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

Abstract

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.

Keywords

• Polycystic ovary syndrome
• insulin resistance
• endometrium
• pregnancy
• proteomics