Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis

3 Apr 2023

Abstract excerpt

Background: Sepsis is a heterogeneous disease, therefore the single-gene-based biomarker is not sufficient to fully understand the disease. Higher-level biomarkers need to be explored to identify important pathways related to sepsis and evaluate their clinical significance. Methods: Gene Set Enrichment Analysis (GSEA) was used to analyze the sepsis transcriptome to obtain the pathway-level expression. Limma was...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Inflammation biomarkers and pathways
Share to Neutrophil, Myeloperoxidase and Oxidative Mechanisms
Share to Sepsis Diagnosis and Treatment

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 37077915
DOI: 10.3389/fimmu.2023.1110070

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Integrated analysis of multi-omics data reveals T cell exhaustion in sepsisPMID 37077915Read ↗

Identification of Potential Biomarkers and Immune Features of Sepsis Using Bioinformatics AnalysisSemantic proximity Read ↗

exploring the molecular mechanism of sepsis comprehensively based on multi-omics analysisSemantic proximity Read ↗

Multiple datasets to explore the molecular mechanism of sepsisSemantic proximity Read ↗

Analysis of novel biomarkers and immune cell alterations in sepsis based on single-cell sequencing and machine learning algorithmsSemantic proximity Read ↗

Integration of Transcriptomic and Single-Cell Data to Uncover Senescence- and Ferroptosis-Associated Biomarkers in SepsisSemantic proximity Read ↗

A comprehensive analysis of immune features and construction of an immune gene diagnostic model for sepsisSemantic proximity Read ↗

Screening of Key Genes of Sepsis and Septic Shock Using Bioinformatics AnalysisSemantic proximity Read ↗

Identification of a novel sepsis prognosis model and analysis of possible drug application prospects: Based on scRNA-seq and RNA-seq dataSemantic proximity Read ↗

Comprehensive Multi‐Omics Integration Reveals B Cell‐Derived ELL2 as a Novel Diagnostic and Prognostic Biomarker in SepsisSemantic proximity Read ↗

A Meta-Analysis of Whole Blood Transcriptome Reveals Association of Increased Neutrophil Activity and T cell Suppression in SepsisSemantic proximity Read ↗

Using RNA-Seq to Investigate Immune-Metabolism Features in Immunocompromised Patients With SepsisSemantic proximity Read ↗

Construction of gene network database, and identification of key genes for diagnosis, prognosis, and treatment in sepsisSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.