Dataset Information

Bioinformatic Analysis and Translational Validation of Psoriasis Candidate Genes for Precision Medicine.

ABSTRACT:

Background

Psoriasis is a recurrent, chronic, inflammation- and immune-mediated skin disease with multiple causative factors. However, the genetic markers associated with recurrence have not yet been fully elucidated. Accordingly, in this study, we aimed to identify markers associated with the recurrence of psoriasis.

Methods

We analyzed differentially expressed genes to determine which targets were associated with the recurrence of psoriasis and used these data to construct a protein-protein interaction network using Cytoscape software. The results were then validated by analysis of core targets using Gene Expression Omnibus (GEO) datasets and clinical samples. Functional enrichment analysis was used to explore the potential mechanisms mediating the recurrence of psoriasis.

Results

We screened out six core targets that played important roles in recurrence of psoriasis, and validation of GEO datasets and clinical samples showed that the expression levels of five core targets were higher in patients with psoriasis than in healthy individuals. Functional enrichment analysis revealed that the cell cycle and oocyte meiosis signaling pathways were involved in the recurrence of psoriasis.

Conclusion

Our findings provided insights into the mechanisms mediating the onset and recurrence of psoriasis.

SUBMITTER: Li AH

PROVIDER: S-EPMC9343179 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Bioinformatic Analysis and Translational Validation of Psoriasis Candidate Genes for Precision Medicine.

Li An-Hai AH Li Wen-Wen WW Yu Xiao-Qian XQ Zhang Dai-Ming DM Liu Yi-Ran YR Li Ding D

Clinical, cosmetic and investigational dermatology 20220728

<h4>Background</h4>Psoriasis is a recurrent, chronic, inflammation- and immune-mediated skin disease with multiple causative factors. However, the genetic markers associated with recurrence have not yet been fully elucidated. Accordingly, in this study, we aimed to identify markers associated with the recurrence of psoriasis.<h4>Methods</h4>We analyzed differentially expressed genes to determine which targets were associated with the recurrence of psoriasis and used these data to construct a pro ...[more]

PMID: 35924255

Similar Datasets

Project description:Psoriasis is an immune‑mediated cutaneous disorder with a high incidence and prevalence. Patients with psoriasis may experience irritation, pain and psychological problems. The cause and underlying molecular etiology of psoriasis remains unknown. In an attempt to achieve a more comprehensive understanding of the molecular pathogenesis of psoriasis, the gene expression profiles of 175 pairs of lesional and corresponding non‑lesional skin samples were downloaded from 5 data sets in the Gene Expression Omnibus (GEO) database. Integrated differentially expressed genes (DEGs) were obtained with the use of R software. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed using the DAVID online analysis tool. The protein‑protein interaction (PPI) network was constructed on the STRING platform and hub genes were calculated with the use of Cytoscape software. Finally, GEO2R was used to determine the expression of the hub genes in scalp psoriasis. A total of 373 genes from the 5 data sets were identified as DEGs, including 277 upregulated and 96 downregulated genes. GO analysis revealed that immune responses and epidermal differentiation/development were the most enriched terms in biological processes, extracellular space/matrix was the most enriched term in cellular components, and endopeptidase inhibitor activity was the most enriched term in molecular functions. In the KEGG pathway enrichment, DEGs were mainly enriched in the metabolic and viral infection‑associated pathways. A total of 17 hub genes were calculated, including CSK2, CDC45, MCM10, SPC25, NDC80, NUF2, AURKA, CENPE, RRM2, DLGP5, HMMR, TTK, IFIT1, RSAD2, IFI6, IFI27 and ISG20, among which interferon‑α‑inducible genes were revealed to display a similar expression pattern as that obtained in scalp psoriasis. This comprehensive bioinformatic re‑analysis of GEO data provides new insights on the molecular pathogenesis of psoriasis and the identification of potential therapeutic targets for the treatment of psoriasis.

Project description:In today's globalized and flat world, a patient can access and seek multiple health and disease management options. A digitally enabled participatory framework that allows an evidence-based informed choice is likely to assume an immense importance in the future. In India, traditional knowledge systems, like Ayurveda, coexist with modern medicine. However, due to limited crosstalk between the clinicians of both disciplines, a patient attempts integrative medicine by seeking both options independently with limited understanding and evidence. There is a need for an integrative medicine platform with a formalized approach, which allows practitioners from the two diverse systems to crosstalk, coexist, and coevolve for an informed cross-referral that benefits the patients. To be successful, this needs frameworks that enable the bridging of disciplines through a common interface with shared ontologies. Ayurgenomics is an emerging discipline that explores the principles and practices of Ayurveda combined with genomics approaches for mainstream integration. The present review highlights how in conjunction with different disciplines and technologies this has provided frameworks for (1) the discovery of molecular correlates to build ontological links between the two systems, (2) the discovery of biomarkers and targets for early actionable interventions, (3) understanding molecular mechanisms of drug action from its usage perspective in Ayurveda with applications in repurposing, (4) understanding the network and P4 medicine perspective of Ayurveda through a common organizing principle, (5) non-invasive stratification of healthy and diseased individuals using a compendium of system-level phenotypes, and (6) developing evidence-based solutions for practice in integrative medicine settings. The concordance between the two contrasting streams has been built through extensive explorations and iterations of the concepts of Ayurveda and genomic observations using state-of-the-art technologies, computational approaches, and model system studies. These highlight the enormous potential of a trans-disciplinary approach in evolving solutions for personalized interventions in integrative medicine settings.

Dataset Information

Bioinformatic Analysis and Translational Validation of Psoriasis Candidate Genes for Precision Medicine.

Background

Methods

Results

Conclusion

Publications

Bioinformatic Analysis and Translational Validation of Psoriasis Candidate Genes for Precision Medicine.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets