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Proteomics analysis of potential serum biomarkers for insulin resistance in patients with polycystic ovary syndrome.

ABSTRACT: The aim of the present study was to identify potential serum biomarkers for insulin resistance (IR) in patients with polycystic ovary syndrome (PCOS) by comparing the differences in serum protein expression levels between PCOS patients with and without IR. PCOS patients aged from 18 to 35 years were recruited at Guangdong Women and Children's Hospital from January, 2013 to February, 2014. A total of 218 PCOS patients were enrolled and divided into the insulin resistance (PCOS?IR) and non?insulin resistance (PCOS?NIR) groups according to their homeostasis model assessment of insulin resistance. Two?dimensional difference gel electrophoresis (2D?DIGE) and matrix?assisted laser desorption/ionization time?of?flight mass spectrometry (MALDI?TOF?MS/MS) techniques were used to identify differences in protein expression levels between the PCOS?IR and PCOS?NIR groups. The present study demonstrated that the total cholesterol (TCH), triglycerides (TG), low?density lipoprotein (LDL), fasting plasma glucose (FPG), 3?h blood glucose (3hBG) and uric acid (UA) levels in the PCOS?IR group were higher than those in the PCOS?NIR group (P<0.05). Between the PCOS?IR and PCOS?NIR groups, a total of 20 differentially expressed protein spots were detected by 2D?DIGE. Among these, 4 proteins, namely afamin, serotransferrin, complement C3 and apolipoprotein C3 (APOC3), were also identified by MALDI?TOF?MS/MS. The alteration of APOC3 was further confirmed by western blot analysis and enzyme?linked immunosorbent assay (ELISA). The present study also confirmed that the expression level of APOC3 was positively associated with the homeostasis model assessment of insulin resistance (HOMA?IR). On the whole, the data indicate that APOC3 may be a potential diagnostic marker for PCOS?IR patients.

SUBMITTER: Li L

PROVIDER: S-EPMC7138261 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Proteomics analysis of potential serum biomarkers for insulin resistance in patients with polycystic ovary syndrome.

Li Li L Zhang Jing J Zeng Jing J Liao Biling B Peng Xiuhong X Li Tiantian T Li Jieming J Tan Qiuxiao Q Li Xiaofang X Yang Ying Y Chen Zhijing Z Liang Zhijiang Z

International journal of molecular medicine 20200303 5

The aim of the present study was to identify potential serum biomarkers for insulin resistance (IR) in patients with polycystic ovary syndrome (PCOS) by comparing the differences in serum protein expression levels between PCOS patients with and without IR. PCOS patients aged from 18 to 35 years were recruited at Guangdong Women and Children's Hospital from January, 2013 to February, 2014. A total of 218 PCOS patients were enrolled and divided into the insulin resistance (PCOS‑IR) and non‑insulin ...[more]

PMID: 32323743

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Project description:BackgroundPolycystic ovary syndrome (PCOS) is the most common reproductive endocrine disease in women of childbearing age, and insulin resistance is an important etiological mechanism in PCOS. This study revealed the microRNA (miRNA) expression profile of PCOS with insulin resistance and explored the potential biological functions of differentially expressed miRNA.MethodsA total of 76 patients with PCOS and 30 normal healthy women were recruited in the gynecological clinic of the Second Hospital of Tianjin Medical University. We divided the patients with PCOS into a group with insulin resistance (n=46) and a group without insulin resistance (n=30). Peripheral venous serum samples from each group were used for deep sequencing to identify differentially expressed miRNAs. Hierarchical clustering heat maps were used to show differences in miRNA expression. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and target gene network databases were used to explore the potential target genes of differentially expressed miRNAs and to analyze their specific biological functions.ResultsA case-control analysis found that the levels of body mass index (BMI), prolactin (PRL), total testosterone (T), fasting blood glucose (FBG), and fasting insulin (INS) in patients with PCOS were higher than those in healthy controls. High BMI, high blood sugar, and hyperinsulinemia were more significant in the PCOS with insulin resistance group than without insulin resistance group. Among the patients with PCOS, miR-122-5p was found to have more significant differences in the PCOS with insulin resistance group. GO and KEGG pathway analysis showed that the identified miRNAs were involved in the regulation of different biological processes, such as signal transduction, negative regulation of GTPase activity, chloride channel complex. The predicted target genes were related to the citrate cycle (TCA cycle) and the biosynthesis of mucin-type O-glycans.ConclusionsOur research demonstrated the use of miRNAs as new biomarkers for the diagnosis, treatment and presented a new strategy to lessen the symptoms of PCOS with insulin resistance.

Project description:Objectives: Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder with heterogeneous manifestations and complex etiology. We used quantitative proteomics analysis based on mass spectrometry to identify the differences in proteomics profiles for follicular fluid obtained from patients with or without PCOS and explore possible mechanisms underlying PCOS. Methods: Follicular fluid samples were collected from infertile patients with (n = 9) or without (n = 9) PCOS. Total protein was extracted, quantitatively labeled with a tandem mass tag (TMT), and analyzed using liquid chromatography-mass spectrometry (LC-MS). TMT-based proteomics and bioinformatics analysis were used to determine the differentially expressed proteins (DEPs) and understand the protein networks. The analysis included protein annotation, unsupervised hierarchical clustering, functional classification, functional enrichment and clustering, and protein-protein interaction analysis. Selected DEPs were confirmed by ELISA, and correlation analysis was performed between these DEPs and the clinical characteristics. Results: In this study, we have identified 1,216 proteins, including 70 DEPs (32 upregulated proteins, 38 downregulated proteins). Bioinformatics analysis revealed that the inflammatory response, complement and coagulation cascades, activation of the immune response, lipid transport, and regulation of protein metabolic processes were co-enriched in patients with PCOS. Based on ELISA results, insulin-like growth factor binding protein 1 (IGFBP1) and apolipoprotein C2 (APOC2) were differentially expressed between patients with and without PCOS. Follicular IGFBP1 showed a positive correlation with the serum levels of high-density lipoprotein cholesterol (HDL-C) (r = 0.3046, p = 0.0419), but negatively correlated with the serum levels of anti-Müllerian hormone (AMH) (r = -0.2924, p = 0.0354) and triglycerides (r = -0.3177, p = 0.0246). Follicular APOC2 was negatively correlated with the serum apolipoprotein A1 (APOA1) levels (r = 0.4509, p = 0.0002). Conclusion: Our study identified DEPs in the follicular fluid of patients with PCOS. Inflammatory response, complement and coagulation cascades, activation of the immune response, lipid transport, and regulation of protein metabolic process were deregulated in PCOS, which may play essential roles in the pathogenesis of PCOS.

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Proteomics analysis of potential serum biomarkers for insulin resistance in patients with polycystic ovary syndrome.

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Proteomics analysis of potential serum biomarkers for insulin resistance in patients with polycystic ovary syndrome.

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