Evidence map›Paper›PMID 38956669›Full record

ArticleJournal of translational medicine2024

Leveraging a neutrophil-derived PCD signature to predict and stratify patients with acute myocardial infarction: from AI prediction to biological interpretation.

Yihao Zhu, Yuxi Chen, Yao Zu

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Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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0cells of the map it votes in
5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Yihao ZhuInternational Research Center for Marine Biosciences, Ministry of Science and Technology, Shanghai Ocean University, Shanghai, 201306, People's Republic of China.ORCID 0000-0002-8354-9407
Yuxi ChenInternational Research Center for Marine Biosciences, Ministry of Science and Technology, Shanghai Ocean University, Shanghai, 201306, People's Republic of China.
Yao ZuInternational Research Center for Marine Biosciences, Ministry of Science and Technology, Shanghai Ocean University, Shanghai, 201306, People's Republic of China. yzu@shou.edu.cn.ORCID 0000-0001-8081-9136

Funding

Chenguang Program from the Shanghai Education Committee 14CG49National Natural Science Foundation of China 31501166National Natural Science Foundation of China 32170423Shanghai Sailing Program of Science and Technology Commission of Shanghai Municipality 15YF1405000
6 · The paper itself

Abstract

backgroundProgrammed cell death (PCD) has recently been implicated in modulating the removal of neutrophils recruited in acute myocardial infarction (AMI). Nonetheless, the clinical significance and biological mechanism of neutrophil-related PCD remain unexplored.

methodsWe employed an integrative machine learning-based computational framework to generate a predictive neutrophil-derived PCD signature (NPCDS) within five independent microarray cohorts from the peripheral blood of AMI patients. Non-negative matrix factorization was leveraged to develop an NPCDS-based AMI subtype. To elucidate the biological mechanism underlying NPCDS, we implemented single-cell transcriptomics on Cd45+ cells isolated from the murine heart of experimental AMI. We finally conducted a Mendelian randomization (MR) study and molecular docking to investigate the therapeutic value of NPCDS on AMI.

resultsWe reported the robust and superior performance of NPCDS in AMI prediction, which contributed to an optimal combination of random forest and stepwise regression fitted on nine neutrophil-related PCD genes (MDM2, PTK2B, MYH9, IVNS1ABP, MAPK14, GNS, MYD88, TLR2, CFLAR). Two divergent NPCDS-based subtypes of AMI were revealed, in which subtype 1 was characterized as inflammation-activated with more vibrant neutrophil activities, whereas subtype 2 demonstrated the opposite. Mechanically, we unveiled the expression dynamics of NPCDS to regulate neutrophil transformation from a pro-inflammatory phase to an anti-inflammatory phase in AMI. We uncovered a significant causal association between genetic predisposition towards MDM2 expression and the risk of AMI. We also found that lidoflazine, isotetrandrine, and cepharanthine could stably target MDM2.

conclusionAltogether, NPCDS offers significant implications for prediction, stratification, and therapeutic management for AMI.

Indexed as

ApoptosisMyocardial InfarctionNeutrophilsAnimalsHumansMachine LearningMaleMiceMice, Inbred C57BLMolecular Docking SimulationTranscriptomeDiagnosisMachine learningMechanismNeutrophilProgrammed cell death

Identifiers

PMID38956669
PMCPMC11221097

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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.