Comprehensive Analysis to Identify Rh Family C Glycoprotein (
DC cells
RHCG
Rh family C glycoprotein
keratinocyte
psoriasis
traditional Chinese medicine
Journal
Drug design, development and therapy
ISSN: 1177-8881
Titre abrégé: Drug Des Devel Ther
Pays: New Zealand
ID NLM: 101475745
Informations de publication
Date de publication:
2023
2023
Historique:
received:
30
05
2023
accepted:
17
08
2023
medline:
5
9
2023
pubmed:
4
9
2023
entrez:
4
9
2023
Statut:
epublish
Résumé
Psoriasis is a complex autoimmune disease. Frequent interactions between epidermal and immune cells are likely to be responsible for the strong heterogeneity of psoriasis. Therefore, our work aims to build on current knowledge and further search for new molecular mechanisms related to psoriasis pathogenesis in order to develop new targeted drugs. Data from psoriasis samples were obtained from the Gene Expression Omnibus (GEO) database, and batch effects were corrected using the "Combat" algorithm in the "SVA" package. Functional annotation of differential genes in psoriasis was performed by Gene set enrichment analysis (GSEA). Core functional modules were identified using the Multiscale Embedded Gene Co-Expression Network Analysis (MEGENA) algorithm for selection from the differential gene interaction network. The expression and potential function of Rh Family C Glycoprotein ( Immune response (represented by C1_2) and collagen matrix formation (represented by C1_3) were identified as two important pathogenic factors in psoriasis and helped to define new biological subtypes of psoriasis. One important psoriasis hub gene, Our study identifies a new causal disease gene (RHCG) and offers potential alternatives for the treatment of psoriasis.
Sections du résumé
Background
UNASSIGNED
Psoriasis is a complex autoimmune disease. Frequent interactions between epidermal and immune cells are likely to be responsible for the strong heterogeneity of psoriasis. Therefore, our work aims to build on current knowledge and further search for new molecular mechanisms related to psoriasis pathogenesis in order to develop new targeted drugs.
Methods
UNASSIGNED
Data from psoriasis samples were obtained from the Gene Expression Omnibus (GEO) database, and batch effects were corrected using the "Combat" algorithm in the "SVA" package. Functional annotation of differential genes in psoriasis was performed by Gene set enrichment analysis (GSEA). Core functional modules were identified using the Multiscale Embedded Gene Co-Expression Network Analysis (MEGENA) algorithm for selection from the differential gene interaction network. The expression and potential function of Rh Family C Glycoprotein (
Results
UNASSIGNED
Immune response (represented by C1_2) and collagen matrix formation (represented by C1_3) were identified as two important pathogenic factors in psoriasis and helped to define new biological subtypes of psoriasis. One important psoriasis hub gene,
Conclusions
UNASSIGNED
Our study identifies a new causal disease gene (RHCG) and offers potential alternatives for the treatment of psoriasis.
Identifiants
pubmed: 37664450
doi: 10.2147/DDDT.S421300
pii: 421300
pmc: PMC10473404
doi:
Substances chimiques
Glycoproteins
0
RHCG protein, human
0
Membrane Glycoproteins
0
Cation Transport Proteins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2593-2611Informations de copyright
© 2023 Zhang et al.
Déclaration de conflit d'intérêts
The authors declare no conflicts of interest in this work.
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