The aim of this study is to identify differentially expressed proteins (DEPs) in granulose cells (GCs) from women with or withoutpolycystic ovary syndrome (PCOS) via data independent acquisition (DIA) proteomic analysis.A total of 63 women were recruited for this study, 34 PCOS patients as experimental group (P), and 29 women without PCOS as Normal group (NP). DIA-based proteomic analysis was performed to identify DEPs in GCs between the P and NP samples. Certain typical DEPs were further validated by Parallel reaction monitoring (PRM), and correlation analysis was performed between these DEPs and the clinical characteristics.Cell vitality was measured by CCK-8 assay. DIA analysis revealed 174 significantly DEPs, of which 7 were upregulated and 167 downregulated. Bioinformatics analysis was performed to analysis the significantly DEPs. The PRM experiment confirmed TOP2A and SPHKAP were upregulated significantly in P by comparing to NP, while GM2A, MRPS16, APOA2 and FGF2 were downregulated significantly. Most notably, Correlation analysis revealed that TOP2A, SPHKAP, MRPS16 and FGF2were positively correlated with TG, AMH and Age, but negatively correlated with Menarche age, DBIL, FT3, Basal serum FSH and LH.Meanwhile, CCK-8 assay has shown that downregulation of FGF2 could weaken cell viability. Finally, a panel of DEPs were identified in the GCs of patients with PCOS, of which certain significant DEPs might play essential roles in the pathogenesis of PCOS, could be regarded as candidate biomarkers for PCOS. 

DIA proteomic and PRM validation through human granulose cells profiles screen suitable biomarkers for polycystic ovary syndrome patients

Objective: The aim of this study was to investigate the endometrial proteomic profiles of patients with polycystic ovary syndrome (PCOS) with and without insulin resistance (IR). Method of Study: We collected 40 endometrial samples, including PCOS-IR (n = 21), PCOS-non-IR (n = 12), and control (n = 7). Data-independent acquisition (DIA)-based proteomics method is used to identify the expressed proteins among the three groups. The correlation between pregnancy outcomes and identified proteins was analyzed by Lasso regression. Results: A total of 5331 proteins were identified, while 275 proteins were differentially expressed in the PCOS vs. control group and 215 proteins were differentially expressed in the PCOS-IR vs. PCOS-non-IR group. Platelet degranulation, neutrophil degranulation, and very long-chain fatty acid catabolic processes have been found to play important roles in the endometrium of patients with PCOS-IR. Lasso regression analysis found that ACTR1A, TSC22D2, CKB, ABRAXAS2, and TAGLN2 were associated with miscarriage in patients with PCOS. ACTR1A and CKB were higher in the PCOS-IR group and were positively correlated with HOMA-IR (p < .05). Conclusion: In this study, a panel of proteins was found to be differently expressed in the endometrium. ACTR1A and CKB may be considered as PCOS-IR candidate biomarkers.

Proteomic and bioinformatic analysis of human endometrium from polycystic ovarian syndrome with and without insulin resistance.

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Proteomic and bioinformatic analysis of human endometrium from polycystic ovarian syndrome with and without insulin resistance.