Research Papers:

Integrated analyses for genetic markers of polycystic ovary syndrome with 9 case-control studies of gene expression profiles

Chenqi Lu, Xiaoqin Liu, Lin Wang, Ning Jiang, Jun Yu, Xiaobo Zhao, Hairong Hu, Saihua Zheng, Xuelian Li and Guiying Wang _

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Oncotarget. 2017; 8:3170-3180. https://doi.org/10.18632/oncotarget.13881

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Chenqi Lu1, Xiaoqin Liu2, Lin Wang3, Ning Jiang1, Jun Yu2, Xiaobo Zhao2, Hairong Hu1, Saihua Zheng1, Xuelian Li1, Guiying Wang2

1Department 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

2Clinical 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

3Department of Endocrinology, East Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China

Correspondence to:

Guiying Wang, email: [email protected]

Xuelian Li, email: [email protected]

Keywords: polycystic ovary syndrome, common markers, integrated analysis, susceptibility, gene expression profile

Received: September 14, 2016     Accepted: December 01, 2016     Published: December 10, 2016


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.

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