Gansu University of Chinese Medicine, Lanzhou, 730030, China.
Department of Reproductive Medicine, Lanzhou University Second Hospital Lanzhou, Lanzhou, 730030, China.
BMC Pregnancy Childbirth. 2024 Feb 21;24(1):152. doi: 10.1186/s12884-024-06273-w.
We aimed to investigate the significance of autophagy proteins and their association with clinical data on pregnancy loss in polycystic ovary syndrome (PCOS), while also constructing predictive models.
This study was a secondary analysis. we collected endometrial samples from 33 patients with polycystic ovary syndrome (PCOS) and 7 patients with successful pregnancy control women at the Reproductive Center of the Second Hospital of Lanzhou University between September 2019 and September 2020. Liquid chromatography tandem mass spectrometry was employed to identify expressed proteins in the endometrium of 40 patients. R was use to identify differential expression proteins(DEPs). Subsequently, Metascape was utilized for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Multivariate Cox analysis was performed to analyze autophagy proteins associated with reproductive outcomes, while logistic regression was used for analyzing clinical data. Linear correlation analysis was conducted to examine the relationship between autophagy proteins and clinical data. We established prognostic models and constructed the nomograms based on proteome data and clinical data respectively. The performance of the prognostic model was evaluated by the receiver operating characteristic curve (ROC) and decision curve analysis (DCA).
A total of 5331 proteins were identified, with 450 proteins exhibiting significant differential expression between the PCOS and control groups. A prognostic model for autophagy protein was developed based on three autophagy proteins (ARSA, ITGB1, and GABARAPL2). Additionally, another prognostic model for clinical data was established using insulin, TSH, TPOAB, and VD3. Our findings revealed a significant positive correlation between insulin and ARSA (R = 0.49), as well as ITGB1 (R = 0.3). Conversely, TSH exhibited a negative correlation with both ARSA (-0.33) and ITGB1 (R = -0.26).
Our research could effectively predict the occurrence of pregnancy loss in PCOS patients and provide a basis for subsequent research.
本研究旨在探讨自噬蛋白在多囊卵巢综合征（PCOS）妊娠丢失患者中的意义及其与临床数据的相关性，并构建预测模型。
本研究为二次分析。我们收集了 2019 年 9 月至 2020 年 9 月兰州大学第二医院生殖中心 33 例 PCOS 患者和 7 例成功妊娠对照组患者的子宫内膜样本。采用液相色谱串联质谱法鉴定 40 例患者子宫内膜中的表达蛋白。R 软件用于识别差异表达蛋白（DEPs）。随后，利用 Metascape 进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析。多变量 Cox 分析用于分析与生殖结局相关的自噬蛋白，逻辑回归用于分析临床数据。线性相关分析用于检验自噬蛋白与临床数据的关系。我们分别基于蛋白质组数据和临床数据建立了预后模型，并构建了列线图。通过接受者操作特征曲线（ROC）和决策曲线分析（DCA）评估预后模型的性能。
共鉴定出 5331 种蛋白质，其中 450 种蛋白质在 PCOS 组和对照组之间表现出显著差异表达。基于 3 种自噬蛋白（ARSA、ITGB1 和 GABARAPL2）建立了自噬蛋白预后模型。此外，还利用胰岛素、TSH、TPOAB 和 VD3 建立了临床数据的预后模型。我们的研究结果表明，胰岛素与 ARSA（R=0.49）和 ITGB1（R=0.3）呈显著正相关，而 TSH 与 ARSA（-0.33）和 ITGB1（R=-0.26）呈显著负相关。
本研究能够有效预测 PCOS 患者妊娠丢失的发生，并为后续研究提供依据。
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