Identification of Potential Biomarkers of Polycystic Ovary Syndrome via Integrated Bioinformatics Analysis
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REPRODUCTIVE ENDOCRINOLOGY: ORIGINAL ARTICLE
Identification of Potential Biomarkers of Polycystic Ovary Syndrome via Integrated Bioinformatics Analysis Dongyong Yang 1 & Na Li 2 & Aiping Ma 3 & Fangfang Dai 1 & Yajing Zheng 1 & Xuejia Hu 4 & Yanqing Wang 1 & Shu Xian 1 & Li Zhang 1 & Mengqin Yuan 1 & Shiyi Liu 1 & Zhimin Deng 1 & Yi Yang 4 & Yanxiang Cheng 1 Received: 4 June 2020 / Accepted: 5 October 2020 # Society for Reproductive Investigation 2020
Abstract Polycystic ovary syndrome (PCOS) is a life-long reproductive, endocrine, and metabolic disorder that affects up to 17% of women of reproductive age. However, the effect of granulosa cells (GCs) transcriptome changes on oocyte capacity and follicular development in patients with PCOS has not been elucidated. This study aims to analyze transcriptome changes in GCs of PCOS from different perspectives and explore potential biomarkers for the diagnosis and treatment of PCOS. The gene expression profiles of GSE34526 and GSE107746 were obtained from the GEO database. Differentially expressed genes (DEGs) and key signaling pathways were identified. Gene Set Enrichment Analysis (GSEA) revealed that Toll-like receptors, NOD-like receptors, and NOTCH signaling pathways were obviously enriched in GCs of PCOS. We further analyzed DEGs from three aspects: transcription factors (TFs), secreted proteins, and follicular development. Compared with normal GCs, the DEGs encoding TFs and secretory proteins in GCs of PCOS remarkably changed. Besides, HAS2 and CBLN1, which are highly expressed in preovulatory follicular GCs and may trigger ovulation, were significantly decreased in GCs of PCOS. This study found candidate genes and signaling pathways in PCOS, providing new insights and foundations for the etiology of PCOS. Besides, HSA2 and CBLN1 may be potential therapeutic biomarkers for ovulation disorders in PCOS. Keywords Polycystic ovary syndrome . Granulosa cell . Follicle development . Bioinformatics analysis . Biomarkers
Introduction Dongyong Yang, Na Li, and Aiping Ma are authors should be regarded as joint First Authors. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s43032-020-00352-x) contains supplementary material, which is available to authorized users. * Yi Yang [email protected] * Yanxiang Cheng [email protected] 1
Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan 430060, China
2
Department of Oncology, Renmin Hospital of Wuhan University, Wuhan 430060, China
3
Department of Obstetrics and Gynecology, People’s Hospital of Hanchuan, Hanchuan 431600, China
4
School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan 430072, China
Polycystic ovary syndrome (PCOS) is a life-long reproductive, endocrine, and metabolic disorder that affects up to 17% of women of reproductive age and is characterized as ovulatory dysfunction, hyperandrogenemia, and polycystic ovaries [1]. With oligoovulation or anovul
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