Metabolism-related proteins as biomarkers for predicting prognosis in polycystic ovary syndrome.

IF 2.1 3区生物学 Q3 BIOCHEMICAL RESEARCH METHODS

Proteome Science Pub Date : 2024-12-19 DOI:10.1186/s12953-024-00238-9

Nan Ding, Ruifang Wang, Peili Wang, Fang Wang

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引用次数: 0

Abstract

Objective: The study aimed to explore the role of metabolism-related proteins and their correlation with clinical data in predicting the prognosis of polycystic ovary syndrome (PCOS).

Methods: This research involves a secondary analysis of proteomic data derived from endometrial samples collected from our study group, which includes 33 PCOS patients and 7 control subjects. A comprehensive identification and analysis of 4425 proteins were conducted to screened differentially expressed proteins (DEPs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were subsequently performed on the DEPs. To identify independent prognostic metabolism-related proteins, univariate Cox regression and LASSO regression were applied. The expression levels of these proteins were then used to develop a prognostic model, with their predictive accuracy evaluated through receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. Furthermore, we also investigate the correlation between clinical data and prognostic proteins.

Results: The study identified 285 DEPs between the PCOS and control groups. GO enrichment analysis revealed significant involvement in metabolic processes, while KEGG pathway analysis highlighted pathways such as glycolysis/gluconeogenesis and glucagon signaling. Ten key metabolism-related proteins (ACSL5, ANPEP, CYB5R3, ENOPH1, GLS, GLUD1, LDHB, PLCD1, PYCR2, and PYCR3) were identified as significant predictors of PCOS prognosis. Patients were separated into high and low-risk groups according to the risk score. The ROC curves for predicting outcomes at 6, 28, and 37 weeks demonstrated excellent predictive performance, with AUC values of 0.98, 1.0, and 1.0, respectively. The nomogram constructed from these proteins provided a reliable tool for predicting pregnancy outcomes. DCA indicated a net benefit of the model across various risk thresholds, and the calibration curve confirmed the model's accuracy. Additionally, we also found BMI exhibited a significant negative correlation with the expression of GLS (r =-0.44, p = 0.01) and CHO showed a significant positive correlation with the expression of LDHB (r = 0.35, p = 0.04).

Conclusion: The identified metabolism-related proteins provide valuable insights into the prognosis of PCOS. The protein based prognostic model offers a robust and reliable tool for risk stratification and personalized management of PCOS patients.

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代谢相关蛋白作为多囊卵巢综合征预后预测的生物标志物

目的：探讨代谢相关蛋白在多囊卵巢综合征（PCOS）预后预测中的作用及其与临床资料的相关性。方法：本研究涉及对我们研究组收集的子宫内膜样本的蛋白质组学数据进行二次分析，其中包括33名PCOS患者和7名对照受试者。对4425个蛋白进行综合鉴定和分析，筛选差异表达蛋白（DEPs）。随后对dep进行基因本体（GO）和京都基因与基因组百科全书（KEGG）富集分析。为了确定独立的预后代谢相关蛋白，应用单变量Cox回归和LASSO回归。然后利用这些蛋白的表达水平建立预后模型，并通过受试者工作特征（ROC）曲线、决策曲线分析（DCA）和校准曲线评估其预测准确性。此外，我们还研究了临床数据与预后蛋白之间的相关性。结果：在PCOS组和对照组之间共鉴定出285例dep。氧化石墨烯富集分析揭示了代谢过程的重要参与，而KEGG途径分析强调了糖酵解/糖异生和胰高血糖素信号传导等途径。十个关键代谢相关蛋白（ACSL5、ANPEP、CYB5R3、ENOPH1、GLS、GLUD1、LDHB、PLCD1、PYCR2和PYCR3）被确定为PCOS预后的重要预测因子。根据风险评分将患者分为高危组和低危组。预测6周、28周和37周预后的ROC曲线表现出良好的预测性能，AUC值分别为0.98、1.0和1.0。由这些蛋白构建的图为预测妊娠结局提供了可靠的工具。DCA表明模型在各种风险阈值上的净收益，校准曲线证实了模型的准确性。此外，我们还发现BMI与GLS表达呈显著负相关（r =-0.44, p = 0.01）， CHO与LDHB表达呈显著正相关（r = 0.35, p = 0.04）。结论：所鉴定的代谢相关蛋白为PCOS的预后提供了有价值的见解。基于蛋白质的预后模型为PCOS患者的风险分层和个性化管理提供了一个强大而可靠的工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Proteome Science 生物-生化研究方法

CiteScore

2.90

自引率

0.00%

发文量

审稿时长

4.5 months

期刊介绍： Proteome Science is an open access journal publishing research in the area of systems studies. Proteome Science considers manuscripts based on all aspects of functional and structural proteomics, genomics, metabolomics, systems analysis and metabiome analysis. It encourages the submissions of studies that use large-scale or systems analysis of biomolecules in a cellular, organismal and/or environmental context. Studies that describe novel biological or clinical insights as well as methods-focused studies that describe novel methods for the large-scale study of any and all biomolecules in cells and tissues, such as mass spectrometry, protein and nucleic acid microarrays, genomics, next-generation sequencing and computational algorithms and methods are all within the scope of Proteome Science, as are electron topography, structural methods, proteogenomics, chemical proteomics, stem cell proteomics, organelle proteomics, plant and microbial proteomics. In spite of its name, Proteome Science considers all aspects of large-scale and systems studies because ultimately any mechanism that results in genomic and metabolomic changes will affect or be affected by the proteome. To reflect this intrinsic relationship of biological systems, Proteome Science will consider all such articles.