Evidence map›Paper›PMID 38716195›Full record

ArticleFrontiers in cellular and infection microbiology2024

Integrating machine learning algorithms and single-cell analysis to identify gut microbiota-related macrophage biomarkers in atherosclerotic plaques.

Yin Ke, Jian Yue, Jiaming He, Guojing Liu

Abstract read
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yin Ke *Department of Neurosurgery, The University-Town Hospital of Chongqing Medical University, Chongqing, China.
Jian Yue *Department of Breast Surgery, Gaozhou People's Hospital, Gaozhou, China.
Jiaming HeInstitute of Life Sciences, Chongqing Medical University, Chongqing, China.
Guojing LiuDepartment of Neurosurgery, The University-Town Hospital of Chongqing Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The relationship between macrophages and the gut microbiota in patients with atherosclerosis remains poorly defined, and effective biological markers are lacking. This study aims to elucidate the interplay between gut microbial communities and macrophages, and to identify biomarkers associated with the destabilization of atherosclerotic plaques. The goal is to enhance our understanding of the underlying molecular pathways and to pave new avenues for diagnostic approaches and therapeutic strategies in the disease. Methods: This study employed Weighted Gene Co-expression Network Analysis (WGCNA) and differential expression analysis on atherosclerosis datasets to identify macrophage-associated genes and quantify the correlation between these genes and gut microbiota gene sets. The Random Forest algorithm was utilized to pinpoint PLEK, IRF8, BTK, CCR1, and CD68 as gut microbiota-related macrophage genes, and a nomogram was constructed. Based on the top five genes, a Non-negative Matrix Factorization (NMF) algorithm was applied to construct gut microbiota-related macrophage clusters and analyze their potential biological alterations. Subsequent single-cell analyses were conducted to observe the expression patterns of the top five genes and the interactions between immune cells. Finally, the expression profiles of key molecules were validated using clinical samples from atherosclerosis patients. Results: Utilizing the Random Forest algorithm, we ultimately identified PLEK, IRF8, CD68, CCR1, and BTK as gut microbiota-associated macrophage genes that are upregulated in atherosclerotic plaques. A nomogram based on the expression of these five genes was constructed for use as an auxiliary tool in clinical diagnosis. Single-cell analysis confirmed the specific expression of gut microbiota-associated macrophage genes in macrophages. Clinical samples substantiated the high expression of PLEK in unstable atherosclerotic plaques. Conclusion: Gut microbiota-associated macrophage genes (PLEK, IRF8, CD68, CCR1, and BTK) may be implicated in the pathogenesis of atherosclerotic plaques and could serve as diagnostic markers to aid patients with atherosclerosis.

Indexed as

AlgorithmsAtherosclerosisBiomarkersGastrointestinal MicrobiomeMachine LearningMacrophagesPlaque, AtheroscleroticReceptors, CCR1Single-Cell AnalysisAgammaglobulinaemia Tyrosine KinaseAntigens, CDAntigens, Differentiation, MyelomonocyticCD68 MoleculeGene Expression ProfilingGene Regulatory NetworksHumansAgammaglobulinaemia Tyrosine KinaseAntigens, CDAntigens, Differentiation, MyelomonocyticBiomarkersCCR1 protein, humanCD68 antigen, humanCD68 MoleculeInterferon Regulatory Factor-8Interferon Regulatory FactorsReceptors, CCR1atherosclerotic plaquesbioinformaticsgut microbiotamachine learningmacrophage

Identifiers

PMID38716195
PMCPMC11074432

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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.