Evidence map›Paper›PMID 36686015›Full record

ArticlePostepy dermatologii i alergologii2022

Integrated bioinformatics-based identification of potential diagnostic biomarkers associated with atopic dermatitis.

Guanghua Chen, Jia Yan

Abstract read
In one paragraph

Article in Postepy dermatologii i alergologii, 2022. 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

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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

2 authors.

Guanghua ChenDepartment of Dermatology, Children's Hospital of Chongqing Medical University, National Clinical Research Centre for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.
Jia YanDigestive Department, University-Town Hospital of Chongqing Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: In-depth analysis of the rambling genes of atopic dermatitis may help to identify the pathologic mechanism of this disease. However, this has seldom been performed. Aim: Using bioinformatics approaches, we analysed 3 gene expression profiles in the gene expression omnibus (GEO) database, identified the differentially expressed genes (DEGs), and found out the overlapping DEGs (common DEGs, cDEGs) in the above 3 profiles. Material and methods: We identified 91 upregulated cDEGs, which were then arranged into a protein-protein interaction (PPI) network, and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) term enrichment analyses were performed to explore the functional roles of these genes. Results: GO analyses revealed these DEGs to be significantly enriched in biological processes including immune system process, immune response, defence response, leukocyte activation, and response to the biotic stimulus. These DEGs were also enriched in the KEGG pathway, including influenza A, amoebiasis, primary immunodeficiency, cytokine-cytokine receptor interaction, and IL-17 signalling pathway. PPI analysis showed that 9 genes (PTPRC-CTLA4-CD274-CD1C-IL7R-GZMB-CCL5-CD83, and CCL22) were probably the novel hub genes of atopic dermatitis. Conclusions: Together, the findings of these bioinformatics analyses thus identified key hub genes associated with AD development.

Indexed as

atopic dermatitisbioinformaticsGEO databasehub genespathways

Identifiers

PMID36686015
PMCPMC9837587

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