Open AccessConstruction of a three commitment points for S phase entry cell cycle model and immune-related ceRNA network to explore novel therapeutic options for psoriasis
Author(s) -
Jingxi Xu,
Jiangtao Li
Publication year - 2022
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022630
Subject(s) - competing endogenous rna , subnetwork , computational biology , psoriasis , gene regulatory network , biology , cell cycle , transcriptome , gene , computer science , bioinformatics , gene expression , genetics , rna , long non coding rna , immunology , computer network
While competing endogenous RNAs (ceRNAs) play pivotal roles in various diseases, the proliferation and differentiation of keratinocytes are becoming a research focus in psoriasis. Therefore, the three commitment points for S phase entry (CP1-3) cell cycle model has pointed to a new research direction in these areas. However, it is unclear what role ceRNA regulatory mechanisms play in the interaction between keratinocytes and the immune system in psoriasis. In addition, the ceRNA network-based screening of potential therapeutic agents for psoriasis has not been explored. Therefore, we used multiple bioinformatics approaches to construct a ceRNA network for psoriasis, identified CTGF as the hub gene, and constructed a ceRNA subnetwork, after which validation datasets authenticated the results' accuracy. Subsequently, we used multiple online databases and the single-sample gene-set enrichment analysis algorithm, including the CP1-3 cell cycle model, to explore the mechanisms accounting for the increased proliferation and differentiation of keratinocytes and the possible roles of the ceRNA subnetwork in psoriasis. Next, we performed cell cycle and cell trajectory analyses based on a single-cell RNA-seq dataset of psoriatic skin biopsies. We also used weighted gene co-expression network analysis and single-gene batch correlation analysis-based gene set enrichment analysis to explore the functions of CTGF. Finally, we used the Connectivity Map to identify MS-275 (entinostat) as a novel treatment for psoriasis, SwissTargetPrediction to predict drug targets, and molecular docking to investigate the minimum binding energy and binding sites of the drug to target proteins.