Multi-modal transcriptomic analysis reveals metabolic dysregulation and immune responses in chronic obstructive pulmonary disease

被引:3
作者
Luo, Xiufang [1 ]
Zeng, Wei [2 ]
Tang, Jingyi [3 ]
Liu, Wang [4 ]
Yang, Jinyan [5 ]
Chen, Haiqing [3 ]
Jiang, Lai [3 ]
Zhou, Xuancheng [3 ]
Huang, Jinbang [3 ]
Zhang, Shengke [3 ]
Du, Linjuan [6 ]
Shen, Xiang [7 ]
Chi, Hao [3 ]
Wang, Huachuan [8 ]
机构
[1] Dazhou Cent Hosp, Geriatr Dept, Dazhou 635000, Peoples R China
[2] Second Peoples Hosp Yaan City, Oncol Dept, Yaan 625000, Peoples R China
[3] Southwest Med Univ, Clin Med Coll, Dept Clin Med, Luzhou 646000, Peoples R China
[4] Cheng Fei Hosp, Dept Gen Surg, Chengdu 610000, Peoples R China
[5] Southwest Med Univ, Sch Stomatol, Luzhou 646000, Peoples R China
[6] Dazhou Cent Hosp, Oncol Dept, Dazhou 635000, Peoples R China
[7] Southwest Med Univ, Affiliated Hosp, Dept Resp & Crit Care Med, Luzhou 646000, Sichuan, Peoples R China
[8] Dazhou Cent Hosp, Dept Thorac Surg, Dazhou 635000, Peoples R China
关键词
Omics technologies; Senescence-related genes; Metabolic pathways; COPD; Diagnostic models; Dysregulation; Therapeutic targets; CELLULAR SENESCENCE; EXPRESSION; APOPTOSIS; TARGET; COPD;
D O I
10.1038/s41598-024-71773-w
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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页数:24
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