Evidence map›Paper›PMID 41557688›Full record

ArticlePloS one2026

Cell death-related gene signatures as dual-function biomarkers: Early diagnosis and therapeutic targeting in Staphylococcus aureus pneumonia.

Cao Qing, Wanjuan Sun, Chaomian Yang, Yien Yao, Qiong Liang, Tianxia Huang, Lu Lin

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In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Cao QingDepartment of Pulmonary and Critical Care Medicine, The First People's Hospital of Nanning, Nanning, China.
Wanjuan SunDepartment of Pulmonary and Critical Care Medicine, The First People's Hospital of Nanning, Nanning, China.
Chaomian YangDepartment of Pulmonary and Critical Care Medicine, The First People's Hospital of Nanning, Nanning, China.
Yien YaoDepartment of Pulmonary and Critical Care Medicine, The First People's Hospital of Nanning, Nanning, China.
Qiong LiangDepartment of Pulmonary and Critical Care Medicine, The First People's Hospital of Nanning, Nanning, China.
Tianxia HuangDepartment of Pulmonary and Critical Care Medicine, The First People's Hospital of Nanning, Nanning, China.
Lu LinDepartment of Pulmonary and Critical Care Medicine, The First People's Hospital of Nanning, Nanning, China.ORCID https://orcid.org/0000-0001-9273-0809

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStaphylococcus aureus (S. aureus) pneumonia constitutes a lethal respiratory infection with persistently high clinical mortality. Although programmed cell death (PCD) pathways are implicated in diverse disease processes, their mechanistic roles in S. aureus pneumonia pathogenesis, particularly as dual-purpose biomarkers for early diagnosis and therapeutic targeting remain insufficiently characterized.

methodsHigh-throughput RNA sequencing was conducted on S. aureus challenged murine pulmonary tissues to delineate pneumonia-associated differentially expressed genes (DEGs). Through bioinformatics screening, we established a PCD -related gene signature and validated its clinical relevance via transcriptomic profiling of peripheral blood samples from confirmed S. aureus pneumonia patients. Machine learning (including LASSO regression and SVM-RFE algorithms) were employed to prioritize characteristic biomarkers, followed by construction of a risk-prediction nomogram with Receiver Operating Characteristic (ROC) curve validation. Multidimensional analyses encompassing immune cell infiltration patterns and DSigDB based drug discovery were performed, supplemented by molecular docking simulations and qPCR confirmation of core regulatory elements.

resultsWhole-transcriptome analysis revealed 71 PCD-related DEGs (DE-PCDs) with conserved cross-species dysregulation (19 genes in human specimens). Machine learning identified 11 hub genes modulating apoptosis, necroptosis, autophagy, and ferroptosis interconnections. A four-gene diagnostic panel (NAMPT, NFKBIA, SLC40A1, PRKCQ) demonstrated high predictive accuracy (AUC = 0.92) via nomogram modeling. Significant correlations emerged between biomarkers and neutrophil/T-cell infiltration dynamics, while computational drug screening identified 27 candidate compounds targeting these determinants.

conclusionThis investigation delineates PCD-mediated regulatory networks in S. aureus pneumonia, establishing a clinically translatable biomarker panel with theranostic potential against infectious pulmonary inflammation.

Indexed as

Pneumonia, StaphylococcalStaphylococcus aureusTranscriptomeAnimalsApoptosisBiomarkersCell DeathEarly DiagnosisFemaleGene Expression ProfilingHumansMachine LearningMaleMiceMolecular Docking SimulationBiomarkers

Identifiers

PMID41557688
PMCPMC12818594

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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.