Association between plasma proteins and osteoporosis and identification of potential therapeutic targets: information analysis based on the UK Biobank database

doi:10.12307/2025.690

Abstract

Abstract: BACKGROUND: Osteoporosis is a significant contributor to the global burden of disease and disability. Plasma proteins are involved in complex biological processes and play a crucial role in uncovering disease mechanisms and identifying potential therapeutic targets. Although existing studies have suggested an association between plasma proteins and osteoporosis, the causal nature of these associations is not fully clarified. Therefore, it is imperative to identify the causal proteins associated with osteoporosis and potential therapeutic targets for the amelioration and management of this condition using large-scale plasma protein data.
OBJECTIVE: To evaluate the causal relationship between plasma proteins and osteoporosis based on the UK Biobank database as source information using the two-sample Mendelian randomization.
METHODS: A total of 1 001 plasma protein-related genome-wide significant quantitative trait loci (P < 5×10-8) were obtained from the UK Biobank database and used as instrumental variables, with linkage disequilibrium excluded. Summary data on osteoporosis were collected from the FinnGen database, which included 438 872 individuals of European descent. The study was analyzed using inverse variance weighting, MR-Egger regression, weighted median, and several sensitivity analyses to ensure the robustness of the results. Further, a protein-protein interaction network was constructed, and Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were conducted to explore the functional relevance and potential mechanisms of plasma proteins.
RESULTS AND CONCLUSION: (1) The Mendelian randomization analysis using the inverse variance weighted method identified 50 plasma proteins that have causal associations with osteoporosis (P < 0.05). Among them, 20 plasma proteins, including chromosome 19 open reading frame 12 (odds ratio [OR]=0.610; 95% confidence interval [CI]: 0.483-0.769, P=2.967×10-5) and epidermal growth factor (EGF; OR=0.877; 95% CI: 0.770-0.999, P=0.049), might be associated with a reduced risk of osteoporosis. In contrast, 30 plasma proteins, such as C-C motif chemokine ligand (CCL) 18 (OR=1.091; 95% CI: 1.037-1.147, P=0.001) and CD209 (OR=1.036; 95% CI: 1.003-1.070, P=0.034), might be associated with an increased risk of osteoporosis. After Bonferroni correction, only chromosome 19 open reading frame 12 showed a significant causal association with osteoporosis. (2) Multiple sensitivity analyses revealed no evidence of pleiotropy or heterogeneity, indicating the robustness of the results. (3) The construction of the PPI network identified core proteins such as EGF, CCL5, C-X-C motif chemokine ligand (CXCL) 13, CXCL5, vascular endothelial growth factor C, CCL17, CCL18, TEK receptor tyrosine kinase, tyrosine kinase with immunoglobulin like and EGF like domains 1, and CCL23. (4) Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis suggested that these plasma proteins play essential roles in the immune system, influencing osteoporosis through processes such as signal transduction, cell migration, and chemotaxis. (5) This study reveals the potential causal associations between 1 001 plasma proteins and osteoporosis, highlighting the utility of a large-scale, data-driven approach to identify new biomarkers and drug targets in diverse populations. Additionally, our findings suggest that processes such as immune signaling, cell migration, and chemotaxis play significant roles in the pathogenesis of osteoporosis, offering new directions for research under specific genetic backgrounds and environmental factors. Finally, the core proteins identified in this study (e.g., EGF, CCL5, and CXCL13) may serve as novel biomarkers or therapeutic targets, providing a new basis for the precise prevention and treatment of osteoporosis.

中国组织工程研究杂志出版内容重点：组织构建；骨细胞；软骨细胞；细胞培养；成纤维细胞；血管内皮细胞；骨质疏松；组织工程

Key words: plasma protein, protein quantitative trait loci, osteoporosis, Mendelian randomization, causal relationship, enrichment analysis, immune, chemotaxis, biomarker, drug target

CLC Number:

Zhu Kai, Liu Wanxin, Luo Haobing, Feng Shengyi, Wang Qiugen. Association between plasma proteins and osteoporosis and identification of potential therapeutic targets: information analysis based on the UK Biobank database[J]. Chinese Journal of Tissue Engineering Research, 2025, 29(18): 3948-3960.

