ArticleMediators of inflammation2022
Exploration of Biomarkers of Psoriasis through Combined Multiomics Analysis.
Article in Mediators of inflammation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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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.
The trial behind it
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China.Frontiers in digital health · 2025Pooled it
- Multi‑omics and their integration in psoriasis research (Review).Molecular medicine reports · 2026Review
- Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity.Communications medicine · 2026Article
- Association and Mechanistic Insights Linking Bisphenol A to Psoriasis: Integrative Analysis of Bulk Transcriptomics, Network Toxicology, Mendelian Randomization and Single‑cell RNA Sequencing.Clinical, cosmetic and investigational dermatology · 2026Article
- Downregulation of Mitophagy, Complex I Biogenesis, and Signaling by ROBO Receptors-Implications for Psoriasis Pathogenesis.International journal of molecular sciences · 2025Article
- Identification and validation of shared biomarkers and drug repurposing in psoriasis and Crohn's disease: integrating bioinformatics, machine learning, and experimental approaches.Frontiers in immunology · 2025Article
- Tanshinone I Ameliorates Psoriasis-Like Dermatitis by Suppressing Inflammation and Regulating Keratinocyte Differentiation.Drug design, development and therapy · 2025Article
- Recent progress on DNA methylation in psoriasis.Frontiers in genetics · 2025Review
- Identifying key inflammatory genes in psoriasis via weighted gene co-expression network analysis: Potential targets for therapy.Biomolecules & biomedicine · 2024Article
- Advancing Psoriasis Care through Artificial Intelligence: A Comprehensive Review.Current dermatology reports · 2024Article
- Identification of biomarkers for abdominal aortic aneurysm in Behçet's disease via mendelian randomization and integrated bioinformatics analyses.Journal of cellular and molecular medicine · 2024Article
- Article
- Genome-Wide Differential Transcription of Long Noncoding RNAs in Psoriatic Skin.International journal of molecular sciences · 2023Article
- Psoriasis Associated Hub Genes Revealed by Weighted Gene Co-Expression Network Analysis.Cell journal · 2023Article
- Comprehensive Analysis to Identify Rh Family C Glycoprotein (Drug design, development and therapy · 2023Article
- Identification of endoplasmic reticulum stress-related biomarkers of diabetes nephropathy based on bioinformatics and machine learning.Frontiers in endocrinology · 2023Article
- Omics and Multi-Omics Analysis for the Early Identification and Improved Outcome of Patients with Psoriatic Arthritis.Biomedicines · 2022Review
- Exploration of Biomarkers of Psoriasis through Combined Multiomics Analysis.Mediators of inflammation · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Aberrant DNA methylation patterns are of increasing interest in the study of psoriasis mechanisms. This study aims to screen potential diagnostic indicators affected by DNA methylation for psoriasis based on bioinformatics using multiple machine learning algorithms and to preliminarily explore its molecular mechanisms. Methods: GSE13355, GSE14905, and GSE73894 were collected from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated region- (DMR-) genes between psoriasis and control samples were combined to obtain differentially expressed methylated genes. Subsequently, a protein-protein interaction (PPI) network was established to analyze the interaction between differentially expressed methylated genes. Moreover, the hub genes of psoriasis were screened by the least absolute shrinkage and selection operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM), which were further performed single-gene gene set enrichment analysis (GSEA) to clarify the pathogenesis of psoriasis. The druggable genes were predicted using DGIdb. Finally, the expressions of hub genes in psoriasis lesions and healthy controls were detected by immunohistochemistry (IHC) and quantitative real-time PCR (RT-qPCR). Results: In this study, a total of 767 DEGs and 896 DMR-genes were obtained. Functional enrichment showed that they were significantly associated with skin development, skin barrier function, immune/inflammatory response, and cell cycle. The combined transcriptomic and DNA methylation data resulted in 33 differentially expressed methylated genes, of which GJB2 was the final identified hub gene for psoriasis, with robust diagnostic power. IHC and RT-qPCR showed that GJB2 was significantly higher in psoriasis samples than those in healthy controls. Additionally, GJB2 may be involved in the development and progression of psoriasis by disrupting the body's immune system, mediating the cell cycle, and destroying the skin barrier, in addition to possibly inducing diseases related to the skeletal aspects of psoriasis. Moreover, OCTANOL and CARBENOXOLONE were identified as promising compounds through the DGIdb database. Conclusion: The abnormal expression of GJB2 might play a critical role in psoriasis development and progression. The genes identified in our study might serve as a diagnostic indicator and therapeutic target in psoriasis.
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