Evidence map›Paper›PMID 40008080›Full record

ArticleJournal of inflammation research2025

Screening and Validation of Potential Biomarkers of Immune Cells in Childhood Asthma Patients via Mendelian Randomization and Machine Learning.

Yang Zhang, Yang Hai, Bangguo Song, Jing Xu, Liangjia Cao, Rukeye Yasen, Wenjuan Xu, Jiaxuan Zhang, Jihong Hu

Abstract read
In one paragraph

Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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.

Yang ZhangScientific Research and Experimental Center, Gansu University of Chinese Medicine, Lanzhou, People's Republic of China.ORCID 0009-0004-3433-1067
Yang HaiScientific Research and Experimental Center, Gansu University of Chinese Medicine, Lanzhou, People's Republic of China.
Bangguo SongClinical College of Chinese Medicine, Gansu University of Chinese Medicine, Lanzhou, People's Republic of China.ORCID 0009-0007-0153-0360
Jing XuScientific Research and Experimental Center, Gansu University of Chinese Medicine, Lanzhou, People's Republic of China.
Liangjia CaoCollege of Public Health, Gansu University of Chinese Medicine, Lanzhou, People's Republic of China.
Rukeye YasenCollege of Public Health, Gansu University of Chinese Medicine, Lanzhou, People's Republic of China.
Wenjuan XuCollege of Public Health, Gansu University of Chinese Medicine, Lanzhou, People's Republic of China.
Jiaxuan ZhangCollege of Public Health, Gansu University of Chinese Medicine, Lanzhou, People's Republic of China.
Jihong HuLaboratory and Simulation Training Center, Gansu University of Chinese Medicine, Lanzhou, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Asthma is one of the most common chronic respiratory diseases affecting children, and there is currently no clear remedy. Immune cells play a key role in childhood asthma. Therefore, a deeper investigation of the correlation between immune cells and childhood asthma could lead to a better understanding of asthma's origin, the identification of potential treatment targets, and the development of personalized treatment strategies. Patients and Methods: We used a two-sample Mendelian randomization (MR) analysis to investigate the possible causal relationship between childhood asthma and a total of 731 immune cells, including B cell (190), Maturation stages of T cell (79), Monocyte (43), Myeloid cell (64), TBNK (124), Treg (167), and CDC (64). LASSO logistic regression and SVM algorithms were used to identify key genes associated with childhood asthma. Specific signaling pathways associated with these key genes were further explored through gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). Subsequently, the four key genes FCGR3A, TCTN3, ALOX5, and IL4R were verified in an established asthma mouse model using quantitative real-time PCR and Western blotting. Results: MR analysis showed that 60 immune cells were associated with childhood asthma, of which 32 were associated with high risk and 28 were associated with low risk. LASSO logistic regression and SVM algorithm identified six key genes that affect childhood asthma as ATF4, FCGR3A, GAS5, MGAT3, TAB1, and TCTN3. In addition, four genes, FCGR3A, TCTN3, ALOX5, and IL4R, were verified through animal experiments. Conclusion: Our findings confirmed that immune cells contribute to childhood asthma, highlighting the importance of key genes in the role of the immune microenvironment in this disease. These insights provide a new path for the exploration of the biological underpinnings of childhood asthma and the development of early intervention therapies.

Indexed as

biomarkerchildhood asthmaimmune cellskey genesMendelian randomization

Identifiers

PMID40008080
PMCPMC11853831

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