Background:Serum uric acid (UA) is positively correlated with bone mineral density (BMD). However, the mechanism by which serum UA affects BMD remains unclear.Objective:The aim was carried out to search for the functional proteins related to serum UA and femoral neck BMD to better understand the pathophysiological mechanism of osteoporosis.Materials and Methods:In this study, patients in the UA group (hyperuricaemia combined with femoral neck fracture) and the control group (normal uricaemia combined with femoral neck fracture) were selected according to the inclusion criteria. Total protein was extracted from the femoral neck of each patient. Fluorescence differential gel electrophoresis was used to separate the total proteins, and the differentially expressed protein spots were detected by image analysis. After enzyme digestion, peptide mass fingerprinting and database searches were performed to identify the differentially expressed proteins. DAVID software and Kyoto Encyclopedia of Genes and Genomes (KEGG) data were used for enrichment analysis of the screened differential proteins.Results:After mass spectrometry and database searching, 66 differentially expressed protein spots were identified between the UA group and the control group. Most differentially expressed proteins functioned in cytoskeleton formation, energy metabolism, or signal transduction. They were mainly involved in 50 biological processes, including peroxisome proliferator-activated receptor (PPAR) signalling and fatty acid metabolism. PPARγ and PLIN1 were subject to Western blotting analysis detection; results were consistent with the Label-Free result.Conclusion:Based on an analysis of the biological information, these proteins may be associated with the incidence and progression of the femoral neck bone tissues of hyperuricaemia patients.

中文翻译：

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老年女性高尿酸血症患者股骨颈差异蛋白质组学鉴定及生物信息学分析

背景：血清尿酸（UA）与骨密度（BMD）呈正相关。然而，血清UA影响BMD的机制尚不清楚。目的：旨在寻找与血清UA和股骨颈BMD相关的功能蛋白，以更好地了解骨质疏松症的病理生理机制。材料与方法：本研究根据纳入标准选择UA组（高尿酸血症合并股骨颈骨折）和对照组（正常尿酸血症合并股骨颈骨折）患者。从每位患者的股骨颈提取总蛋白。采用荧光差异凝胶电泳分离总蛋白，通过图像分析检测差异表达的蛋白点。酶消化后，进行肽质量指纹图谱和数据库搜索以鉴定差异表达的蛋白质。采用DAVID软件和京都基因与基因组百科全书(KEGG)数据对筛选出的差异蛋白进行富集分析。结果：经质谱和数据库检索，UA组与对照组共鉴定出66个差异表达蛋白点。大多数差异表达的蛋白质在细胞骨架形成、能量代谢或信号转导中起作用。它们主要参与 50 个生物过程，包括过氧化物酶体增殖物激活受体 (PPAR) 信号传导和脂肪酸代谢。PPARγ和PLIN1进行Western blotting分析检测；结果与无标签结果一致。结论：