Exploration of hub genes involved in PCOS using biological informatics methods.
Medicine (Baltimore)
; 101(40): e30905, 2022 Oct 07.
Article
de En
| MEDLINE
| ID: mdl-36221354
BACKGROUND: The aim of this study was to find underlying genes and their interaction mechanism crucial to the polycystic ovarian syndrome (PCOS) by analyzing differentially expressed genes (DEGs) between PCOS and non-PCOS subjects. METHODS: Gene expression data of PCOS and non-PCOS subjects were collected from gene expression omnibus (GEO) database. GEO2R were used to calculating P value and logFC. The screening threshold of DEGs was Pâ
<â
.05 and | FC |â
≥â
1.2. GO annotation and Kyoto encyclopedia of genes and genomes (KEGG) signaling pathway enrichment analysis was performed by using DAVID (2021 Update). The protein-protein interaction (PPI) network of DEGs was constructed by using the STRING database, and the hub genes were recognized through Hubba plugin of Cytoscape software. RESULTS: PCOS and non-PCOS subjects shared a total of 174 DGEs, including 14 upregulated and 160 downregulated genes. The GO biological processes enriched by DEGs mainly involved actin cytoskeleton organization, positive regulation of NF-κB signaling pathway, and positive regulation of canonical Wnt signaling pathway. The DEGs were significantly enriched in cytoplasm, nucleus and cytosol. Their molecular functions mainly focused on protein binding, calmodulin binding and glycerol-3-phosphate dehydrogenase activity. The PI3K/Akt signaling pathway and glycosaminoglycan biosynthesis were highlighted as critical pathways enriched by DEGs. 10 hub genes were screened from the constructed PPI network, of which EGF, FN1 and TLR4 were mainly enriched in the PI3K/Akt signaling pathway. CONCLUSION: In this study, a total of 174 DEGs and 10 hub genes were identified as new candidate targets for insulin resistance (IR) in PCOS individuals, which may provide a new direction for developing novel treatment strategies for PCOS.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Syndrome des ovaires polykystiques
/
Biologie informatique
Type d'étude:
Prognostic_studies
Limites:
Female
/
Humans
Langue:
En
Journal:
Medicine (Baltimore)
Année:
2022
Type de document:
Article
Pays d'affiliation:
Chine
Pays de publication:
États-Unis d'Amérique