Evidence map›Paper›PMID 40546401›Full record

ArticleJournal of inflammation research2025

Integrated Multi-Omics Analyses Reveal Innovative Diagnostic and Therapeutic Targets Associated with Atopic Dermatitis.

Xiangjie Chen, Bochun Cao, Zhiren Tan, Xiaoping Li, Wenrong Xu, Ying Liu, Fang Gong

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 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. Article
  2. 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

7 authors.

Xiangjie Chen *Department of Laboratory Medicine, Jiangnan University Medical Center (Wuxi No. 2 People's Hospital), Wuxi, Jiangsu, People's Republic of China.
Bochun Cao *Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, People's Republic of China.
Zhiren TanWuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, People's Republic of China.
Xiaoping LiDepartment of Laboratory Medicine, the First Affiliated Hospital of Soochow University, Soochow University, Suzhou, People's Republic of China.
Wenrong XuDepartment of Dermatology, The Affiliated Wuxi People' s Hospital of Nanjing Medical University, Wuxi, People's Republic of China.
Ying LiuWuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, People's Republic of China.
Fang GongDepartment of Laboratory Medicine, Jiangnan University Medical Center (Wuxi No. 2 People's Hospital), Wuxi, Jiangsu, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Atopic dermatitis (AD) is a chronic skin disorder that impacts patients' physical and mental health. Diagnosing AD mainly depends on evaluating medical history and symptoms, as there are no universally accepted biomarkers for it. Identifying novel, reliable biomarkers is crucial to enhance diagnostic accuracy, reduce healthcare costs, and aid in developing new treatments. Methods: Data from the Gene Expression Omnibus database were used to identify potential AD biomarkers through Weighted Gene Co-expression Network Analysis and machine-learning. External datasets confirmed these biomarkers' diagnostic utility and their effectiveness in assessing clinical treatment. We also gathered peripheral blood mononuclear cells from healthy individuals and AD patients to validate these biomarkers' diagnostic capability for AD. Correlation analyses linked these biomarkers to AD severity indicators. Euclidean distance clustering was employed to assess the ability of these biomarkers to distinguish between healthy individuals and AD patients. The study also examined their relationships with major inflammatory pathways in AD to understand their mechanisms. Results: The study identified Ribonucleotide Reductase Regulatory Subunit M2 (RRM2), Late Cornified Envelope 3D (LCE3D), Cornifelin (CNFN), and Small Proline Rich Protein 2G (SPRR2G) as biomarkers with greater diagnostic value for AD than traditional biomarkers like eosinophil count and IgE levels. Treatment led to decreased expression of RRM2, LCE3D, and CNFN in AD patients' skin, indicating their potential as markers for evaluating treatment efficacy. LCE3D, CNFN, and SPRR2G correlated with AD severity indicators such as the SCORAD score and serum IgE levels. Additionally, the overexpression of these biomarkers was linked to the activation of inflammatory pathways, suggesting their role in AD pathogenesis and progression. Conclusion: Our study identifies RRM2, LCE3D, CNFN, and SPRR2G as novel biomarkers for diagnosing AD in peripheral blood and lesional tissues, with potential for assessing disease severity, evaluating treatment efficacy, and serving as targets for diagnosis and treatment.

Indexed as

ADatopic dermatitisbioinformaticsbiomarkertranscriptome

Identifiers

PMID40546401
PMCPMC12182234

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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.