ArticleFrontiers in immunology2023
Integrative single-cell RNA sequencing and metabolomics decipher the imbalanced lipid-metabolism in maladaptive immune responses during sepsis.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 41 citations in OpenAlex.
- Metabolomics and machine learning approaches for diagnostic and prognostic biomarkers screening in sepsis.BMC anesthesiology · 2023Trial
- Combining bioinformatics and machine learning to analyze and validate sepsis-related cell senescence genes and potential drugs.Renal failure · 2026Article
- Dissecting T-cell exhaustion heterogeneity and immune ecosystem dynamics in colorectal cancer through multi-omics machine learning.BMC cancer · 2026Article
- Artificial Sweeteners and Autoimmune Diseases: Insights From Integrative Bioinformatics and Mendelian Randomization.Food science & nutrition · 2026Article
- PAFAH2 deficiency drives sepsis-induced vascular leakage by promoting ECHS1-mediated suppression of protein crotonylation and mitochondrial dysfunction.Inflammation and regeneration · 2026Article
- Integrating Mendelian randomization and multi-omics analysis unravels gut microbiota-driven metabolic mechanisms in sepsis and identifies diagnostic biomarkers through experimental validation.APL bioengineering · 2026Article
- Machine learning-derived identification of an obesity and lipid metabolism-related genes signature for the diagnosis and molecular typing of acute myocardial infarction.Frontiers in cardiovascular medicine · 2026Article
- An integrated multi-omics study of key mediators and therapeutic targets for doxorubicin-induced atrial fibrillation.PloS one · 2026Article
- Dynamic muscle loss and metabolic dysregulation in sepsis-associated muscle wasting: an integrated ultrasound and metabolomics study.Frontiers in medicine · 2026Article
- Search for novel biomarkers of febrile neutropenia in hematological patients: an overview.Clinical and experimental medicine · 2025Review
- Article
- Inhalable targeted CD8 + T cell nanoparticles loaded ferroptosis inhibitor ferrostatin-1 alleviate acute lung injury.Journal of nanobiotechnology · 2025Article
- Single-Cell transcriptomic profiles of peripheral blood immune cells reveal early monocyte and platelet activation in the transition from high-risk states to clinical sepsis.Scientific reports · 2025Article
- From cardiac injury to omics signatures: a narrative review on biomarkers in septic cardiomyopathy.Clinical and experimental medicine · 2025Review
- MMEASE: enhanced analytical workflow for single-cell metabolomics.Nucleic acids research · 2025Article
- Immune monitoring in sepsis: Present and prospective issues.Chinese medical journal · 2025Article
- Circ_0008285 regulates macrophage polarization through miR-375/MAPK14 axis in sepsis-induced acute lung injury.Journal of molecular histology · 2025Article
- Identifying early predictive and diagnostic biomarkers and exploring metabolic pathways for sepsis after trauma based on an untargeted metabolomics approach.Scientific reports · 2025Article
- Single-cell lipidomics: protocol development for reliable cellular profiling using capillary sampling.The Analyst · 2025Article
- Machine learning-based transcriptmics analysis revealsFrontiers in pharmacology · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
15 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: To identify differentially expressed lipid metabolism-related genes (DE-LMRGs) responsible for immune dysfunction in sepsis. Methods: The lipid metabolism-related hub genes were screened using machine learning algorithms, and the immune cell infiltration of these hub genes were assessed by CIBERSORT and Single-sample GSEA. Next, the immune function of these hub genes at the single-cell level were validated by comparing multiregional immune landscapes between septic patients (SP) and healthy control (HC). Then, the support vector machine-recursive feature elimination (SVM-RFE) algorithm was conducted to compare the significantly altered metabolites critical to hub genes between SP and HC. Furthermore, the role of the key hub gene was verified in sepsis rats and LPS-induced cardiomyocytes, respectively. Results: A total of 508 DE-LMRGs were identified between SP and HC, and 5 hub genes relevant to lipid metabolism ( Conclusion: The lipid metabolism-related hub genes may have great potential in prognosis prediction and precise treatment for sepsis patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.