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Discovery of novel serum metabolic biomarkers in patients with polycystic ovarian syndrome and premature ovarian failure

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journal contribution
posted on 2021-10-26, 08:40 authored by Jiying Chen, Qinger Zhou, Yonggang Zhang, Wenqing Tan, Hanchao Gao, Liying Zhou, Shuixiu Xiao, Jinhua Gao, Jing Li, Zhiying Zhu

Several widely recognized metabolites play a role in regulating the pathophysiological processes of various disorders. Nonetheless, the lack of effective biomarkers for the early diagnosis of polycystic ovarian syndrome (PCOS) and premature ovarian failure (POF) has led to the discovery of serum-based metabolic biomarkers for these disorders. We aimed to identify various differentially expressed metabolites (DEMs) through serum-based metabolic profiling in patients with PCOS and POF and in healthy individuals by using liquid chromatography–mass spectrometry analysis. Furthermore, heatmap clustering, correlation, and Z-score analyses were performed to identify the top DEMs. Kyoto Encyclopedia of Genes and Genomes enriched pathways of DEMs were determined using metabolite-based databases. Moreover, the clinical significance of these DEMs was evaluated on the basis of area under the receiver operating characteristic curve. Significantly dysregulated expressions of several metabolites were observed in the intergroup comparisons of the PCOS, POF, and healthy control groups. Furthermore, 6 DEMs were most frequently observed among the three groups. The expressions of these DEMs were not only directly correlated but also exhibited potential significance in patients with PCOS and POF. Novel metabolites with up/downregulated expressions can be discovered in patients with PCOS and POF using serum-based metabolomics; these metabolites show good diagnostic performance and can act as effective biomarkers for the early detection of PCOS and POF. Furthermore, these metabolites might be involved in the pathophysiological mechanisms of PCOS and POF via interplay with corresponding genes.

Funding

This research was financially supported in part by a grant from the Key Laboratory of Science and Technology Innovation (Longhua District, Shenzhen, China) grant number of 20170913A0410028.

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