Olink Proteomics for the Identification of Biomarkers for Early Diagnosis of Postmenopausal Osteoporosis

This investigation aims to employ Olink proteomics in analyzing the distinct serum proteins associated with postmenopausal osteoporosis (PMOP) and identifying prognostic markers for early detection of PMOP via molecular mechanism research on postmenopausal osteoporosis. Postmenopausal women admitted to Beijing Jishuitan Hospital were randomly selected and categorized into three groups based on their dual-energy X-ray absorptiometry (DXA) T-scores: osteoporosis group (<i>n =</i> 24), osteopenia group (<i>n =</i> 20), and normal bone mass group (<i>n =</i> 16). Serum samples from all participants were collected for clinical and bone metabolism marker measurements. Olink proteomics was utilized to identify differentially expressed proteins (DEPs) that are highly associated with postmenopausal osteoporosis. The functional analysis of DEPs was performed using Gene Ontology and Kyto Encyclopedia Genes and Genomes (KEGG). The biological characteristics of these proteins and their correlation with PMOP were subsequently analyzed. ROC curve analysis was performed to identify potential biomarkers with the highest diagnostic accuracy for early stage PMOP. Through Olink proteomics, we identified five DEPs highly associated with PMOP, including two upregulated and three downregulated proteins. TWEAK and CDCP1 markers exhibited the highest area under the curve (0.8188 and 0.8031, respectively). TWEAK and CDCP1 have the potential to serve as biomarkers for early prediction of postmenopausal osteoporosis.

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Publication Details

Journal
Journal of Proteome Research
Published
2024-09-03
DOI
https://doi.org/10.1021/acs.jproteome.4c00470
Citations
4
Primary Topic
Bone Metabolism and Diseases
Type
article
Field-Weighted Citation Impact
0.94

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article

Olink Proteomics for the Identification of Biomarkers for Early Diagnosis of Postmenopausal Osteoporosis

Jun Wu, Xinwei Zang, Xiang Cheng, Xiangyu Meng et al.
4 citations
Journal of Proteome Research
Bone Metabolism and Diseases
0.94
article

Olink Proteomics for the Identification of Biomarkers for Early Diagnosis of Postmenopausal Osteoporosis

Jun Wu, Xinwei Zang, Xiang Cheng, Xiangyu Meng, Liyuan Chen, Shuai Lu, Chunyan Li, Shangqi Yin, Wei Zhang, Heng Liu
article en
4 citations

Abstract

This investigation aims to employ Olink proteomics in analyzing the distinct serum proteins associated with postmenopausal osteoporosis (PMOP) and identifying prognostic markers for early detection of PMOP via molecular mechanism research on postmenopausal osteoporosis. Postmenopausal women admitted to Beijing Jishuitan Hospital were randomly selected and categorized into three groups based on their dual-energy X-ray absorptiometry (DXA) T-scores: osteoporosis group (<i>n =</i> 24), osteopenia group (<i>n =</i> 20), and normal bone mass group (<i>n =</i> 16). Serum samples from all participants were collected for clinical and bone metabolism marker measurements. Olink proteomics was utilized to identify differentially expressed proteins (DEPs) that are highly associated with postmenopausal osteoporosis. The functional analysis of DEPs was performed using Gene Ontology and Kyto Encyclopedia Genes and Genomes (KEGG). The biological characteristics of these proteins and their correlation with PMOP were subsequently analyzed. ROC curve analysis was performed to identify potential biomarkers with the highest diagnostic accuracy for early stage PMOP. Through Olink proteomics, we identified five DEPs highly associated with PMOP, including two upregulated and three downregulated proteins. TWEAK and CDCP1 markers exhibited the highest area under the curve (0.8188 and 0.8031, respectively). TWEAK and CDCP1 have the potential to serve as biomarkers for early prediction of postmenopausal osteoporosis.

Journal of Proteome ResearchVol. 23(10)
Capital Medical University (CN), Peking University (CN), Beijing Jishuitan Hospital (CN), Beijing Geriatric Hospital (CN), First Hospital of Shijiazhuang (CN), National Clinical Research (US)
Capital Medical University
Good health and well-being
Openalex Percentile: Top 25%
Bone Metabolism and Diseases
0.94
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