Lu Chenqi, Liu Xiaoqin, Wang Lin, Jiang Ning, Yu Jun, Zhao Xiaobo, Hu Hairong, Zheng Saihua, Li Xuelian, Wang Guiying
Department of Biostatistics and Computational Biology, State Key Laboratory of Genetic Engineering, Department of Gynecology, Obstetrics and Gynecology Hospital, School of Life Sciences, Fudan University, Shanghai, China.
Clinical and Translational Research Center of Shanghai First Maternity and Infant Health Hospital, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Science and Technology, Tongji University, Shanghai, China.
Oncotarget. 2017 Jan 10;8(2):3170-3180. doi: 10.18632/oncotarget.13881.
Due to genetic heterogeneity and variable diagnostic criteria, genetic studies of polycystic ovary syndrome are particularly challenging. Furthermore, lack of sufficiently large cohorts limits the identification of susceptibility genes contributing to polycystic ovary syndrome. Here, we carried out a systematic search of studies deposited in the Gene Expression Omnibus database through August 31, 2016. The present analyses included studies with: 1) patients with polycystic ovary syndrome and normal controls, 2) gene expression profiling of messenger RNA, and 3) sufficient data for our analysis. Ultimately, a total of 9 studies with 13 datasets met the inclusion criteria and were performed for the subsequent integrated analyses. Through comprehensive analyses, there were 13 genetic factors overlapped in all datasets and identified as significant specific genes for polycystic ovary syndrome. After quality control assessment, there were six datasets remained. Further gene ontology enrichment and pathway analyses suggested that differentially expressed genes mainly enriched in oocyte pathways. These findings provide potential molecular markers for diagnosis and prognosis of polycystic ovary syndrome, and need in-depth studies on the exact function and mechanism in polycystic ovary syndrome.
由于基因异质性和诊断标准的差异,多囊卵巢综合征的基因研究极具挑战性。此外,缺乏足够大的队列限制了对多囊卵巢综合征易感基因的识别。在此,我们对截至2016年8月31日存于基因表达综合数据库中的研究进行了系统检索。目前的分析纳入了符合以下条件的研究:1)多囊卵巢综合征患者和正常对照;2)信使核糖核酸的基因表达谱分析;3)有足够的数据用于我们的分析。最终,共有9项研究的13个数据集符合纳入标准,并进行了后续的综合分析。通过全面分析,所有数据集中有13个遗传因素重叠,并被确定为多囊卵巢综合征的显著特异性基因。经过质量控制评估后,还剩下6个数据集。进一步的基因本体富集和通路分析表明,差异表达基因主要富集在卵母细胞通路中。这些发现为多囊卵巢综合征的诊断和预后提供了潜在的分子标志物,并且需要对其在多囊卵巢综合征中的确切功能和机制进行深入研究。