Identification and validation of programmed cell death related biomarkers for the treatment and prevention COVID-19 DOI Creative Commons
Jie Yang,

YaoXi Tan,

Xing Liu

et al.

Annals of Medicine, Journal Year: 2025, Volume and Issue: 57(1)

Published: April 29, 2025

Programmed cell death (PCD) plays a key role in the progression of coronavirus disease 2019 (COVID-19). However, PCD-relevant biomarkers have not been fully discovered. The aim this study was to explore for treatment and prevention COVID-19. Bioinformatic analyses were performed clinical relevant PCD genes with differential expression (DE) COVID-19 compared matched controls. PPI network used hub screening machine learning methods employed filtering feature genes. biomarker screened by Venn diagram. correlations between features immune microenvironment further explored. Biomarker validation samples real-time reverse transcriptase-polymerase chain reaction (RT-qPCR). In total, 118 clinically associated expressed (DEGs) screened, which mainly related apoptosis pathways, among six (Cyclin B1 (CCNB1), cyclin-dependent kinase 1 (CDK1), interferon regulatory factor 4 (IRF4), lipoteichoic acid (LTA), matrix metallopeptidase 9 (MMP9) Oncostatin M (OSM)) identified. excellent or good diagnostic performance determined receiver operating characteristic (ROC) curve analysis. showed diverse indicators, such as age, sex Intensive Care Unit (ICU) admission. Total 14 types cells exerted infiltration Biomarkers correlated at varying levels. classified three clusters, significant associations information, sex, age ICU DEGs patients relative (CCNB1, CDK1, IRF4, LTA, MMP9 OSM) can be served

Language: Английский

Identification and validation of programmed cell death related biomarkers for the treatment and prevention COVID-19 DOI Creative Commons
Jie Yang,

YaoXi Tan,

Xing Liu

et al.

Annals of Medicine, Journal Year: 2025, Volume and Issue: 57(1)

Published: April 29, 2025

Programmed cell death (PCD) plays a key role in the progression of coronavirus disease 2019 (COVID-19). However, PCD-relevant biomarkers have not been fully discovered. The aim this study was to explore for treatment and prevention COVID-19. Bioinformatic analyses were performed clinical relevant PCD genes with differential expression (DE) COVID-19 compared matched controls. PPI network used hub screening machine learning methods employed filtering feature genes. biomarker screened by Venn diagram. correlations between features immune microenvironment further explored. Biomarker validation samples real-time reverse transcriptase-polymerase chain reaction (RT-qPCR). In total, 118 clinically associated expressed (DEGs) screened, which mainly related apoptosis pathways, among six (Cyclin B1 (CCNB1), cyclin-dependent kinase 1 (CDK1), interferon regulatory factor 4 (IRF4), lipoteichoic acid (LTA), matrix metallopeptidase 9 (MMP9) Oncostatin M (OSM)) identified. excellent or good diagnostic performance determined receiver operating characteristic (ROC) curve analysis. showed diverse indicators, such as age, sex Intensive Care Unit (ICU) admission. Total 14 types cells exerted infiltration Biomarkers correlated at varying levels. classified three clusters, significant associations information, sex, age ICU DEGs patients relative (CCNB1, CDK1, IRF4, LTA, MMP9 OSM) can be served

Language: Английский

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