Evidence map›Paper›PMID 39650190›Full record

ArticleFrontiers in medicine2024

Identifying biomarkers of endoplasmic reticulum stress and analyzing immune cell infiltration in chronic obstructive pulmonary disease using machine learning.

Shuaiyang Zhang, Hangyu Duan, Jun Yan

Abstract read
In one paragraph

Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Integrative multi-omics identifies CYP1B1 as a candidate molecular link between toxicant exposure and ferroptosis-related epithelial stress in COPD.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026
    Article
  2. 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

3 authors.

Shuaiyang Zhang *Dongzhimen Hospital of Beijing University of Chinese Medicine, Beijing, China.
Hangyu Duan *Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China.
Jun YanDongzhimen Hospital of Beijing University of Chinese Medicine, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Endoplasmic reticulum stress (ERS) is a crucial factor in the progression of chronic obstructive pulmonary disease (COPD). However, the key genes associated with COPD and immune cell infiltration remain to be elucidated. Therefore, this study aimed to identify biomarkers pertinent to the diagnosis of ERS in COPD and delve deeper into the association between pivotal genes and their possible interactions with immune cells. Methods: We selected the genetic data of 189 samples from the Gene Expression Omnibus database, including 91 control and 98 COPD samples. First, we identified the differentially expressed genes between patients with COPD and controls and then screened the ERS genes associated with COPD. Second, 22 core ERS genes associated with COPD were screened using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model and Support Vector Machine Recursive Feature Elimination (SVM-RFE), and the predictive effects of the screened core genes in COPD were evaluated. Third, we explored immune cell infiltration associated with COPD and conducted an in-depth analysis to explore the possible connections between the identified key genes and their related immune cells. Results: A total of 66 differentially expressed endoplasmic reticulum stress-related genes (DE-ERGs) were identified in this study, among which 12 were upregulated and 54 were downregulated. The 22 key genes screened were as follows: AGR3, BCHE, CBY1, CHRM3, CYP1B1, DCSTAMP, DDHD1, DMPK, EDEM3, EDN1, FKBP10, HSPA2, KPNA2, LGALS3, MAOB, MMP9, MPO, MTTP, PIK3CA, PTGIS, PURA, and TMCC1. Their expression was significantly different between COPD and healthy samples, and the difference between the groups was significant. Receiver operating characteristic curve analysis revealed that CBY1 (area under the curve [AUC] = 0.800), BCHE (AUC = 0.773), EDEM3 (AUC = 0.768), FKBP10 (AUC = 0.760), MAOB (AUC = 0.736), and MMP9 (AUC = 0.729) showed a strong ability to distinguish COPD samples from normal samples. Immune cell infiltration results associated with the three key genes were also obtained. Conclusion: The insights of our study have the potential to present new evidence for exploring emerging diagnostic signs of COPD while also contributing to a better understanding of its developmental mechanisms.

Indexed as

BCHECBY1chronic obstructive pulmonary diseaseEDEM3endoplasmic reticulum stressimmune cell infiltrationmachine learning

Identifiers

PMID39650190
PMCPMC11620885

What Socratic 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 Socratic graph.