Integrated transcriptomic analysis of COVID-19 stages and recovery: insights into key gene signatures, immune features, and diagnostic biomarkers through machine learning.
{"title":"Integrated transcriptomic analysis of COVID-19 stages and recovery: insights into key gene signatures, immune features, and diagnostic biomarkers through machine learning.","authors":"Zhiyuan Gong, He An","doi":"10.3389/fgene.2025.1599867","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":12750,"journal":{"name":"Frontiers in Genetics","volume":"16 ","pages":"1599867"},"PeriodicalIF":2.8000,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12119500/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Genetics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.3389/fgene.2025.1599867","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
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.
Frontiers in GeneticsBiochemistry, 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.