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

Autor: Xin Yang, Wang Xiaoping, Ding Nan, Zhang Jian, Li Xiaofeng, Yuan Liwei, Mengni Zhao, Fang Wang
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Gynecological Endocrinology, Vol 39, Iss 1 (2023)
Druh dokumentu: article
ISSN: 09513590
1473-0766
0951-3590
DOI: 10.1080/09513590.2023.2173948
Popis: 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
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