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      Effects and plasma proteomic analysis of GLP-1RA versus CPA/EE, in combination with metformin, on overweight PCOS women: a randomized controlled trial

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          Abstract

          Purpose

          Polycystic ovary syndrome (PCOS) is characterized by reproductive dysfunctions and metabolic disorders. This study aims to compare the therapeutic effectiveness of glucagon-like peptide-1 receptor agonist (GLP-1RA) + Metformin (Met) versus cyproterone acetate/ethinylestradiol (CPA/EE) + Met in overweight PCOS women and identify potential proteomic biomarkers of disease risk in women with PCOS.

          Methods

          In this prospective, open-label randomized controlled trial, we recruited 60 overweight PCOS women into two groups at a 1:1 ratio to receive CPA/EE (2 mg/day: 2 mg cyproterone acetate and 35-μg ethinylestradiol,) +Met (1500 mg/day) or GLP-1 RA (liraglutide, 1.2–1.8 mg/day) +Met (1500 mg/day) for 12 weeks. The clinical effectiveness and adverse effects were evaluated, followed by plasma proteomic analysis and verification of critical biomarkers by ELISA.

          Results

          Eighty(80%) patients completed the study. Both interventions improved menstrual cycle, polycystic ovaries, LH(luteinizing hormone) and HbA1c(hemoglobin A1c) levels after the 12-week treatment. GLP-1RA + Met was more effective than CPA/EE + Met in reducing body weight, BMI (Body Mass Index), and waist circumference, FBG(fasting blood glucose), AUCI(area under curve of insulin),TC (Total Cholesterol), IL-6(Interleukin-6) and improving insulin sensitivity, and ovulation in overweight women with PCOS, with acceptable short-term side effects. CPA/EE + Met was more effective in improving hyperandrogenemia, including T(total testosterone), LH, LH/FSH(Luteinizing hormone/follicle-stimulating hormone), SHBG(sex hormone-binding globulin) and FAI (free androgen index). By contract, GLP-1RA+Met group only improved LH. Plasma proteomic analysis revealed that the interventions altered proteins involved in reactive oxygen species detoxification (PRDX6, GSTO1, GSTP1, GSTM2), platelet degranulation (FN1), and the immune response (SERPINB9).

          Conclusions

          Both CPA/EE+Met and GLP-1RA + Met treatment improved reproductive functions in overweight PCOS women. GLP-1RA + Met was more effective than CPA/EE + Met in reducing body weight, BMI, and waist, and improving metabolism, and ovulation in overweight women with PCOS, with acceptable short-term side effects. CPA/EE + Met was more effective in reducing hyperandrogenemia. The novel plasma biomarkers PRDX6, FN1, and SERPINB9, might be indicators and targets for PCOS treatment.

          Trial registration ClinicalTials.gov Trial No:

          NCT03151005. Registered 12 May, 2017, https://clinicaltrials.gov/ct2/show/NCT03151005.

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          Most cited references44

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          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Contributors
                zheng.sun@bcm.edu
                fnf7703@hotmail.com
                longmin_casper@163.com
                Journal
                Endocrine
                Endocrine
                Endocrine
                Springer US (New York )
                1355-008X
                1559-0100
                31 August 2023
                31 August 2023
                2024
                : 83
                : 1
                : 227-241
                Affiliations
                [1 ]GRID grid.410570.7, ISNI 0000 0004 1760 6682, Department of Endocrinology, Translational Research Key Laboratory for Diabetes, Xinqiao Hospital, , Army Medical University, ; Chongqing, 400037 China
                [2 ]Department of Endocrinology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210016 China
                [3 ]GRID grid.9227.e, ISNI 0000000119573309, Key Laboratory of Genetic Network Biology, Collaborative Innovation Center of Genetics and Development, Institute of Genetics and Developmental Biology, , Chinese Academy of Sciences, ; 100101 Beijing, China
                [4 ]Univeristy of Chinese Academy of Sciences, ( https://ror.org/05qbk4x57) 100049 Beijing, China
                [5 ]GRID grid.410570.7, ISNI 0000 0004 1760 6682, Department of Endocrinology, Southwest Hospital, , Army Medical University (Third Military Medical University), ; Chongqing, 400038 China
                [6 ]Department of Medicine, Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, ( https://ror.org/02pttbw34) Houston, TX USA
                Author information
                http://orcid.org/0000-0003-1071-8131
                Article
                3487
                10.1007/s12020-023-03487-4
                10806039
                37653215
                2a07e199-14c0-4798-940e-f7d42b6a79ad
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 April 2023
                : 9 August 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/, Chongqing Natural Science Foundation;
                Award ID: No.cstc2020jcyj-jqx0017
                Award ID: No.cstc2020jcyj-jqx0017
                Award ID: No.cstc2020jcyj-jqx0017
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/http://dx.doi.org/10.13039/501100014219, National Science Fund for Distinguished Young Scholars;
                Award ID: No. 81925007
                Award Recipient :
                Categories
                Original Article
                Custom metadata
                © Springer Science+Business Media, LLC, part of Springer Nature 2024

                Endocrinology & Diabetes
                pcos (polycystic ovary syndrome),glucagon-like peptide-1 receptor agonist (glp-1 ra),metformin,proteomics analysis

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