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Transcriptomic clustering of critically-ill COVID-19 patients [Day 1-RNA-Seq]

ABSTRACT: Transcriptomic clustering of critically-ill COVID-19 patients [Day 1-RNA-Seq]

PROVIDER: PRJNA809356 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR18101100.fastq.gz	Fastqsanger.gz
	SRR18101101.fastq.gz	Fastqsanger.gz
	SRR18101102.fastq.gz	Fastqsanger.gz
	SRR18101103.fastq.gz	Fastqsanger.gz
	SRR18101104.fastq.gz	Fastqsanger.gz

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Transcriptomic clustering of critically-ill COVID-19 patients

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Project description:Background: COVID-19 has revealed novel pathological mechanisms, particularly hypercoagulability leading to increased thrombotic risk in critically ill patients. This study investigates transcriptional signatures associated with thrombosis development in COVID-19 intensive care unit (ICU) patients and evaluates their predictive potential. Methods: We performed whole blood transcriptional profiling of 57 mechanically ventilated COVID-19 patients, comparing those with thrombotic complications (TC, n=36) to those without (non-TC, n=21) using differential gene expression and machine learning approaches. Results: TC patients showed greater transcriptome disruption and 283 differentially expressed genes compared to non-TC patients. Key features included enhanced neutrophil activation, inflammatory responses, and monocyte activation alongside suppressed lymphocyte function. An OPLS-DA model achieved excellent classification performance (AUC=0.961, 95% CI: 0.905-0.997). The maltase-glucoamylase gene (MGAM) was the top discriminatory biomarker outperforming traditional clinical markers like D-dimer and C-reactive protein (AUC=0.94). Conclusions: Thrombotic complications in critically ill COVID-19 patients are characterized by distinct transcriptional signatures reflecting heightened neutrophil activation and inflammatory dysregulation. MGAM represents a novel potential biomarker that outperforms traditional clinical markers for identifying patients at high thrombotic risk, offering new opportunities for personalized risk stratification and management in severe COVID-19.

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Transcriptomic clustering of critically-ill COVID-19 patients

Project description:This SuperSeries is composed of the SubSeries listed below.

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