Geriatric Department, Dazhou Central Hospital, Dazhou, 635000, China.
Oncology Department, Second People's Hospital of Yaan City, Yaan, 625000, China.
Sci Rep. 2024 Sep 30;14(1):22699. doi: 10.1038/s41598-024-71773-w.
Chronic obstructive pulmonary disease (COPD), a progressive inflammatory condition of the airways, emerges from the complex interplay between genetic predisposition and environmental factors. Notably, its incidence is on the rise, particularly among the elderly demographic. Current research increasingly highlights cellular senescence as a key driver in chronic lung pathologies. Despite this, the detailed mechanisms linking COPD with senescent genomic alterations remain elusive. To address this gap, there is a pressing need for comprehensive bioinformatics methodologies that can elucidate the molecular intricacies of this link. This approach is crucial for advancing our understanding of COPD and its association with cellular aging processes. Utilizing a spectrum of advanced bioinformatics techniques, this research delved into the potential mechanisms linking COPD with aging-related genes, identifying four key genes (EP300, MTOR, NFE2L1, TXN) through machine learning and weighted gene co-expression network analysis (WGCNA) analyses. Subsequently, a precise diagnostic model leveraging an artificial neural network was developed. The study further employed single-cell analysis and molecular docking to investigate senescence-related cell types in COPD tissues, particularly focusing on the interactions between COPD and NFE2L1, thereby enhancing the understanding of COPD's molecular underpinnings. Leveraging artificial neural networks, we developed a robust classification model centered on four genes-EP300, MTOR, NFE2L1, TXN-exhibiting significant predictive capability for COPD and offering novel avenues for its early diagnosis. Furthermore, employing various single-cell analysis techniques, the study intricately unraveled the characteristics of senescence-related cell types in COPD tissues, enriching our understanding of the disease's cellular landscape. This research anticipates offering novel biomarkers and therapeutic targets for early COPD intervention, potentially alleviating the disease's impact on individuals and healthcare systems, and contributing to a reduction in global COPD-related mortality. These findings carry significant clinical and public health ramifications, bolstering the foundation for future research and clinical strategies in managing and understanding COPD.
慢性阻塞性肺疾病(COPD)是一种气道的进行性炎症性疾病,源于遗传易感性和环境因素的复杂相互作用。值得注意的是,它的发病率在上升,特别是在老年人群中。目前的研究越来越强调细胞衰老作为慢性肺部病变的关键驱动因素。尽管如此,将 COPD 与衰老相关的基因组改变联系起来的详细机制仍然难以捉摸。为了弥补这一空白,迫切需要全面的生物信息学方法来阐明这种联系的分子复杂性。这种方法对于深入了解 COPD 及其与细胞衰老过程的关联至关重要。本研究利用一系列先进的生物信息学技术,深入探讨了 COPD 与衰老相关基因之间的潜在机制,通过机器学习和加权基因共表达网络分析(WGCNA)分析确定了四个关键基因(EP300、MTOR、NFE2L1、TXN)。随后,利用人工神经网络开发了一个精确的诊断模型。该研究还进一步进行了单细胞分析和分子对接,以研究 COPD 组织中的与衰老相关的细胞类型,特别关注 COPD 与 NFE2L1 之间的相互作用,从而增强对 COPD 分子基础的理解。本研究利用人工神经网络,开发了一个以 EP300、MTOR、NFE2L1 和 TXN 四个基因为中心的强大分类模型,该模型对 COPD 具有显著的预测能力,并为其早期诊断提供了新的途径。此外,本研究还利用各种单细胞分析技术,详细揭示了 COPD 组织中与衰老相关的细胞类型的特征,丰富了我们对疾病细胞景观的理解。这项研究有望为早期 COPD 干预提供新的生物标志物和治疗靶点,可能减轻疾病对个人和医疗系统的影响,并有助于降低全球 COPD 相关死亡率。这些发现具有重要的临床和公共卫生意义,为未来 COPD 管理和理解的研究和临床策略奠定了基础。