Integrated transcriptomic analysis of COVID-19 stages and recovery: insights into key gene signatures, immune features, and diagnostic biomarkers through machine learning.

IF 2.8 3区 生物学 Q2 GENETICS & HEREDITY
Frontiers in Genetics Pub Date : 2025-05-15 eCollection Date: 2025-01-01 DOI:10.3389/fgene.2025.1599867
Zhiyuan Gong, He An
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引用次数: 0

Abstract

Background: COVID-19 progression and recovery involve complex gene expression changes and immune dysregulation, but their dynamic alterations remain poorly understood. Current clinical indicators lack precision in distinguishing severe cases, highlighting the need for molecular biomarkers and diagnostic tools.

Methods: Three transcriptomic datasets were analyzed: 1) COVID-19 progression from Healthy, Moderate, Severe, to ICU patients; 2) recovery stages (1, 3, and 6 months) compared to Healthy controls; and 3) COVID-19 ICU versus non-ICU patients. Differential expression analysis, immune cell infiltration estimation, machine learning (LASSO regression and random forest), and functional enrichment were used to identify key genes and molecular mechanisms.

Results: Gene expression analysis revealed dynamic changes during COVID-19 progression. Adaptive immune cells (e.g., B cells and T cells) decreased, while innate immune cells (e.g., monocytes and neutrophils) increased, particularly in ICU patients. Recovery analysis showed significantly reduced adaptive immune cells at 1 month, with partial recovery by 3 and 6 months. Machine learning identified CCR5, CYSLTR1, and KLRG1 as diagnostic biomarkers for distinguishing ICU from non-ICU patients, with AUC values of 0.916, 0.885, and 0.899, respectively.

Conclusion: This study identified CCR5, CYSLTR1, and KLRG1 as efficient diagnostic biomarkers for severe COVID-19 using machine learning and revealed immune regulatory features across COVID-19 progression and recovery.

COVID-19阶段和恢复的综合转录组学分析:通过机器学习了解关键基因特征、免疫特征和诊断生物标志物。
背景:COVID-19的进展和恢复涉及复杂的基因表达变化和免疫失调,但其动态变化尚不清楚。目前的临床指标在区分重症病例方面缺乏准确性,突出了对分子生物标志物和诊断工具的需求。方法:分析三个转录组数据集:1)健康、中度、重度至ICU患者的COVID-19进展情况;2)与健康对照组相比,康复阶段(1、3和6个月);3) COVID-19 ICU与非ICU患者。通过差异表达分析、免疫细胞浸润估计、机器学习(LASSO回归和随机森林)和功能富集来鉴定关键基因和分子机制。结果:基因表达分析揭示了COVID-19进展过程中的动态变化。适应性免疫细胞(如B细胞和T细胞)减少,而先天免疫细胞(如单核细胞和中性粒细胞)增加,特别是在ICU患者中。恢复分析显示,1个月时适应性免疫细胞明显减少,3个月和6个月时部分恢复。机器学习识别出CCR5、CYSLTR1、KLRG1作为区分ICU与非ICU患者的诊断性生物标志物,AUC值分别为0.916、0.885、0.899。结论:本研究利用机器学习技术鉴定出CCR5、CYSLTR1和KLRG1是重症COVID-19的有效诊断生物标志物,并揭示了COVID-19进展和恢复过程中的免疫调节特征。
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来源期刊
Frontiers in Genetics
Frontiers in Genetics Biochemistry, Genetics and Molecular Biology-Molecular Medicine
CiteScore
5.50
自引率
8.10%
发文量
3491
审稿时长
14 weeks
期刊介绍: Frontiers in Genetics publishes rigorously peer-reviewed research on genes and genomes relating to all the domains of life, from humans to plants to livestock and other model organisms. Led by an outstanding Editorial Board of the world’s leading experts, this multidisciplinary, open-access journal is at the forefront of communicating cutting-edge research to researchers, academics, clinicians, policy makers and the public. The study of inheritance and the impact of the genome on various biological processes is well documented. However, the majority of discoveries are still to come. A new era is seeing major developments in the function and variability of the genome, the use of genetic and genomic tools and the analysis of the genetic basis of various biological phenomena.
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