Evidence map›Paper›PMID 40964458›Full record

ArticleInternational journal of chronic obstructive pulmonary disease2025

Integrating Mendelian Randomization and Machine Learning to Identify Hypoxia-Related Diagnostic Biomarkers and Causal Relationship in COPD.

Wenhui Fu, Yangli Liu, Renjie Li, Haiying Jin

Abstract read
In one paragraph

Article in International journal of chronic obstructive pulmonary disease, 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. Review
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

4 authors.

Wenhui FuDepartment of Respiratory Medicine, Jinyun People's Hospital, Lishui, Zhejiang, 321400, People's Republic of China.
Yangli LiuDepartment of Respiratory Medicine, Jinyun People's Hospital, Lishui, Zhejiang, 321400, People's Republic of China.
Renjie LiDepartment of Respiratory Medicine, Jinyun People's Hospital, Lishui, Zhejiang, 321400, People's Republic of China.
Haiying JinDepartment of Respiratory Medicine, Jinyun People's Hospital, Lishui, Zhejiang, 321400, People's Republic of China.ORCID 0009-0001-5203-3475

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Chronic obstructive pulmonary disease (COPD) involves progressive lung function decline, with hypoxia playing a key pathogenic role. However, systematic investigations focusing on hypoxia-related genes (HRGs) in COPD remain limited. Methods: We applied machine learning to identify HRG-associated diagnostic biomarkers and evaluated their performance via Receiver Operating Characteristic (ROC) analysis. Mendelian randomization (MR) was conducted to assess causal relationships between candidate genes and COPD. A nomogram model was constructed to evaluate clinical utility, and a ceRNA network was developed using ENCORI database. Results: Six HRG-based diagnostic biomarkers were identified, including Conclusion: Our findings identify

Indexed as

Glucose Transporter Type 1HypoxiaMachine LearningMendelian Randomization AnalysisPulmonary Disease, Chronic ObstructiveDatabases, GeneticGene Regulatory NetworksGenetic Predisposition to DiseaseHumansNomogramsPhenotypePredictive Value of TestsRisk FactorsRNA, Long NoncodingGlucose Transporter Type 1NEAT1 long non-coding RNA, humanRNA, Long NoncodingSLC2A1 protein, humanchronic obstructive pulmonary diseasehypoxia-related genesmachine learningMendelian randomization

Identifiers

PMID40964458
PMCPMC12439713

What Socratic holds

Textmetadata
LicenceCC BY-NC
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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 Socratic graph.