通过合并症网络分析解读新冠肺炎急性后后遗症的分子机制

IF 3.2 2区 数学 Q1 MATHEMATICS, APPLIED Chaos Pub Date : 2025-02-01 DOI:10.1063/5.0250923
Lue Tian, Eric Wan, Sze Ling Celine Chui, Shirely Li, Esther Chan, Hao Luo, Ian C K Wong, Qingpeng Zhang
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引用次数: 0

摘要

COVID-19急性后后遗症对大流行后的世界构成了重大的卫生挑战。然而,PASC的潜在生物学机制仍然是复杂和难以捉摸的。基于网络的方法可以利用电子健康记录数据和生物学知识来调查COVID-19对PASC的影响,并揭示潜在的生物学机制。本研究分析了50 296名COVID-19患者和100 592名健康非暴露组的全地区纵向电子健康记录(从2020年1月1日至2022年8月31日),以确定COVID-19对疾病进展的影响,提供分子见解,并确定相关生物标志物。我们构建了共病网络,并进行了疾病-蛋白质图谱和蛋白质-蛋白质相互作用网络分析,以揭示COVID-19对疾病轨迹的影响。结果显示,流行疾病合并症模式存在差异,某些模式受COVID-19的影响更为明显。重叠蛋白阐明了COVID-19对每种共病模式影响的生物学机制,并且可以根据其权重确定必需蛋白。我们的研究结果有助于阐明COVID-19的生物学机制,发现干预方法,解码合并症关联的分子基础,同时也为特定疾病进展模式提供潜在的生物标志物和相应的治疗方法。
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Deciphering the molecular mechanism of post-acute sequelae of COVID-19 through comorbidity network analysis.

The post-acute sequelae of COVID-19 (PASC) poses a significant health challenge in the post-pandemic world. However, the underlying biological mechanisms of PASC remain intricate and elusive. Network-based methods can leverage electronic health record data and biological knowledge to investigate the impact of COVID-19 on PASC and uncover the underlying biological mechanisms. This study analyzed territory-wide longitudinal electronic health records (from January 1, 2020 to August 31, 2022) of 50 296 COVID-19 patients and a healthy non-exposed group of 100 592 individuals to determine the impact of COVID-19 on disease progression, provide molecular insights, and identify associated biomarkers. We constructed a comorbidity network and performed disease-protein mapping and protein-protein interaction network analysis to reveal the impact of COVID-19 on disease trajectories. Results showed disparities in prevalent disease comorbidity patterns, with certain patterns exhibiting a more pronounced influence by COVID-19. Overlapping proteins elucidate the biological mechanisms of COVID-19's impact on each comorbidity pattern, and essential proteins can be identified based on their weights. Our findings can help clarify the biological mechanisms of COVID-19, discover intervention methods, and decode the molecular basis of comorbidity associations, while also yielding potential biomarkers and corresponding treatments for specific disease progression patterns.

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来源期刊
Chaos
Chaos 物理-物理:数学物理
CiteScore
5.20
自引率
13.80%
发文量
448
审稿时长
2.3 months
期刊介绍: Chaos: An Interdisciplinary Journal of Nonlinear Science is a peer-reviewed journal devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.
期刊最新文献
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