ArticleMolecular medicine (Cambridge, Mass.)2023
Use of machine learning-based integration to develop a monocyte differentiation-related signature for improving prognosis in patients with sepsis.
Article in Molecular medicine (Cambridge, Mass.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 12 citations in OpenAlex.
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- N-myc and STAT interactor is a novel biomarker for predicting the severity and clinical outcome of sepsis: a prospective research.Frontiers in cellular and infection microbiology · 2026Article
- Bioinformatics combined with machine learning for potential biomarker screening and immune infiltration analysis in neonatal sepsis.Frontiers in pediatrics · 2026Article
- Shared immune biomarkers in necrotizing enterocolitis and neonatal sepsis identified via bioinformatics and machine learning.Scientific reports · 2025Article
- A 5-transcript signature for discriminating viral and bacterial etiology in pediatric pneumonia.iScience · 2025Article
- Immunomodulatory therapy for sepsis: from pathophysiological mechanisms to precision treatment.Frontiers in cellular and infection microbiology · 2025Review
- Targeting Matrix Metalloproteinase-9 to Alleviate T Cell Exhaustion and Improve Sepsis Prognosis.Research (Washington, D.C.) · 2025Article
- Identification of immune patterns in idiopathic pulmonary fibrosis patients driven by PLA2G7-positive macrophages using an integrated machine learning survival framework.Scientific reports · 2024Article
- An application of the Bayesian network model based on the EN-ESL-GA algorithm: Exploring the predictors of heart disease in middle-aged and elderly people in China.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Development of a COVID-19 early risk assessment system based on multiple machine learning algorithms and routine blood tests: a real-world study.Frontiers in immunology · 2024Article
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7 authors at 2 institutions in 1 country.
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Abstract
backgroundAlthough significant advances have been made in intensive care medicine and antibacterial treatment, sepsis is still a common disease with high mortality. The condition of sepsis patients changes rapidly, and each hour of delay in the administration of appropriate antibiotic treatment can lead to a 4-7% increase in fatality. Therefore, early diagnosis and intervention may help improve the prognosis of patients with sepsis.
methodsWe obtained single-cell sequencing data from 12 patients. This included 14,622 cells from four patients with bacterial infectious sepsis and eight patients with sepsis admitted to the ICU for other various reasons. Monocyte differentiation trajectories were analyzed using the "monocle" software, and differentiation-related genes were identified. Based on the expression of differentiation-related genes, 99 machine-learning combinations of prognostic signatures were obtained, and risk scores were calculated for all patients. The "scissor" software was used to associate high-risk and low-risk patients with individual cells. The "cellchat" software was used to demonstrate the regulatory relationships between high-risk and low-risk cells in a cellular communication network. The diagnostic value and prognostic predictive value of Enah/Vasp-like (EVL) were determined. Clinical validation of the results was performed with 40 samples. The "CBNplot" software based on Bayesian network inference was used to construct EVL regulatory networks.
resultsWe systematically analyzed three cell states during monocyte differentiation. The differential analysis identified 166 monocyte differentiation-related genes. Among the 99 machine-learning combinations of prognostic signatures constructed, the Lasso + CoxBoost signature with 17 genes showed the best prognostic prediction performance. The highest percentage of high-risk cells was found in state one. Cell communication analysis demonstrated regulatory networks between high-risk and low-risk cell subpopulations and other immune cells. We then determined the diagnostic and prognostic value of EVL stabilization in multiple external datasets. Experiments with clinical samples demonstrated the accuracy of this analysis. Finally, Bayesian network inference revealed potential network mechanisms of EVL regulation.
conclusionsMonocyte differentiation-related prognostic signatures based on the Lasso + CoxBoost combination were able to accurately predict the prognostic status of patients with sepsis. In addition, low EVL expression was associated with poor prognosis in sepsis.
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