Evidence map›Paper›PMID 41181152›Full record

ArticleFrontiers in immunology2025

Integrating bioinformatics and molecular experiments to reveal the critical role of the cellular energy metabolism-related marker PLA2G1B in COPD epithelial cells.

Jun Shi, Zihan Wang, Yafei Rao, Danyang Li, Ying Luo, Yue Zhang, Yuqiang Pei, Xiaoyan Gai, Yongchang Sun

Abstract read
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jun Shi *Department of Respiratory and Critical Care Medicine, Research Center for Chronic Airway Diseases, Peking University Third Hospital, Peking University Health Science Center, Beijing, China.
Zihan Wang *Department of Respiratory and Critical Care Medicine, Research Center for Chronic Airway Diseases, Peking University Third Hospital, Peking University Health Science Center, Beijing, China.
Yafei Rao *Department of Respiratory and Critical Care Medicine, Research Center for Chronic Airway Diseases, Peking University Third Hospital, Peking University Health Science Center, Beijing, China.
Danyang LiDepartment of Respiratory and Critical Care Medicine, Research Center for Chronic Airway Diseases, Peking University Third Hospital, Peking University Health Science Center, Beijing, China.
Ying LuoDepartment of Respiratory and Critical Care Medicine, Research Center for Chronic Airway Diseases, Peking University Third Hospital, Peking University Health Science Center, Beijing, China.
Yue ZhangDepartment of Respiratory and Critical Care Medicine, Research Center for Chronic Airway Diseases, Peking University Third Hospital, Peking University Health Science Center, Beijing, China.
Yuqiang PeiDepartment of Respiratory and Critical Care Medicine, Research Center for Chronic Airway Diseases, Peking University Third Hospital, Peking University Health Science Center, Beijing, China.
Xiaoyan GaiDepartment of Respiratory and Critical Care Medicine, Research Center for Chronic Airway Diseases, Peking University Third Hospital, Peking University Health Science Center, Beijing, China.
Yongchang SunDepartment of Respiratory and Critical Care Medicine, Research Center for Chronic Airway Diseases, Peking University Third Hospital, Peking University Health Science Center, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease characterized by small airway lesions and persistent airflow limitation. Recent studies have highlighted impaired cellular energy metabolism (CEM) in COPD, although the underlying mechanisms remain incompletely understood. Material and methods: This research identified cell energy metabolism-related differentially expressed genes (CEM-DEGs) by collecting CEM-associated signatures from multiple public databases and integrating these markers with data from the GEO database. Subsequently, five machine learning algorithms-Boruta, Xgboost, GBM, SVM-RFE, and LASSO-were employed to screen for key variables. Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis were then performed on these key CEM-DEGs. Finally, the results of the bioinformatics analysis were verified by Results: Bioinformatic analysis identified six critical markers ( Conclusions: This study identified six CEM-related biomarkers (

Indexed as

Computational BiologyEnergy MetabolismEpithelial CellsPulmonary Disease, Chronic ObstructiveAnimalsBiomarkersGene Expression ProfilingHumansMiceBiomarkerscellular energy metabolismchronic obstructive pulmonary diseasemachine learningPLA2G1Bsingle cell sequencing

Identifiers

PMID41181152
PMCPMC12571731

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.