Abstract
Ovarian aging leads to reproductive and endocrine dysfunction, causing the disorder of multiple organs in the body and even declined quality of offspring’s health. However, few studies have investigated the changes in gene expression profile in the ovarian aging process. Here, we applied integrated bioinformatics to screen, identify, and validate the critical pathogenic genes involved in ovarian aging and uncover potential molecular mechanisms. The expression profiles of GSE84078 were downloaded from the Gene Expression Omnibus (GEO) database, which included the data from ovarian samples of 10 normal C57BL/6 mice, including old (21–22 months old, ovarian failure period) and young (5–6 months old, reproductive bloom period) ovaries. First, we filtered 931 differentially expressed genes (DEGs), including 876 upregulated and 55 downregulated genes through comparison between ovarian expression data from old and young mice. Functional enrichment analysis showed that biological functions of DEGs were primarily immune response regulation, cell–cell adhesion, and phagosome pathway. The most closely related genes among DEGs (Tyrobp, Rac2, Cd14, Zap70, Lcp2, Itgb2, H2-Ab1, and Fcer1g) were identified by constructing a protein–protein interaction (PPI) network and consequently verified using mRNA and protein quantitative detection. Finally, the immune cell infiltration in the ovarian aging process was also evaluated by applying CIBERSORT, and a correlation analysis between hub genes and immune cell type was also performed. The results suggested that plasma cells and naïve CD4+ T cells may participate in ovarian aging. The hub genes were positively correlated with memory B cells, plasma cells, M1 macrophages, Th17 cells, and immature dendritic cells. In conclusion, this study indicates that screening for DEGs and pathways in ovarian aging using bioinformatic analysis could provide potential clues for researchers to unveil the molecular mechanism underlying ovarian aging. These results could be of clinical significance and provide effective molecular targets for the treatment of ovarian aging.
Highlights
Aging is an inevitable, complex, and detrimental process and is among the most significant known risk factors for most human diseases and diseases including tumors, metabolic syndromes, and decline in female fertility (Hayden, 2015)
The identified differentially expressed genes (DEGs) in the ovaries of old and young mice were further analyzed via gene ontology (GO) and KEGG pathway analysis using the “clusterProfiler” package (Yu et al, 2012)
The GO enrichment analysis classified the DEGs into three functional groups, including biological process, cellular component, and molecular function (Gene Ontology Consortium, 2014)
Summary
Complex, and detrimental process and is among the most significant known risk factors for most human diseases and diseases including tumors, metabolic syndromes, and decline in female fertility (Hayden, 2015). The progressive decline in the female ovarian function with increasing chronological age is known as ovarian aging, which leads to reproductive and endocrine dysfunction. Ovarian aging is governed by the gradual declines in the quantity and quality of ovarian follicles, resulting in menopause, the natural consequence of physiological reproductive aging (Younis, 2012). It is an important issue associated with the health of women and their offspring (Wise et al, 1996). It is necessary to study and identify the changes in gene expression profiles of aging ovaries to facilitate the early assessment and intervention of ovarian aging
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