ArticleHereditas2020
Identification of candidate biomarkers and pathways associated with psoriasis using bioinformatics analysis.
Article in Hereditas, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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.
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.
Who cites it
14 citing papers in PubMed, 25 citations in OpenAlex.
- Comprehensive Metabolomic Profiling of Skin Lesions from Psoriasis Patients Reveals Disease Signatures.International journal of biological sciences · 2026Article
- Downregulation of Mitophagy, Complex I Biogenesis, and Signaling by ROBO Receptors-Implications for Psoriasis Pathogenesis.International journal of molecular sciences · 2025Article
- Anti-Inflammatory Effects of Curcumin-Based Nanoparticles Containing α-Linolenic Acid in a Model of Psoriasis In Vitro.Nutrients · 2025Article
- Elucidating the molecular association and potential mechanisms between psoriasis and atrial fibrillation through biomarker and immune infiltration analysis.Archives of dermatological research · 2025Article
- FLI1 Induces Plaque Psoriasis and Its Inhibition Attenuates Disease Progression.Journal of inflammation research · 2025Article
- Article
- Bioinformatics analysis to reveal the potential comorbidity mechanism in psoriasis and nonalcoholic steatohepatitis.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2023Article
- Comprehensive Analysis to Identify Rh Family C Glycoprotein (Drug design, development and therapy · 2023Article
- Identification of key apoptosis-related genes and immune infiltration in the pathogenesis of psoriasis.Hereditas · 2022Article
- Network analysis of potential risk genes for psoriasis.Hereditas · 2021Article
- Maximizing the Utility of Transcriptomics Data in Inflammatory Skin Diseases.Frontiers in immunology · 2021Review
- The Multifaceted Roles of Proline in Cell Behavior.Frontiers in cell and developmental biology · 2021Review
- RPL22 Overexpression Promotes Psoriasis-Like Lesion by Inducing Keratinocytes Abnormal Biological Behavior.Frontiers in immunology · 2021Article
- 2,4-Dimethoxy-6-Methylbenzene-1,3-diol, a Benzenoid FromFrontiers in immunology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe aim of this study was to identify the candidate biomarkers and pathways associated with psoriasis. GSE13355 and GSE14905 were extracted from the Gene Expression Omnibus (GEO) database. Then the differentially expressed genes (DEGs) with |logFC| > 2 and adjusted P < 0.05 were chosen. In addition, the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for DEGs were performed. Then, the GO terms with P < 0.05 and overlap coefficient greater than 0.5 were integrated by EnrichmentMap. Additionally, risk subpathways analysis for DEGs was also conducted by using the iSubpathwayMiner package to obtain more psoriasis-related DEGs and pathways. Finally, protein-protein interaction (PPI) network analysis was performed to identify the hub genes, and the DGIdb database was utilized to search for the candidate drugs for psoriasis.
resultsA total of 127 DEGs which were mostly associated with keratinization, keratinocyte differentiation, and epidermal cell differentiation biological processes were identified. Based on these GO terms, 3 modules (human skin, epidermis and cuticle differentiation, and enzyme activity) were constructed. Moreover, 9 risk subpathways such as steroid hormone biosynthesis, folate biosynthesis, and pyrimidine metabolism were screened. Finally, PPI network analysis demonstrated that CXCL10 was the hub gene with the highest degree, and CXCR2, CXCL10, IVL, OASL, and ISG15 were the potential gene targets of the drugs for treating psoriasis.
conclusionPsoriasis may be mostly caused by keratinization, keratinocyte differentiation, and epidermal cell differentiation; the pathogeneses were more related with pathways such as steroid hormone biosynthesis, folate biosynthesis, and pyrimidine metabolism. Besides, some psoriasis-related genes such as SPRR genes, HSD11B1, GGH, CXCR2, IVL, OASL, ISG15, and CXCL10 may be important targets in psoriatic therapy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.