Figures/Tables 7

2.1 孟德尔随机化分析结果在此文章中，基于工具变量的筛选标准，血浆蛋白共获得了2 279个pQTLs，所纳入单个单核苷酸多态性对应的F统计量均大于10，故研究避免了弱工具偏倚的影响。文章采用孟德尔随机化分析方法，探讨了1 001种血浆蛋白与骨质疏松症之间的关系。结果表明，有50种血浆蛋白与骨质疏松症存在因果关系(P < 0.05)。其中，OR < 1的血浆蛋白共有20种，包括淀粉样前体蛋白(amyloid precursor protein，APP)、染色体19开放阅读框12(chromosome 19 open reading frame 12，C19orf12)、CC基序趋化因子配体14(C-C motif chemokine ligand 14，CCL14)、CCL17、CCL5、细胞通讯网络因子3(cellular communication network factor 3，CCN3)、组织蛋白酶F(cathepsin F，CTSF)、CXC基序趋化因子配体13(C-X-C Motif chemokine ligand 13，CXCL13)、CXC基序趋化因子配体5(C-X-C motif chemokine ligand 5，CXCL5)、表皮生长因子(epidermal growth factOR，EGF)、上皮抑素(epiregulin，EREG)、脂肪酸结合蛋白9 (fatty acid bindingprotein 9，FABP9)、叶抑素(follistatin，FST)、脂多糖结合蛋白 (lipopolysaccharide binding protein，LBP)、巢蛋白1(nidogen 1，NID1)、转化生长因子β受体3(transforming growth factor beta receptor 3，TGFBR3)、Thimet寡肽酶1(thimet oligopeptidase 1，THOP1)、肿瘤坏死因子超家族成员11(tumor necrosis factor superfamily member 11，TNFSF11)、髓系细胞样触发受体2(triggering receptor expressed on myeloid cells like 2，TREML2)及血管内皮生长因子C(vascular endothelial growth factor C，VEGFC)，可能与骨质疏松症的风险降低相关；OR > 1的血浆蛋白共30种，包括BTB结构域和CNC同源物1(BTB domain and CNC homolog 1，BACH1)、基底细胞黏附分子(basal cell adhesion molecule，BCAM)、短链蛋白聚糖(brevican，BCAN)、钙激活核苷酸酶1(calcium activated nucleotidase 1，CANT1)、CCL18、CCL23、CD209、CD300分子样家族成员F(CD300 molecule like family member F，CD300LF)、羧肽酶A2(carboxypeptidase A2，CPA2)、钙释放激活通道调节蛋白2A(calcium release activated channel regulator 2A，CRACR2A)、组织蛋白酶V(cathepsin V，CTSV)、动力蛋白复合物6亚基(dynactin subunit 6，DCTN6)、FK506结合蛋白5(FK506 binding protein 5，FKBP5)、多肽N-乙酰半乳糖胺转移酶10(polypeptide N-Acetylgalactosaminy ltransferase 10，GALNT10)、HtrA丝氨酸肽酶2(htrA serine peptidase 2，HTRA2)、透明质酸酶1(hyaluronidase 1，HYAL1)、肌醇单磷酸酶1(inositol monophosphatase 1，IMPA1)、IRRE类似家族2(kin of irre like 2，KIRREL2)、半乳糖凝集素7(galectin 7，LGALS7)、富含亮氨酸的重复序列25(leucine rich repeat containing 25，LRRC25)、微纤维相关蛋白5(microfibril associated protein 5，MFAP5)、NAD激酶(NAD kinase，NADK)、烟酰胺核苷酸腺苷酸转移酶1(nicotinamide nucleotide adenylyltransferase 1，NMNAT1)、蛋白酪氨酸磷酸酶非受体型1(protein tyrosine phosphatase nonreceptor Type 1，PTPN1)、抵抗素(resistin，RETN)、突触融合蛋白8(syntaxin 8，STX8)、TEK受体酪氨酸激酶(tek receptor tyrosine kinase，TEK)、含免疫球蛋白样和EGF样结构域的酪氨酸激酶1 (tyrosine kinase with immunoglobulin like and EGF like domains 1，TIE1)、泛素特异性肽酶8(ubiquitin specific peptidase 8，USP8)及含von Willebrand因子C结构域2(Von willebrand factor C domain containing 2，VWC2)，这可能与骨质疏松症的风险增加有关。经Bonferroni校正后，只有C19orf12 (P=2.967×10-5)与骨质疏松症存在显著因果关系，详见表1及图2。 2.2 敏感性分析结果敏感性分析的相关结果详见表1。在多效性检验中，50种血浆蛋白的P值均大于0.05，同时MR-PRESSO也进一步表明文章结果不存在水平多效性。此外，经Cochrane’s Q检验，未发现异质性。留一法进一步提示分析结果较为稳健，见图3，4。漏斗图中呈现的因果效应分布基本具有对称性，未见明显偏倚，见图5，6。 2.3 网络分析及功能富集结果文章将孟德尔随机化分析获取的50个蛋白上传到String数据库进行分析，并将String结果导入Cytoscape 3.9.0，去除孤立节点后构建了PPI网络，该网络由34个节点和49条边组成，平均节点度为1.96，其中节点度值位于前10的核心蛋白为EGF、CCL5、CXCL13、CXCL5、VEGFC、CCL18、CCL17、TEK、TIE1和CCL23，详见图7。GO分析如图8所示，在生物过程(biological process，BP)方面，主要富集于白细胞迁移(leukocyte migration)、细胞趋化(cell chemotaxis)过程；细胞组分(cellular component，CC)方面主要定位在囊泡腔(vesicle lumen)、分泌颗粒腔(secretory granule lumen)等；分子功能(molecular function，MF)方面主要涉及信号受体激活因子活性(signaling receptor activator activity)、细胞因子受体结合(cytokine receptor binding)、趋化因子受体结合(chemokine receptor binding)等。KEGG分析结果显示，显著富集的通路包括细胞因子-细胞因子受体相互作用(cytokine-cytokine receptor interaction)、趋化因子信号通路(chemokine signaling pathway)、肿瘤坏死因子信号通路(TNF signaling pathway)、溶酶体(Lysosome)和细胞凋亡(Apoptosis)，详见图9。这些通路强调了蛋白靶标在细胞因子介导的信号传导和免疫系统调节中的核心作用。"

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