG。功能富集结果表明，代谢和免疫相关通路是糖尿病和COPD患者中共同改变的特征。WGCNA从COPD和糖尿病的关键基因模块中鉴定出526个基因。多种机器学习算法确定了4个COPD和糖尿病的共同生物标志物，包括CADPS、EDNRB、THBS4和TMEM27。最后，这4个共同生物标志物在单细胞测序数据、临床样本和动物模型中得到验证，其表达变化与生物信息学分析结果一致。
通过全面的生物信息学分析，我们揭示了糖尿病与COPD之间的潜在联系，为探索共同调控基因提供了理论依据。
