Ma Lingwei, Lu Huan, Chen Runhua, Wu Meng, Jin Yan, Zhang Jinjin, Wang Shixuan
Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Genet. 2020 Nov 16;11:590660. doi: 10.3389/fgene.2020.590660. eCollection 2020.
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 (, , , , , , , and ) 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.
卵巢衰老会导致生殖和内分泌功能障碍，引发身体多个器官的紊乱，甚至导致后代健康质量下降。然而，很少有研究调查卵巢衰老过程中基因表达谱的变化。在此，我们应用综合生物信息学方法来筛选、鉴定和验证参与卵巢衰老的关键致病基因，并揭示潜在的分子机制。从基因表达综合数据库（GEO）下载了GSE84078的表达谱，其中包括10只正常C57BL/6小鼠卵巢样本的数据，包括老年（21 - 22月龄，卵巢衰竭期）和年轻（5 - 6月龄，生殖旺盛期）卵巢。首先，通过比较老年和年轻小鼠的卵巢表达数据，我们筛选出931个差异表达基因（DEGs），其中包括876个上调基因和55个下调基因。功能富集分析表明，DEGs的生物学功能主要是免疫反应调节、细胞间粘附和吞噬体途径。通过构建蛋白质-蛋白质相互作用（PPI）网络鉴定出DEGs中最密切相关的基因（ 、 、 、 、 、 、 和 ），并随后使用mRNA和蛋白质定量检测进行验证。最后，应用CIBERSORT评估卵巢衰老过程中的免疫细胞浸润，并对枢纽基因与免疫细胞类型进行相关性分析。结果表明浆细胞和幼稚CD4 T细胞可能参与卵巢衰老。枢纽基因与记忆B细胞、浆细胞、M1巨噬细胞、Th17细胞和未成熟树突状细胞呈正相关。总之，本研究表明利用生物信息学分析筛选卵巢衰老中的DEGs和途径可为研究人员揭示卵巢衰老的分子机制提供潜在线索。这些结果可能具有临床意义，并为卵巢衰老的治疗提供有效的分子靶点。
