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2025, Vol. 29 ›› Issue (8): 1741-1750

Bioinformatics analysis of potential biomarkers for primary osteoporosis

Zhao Jiacheng1, 2, Ren Shiqi3, Zhu Qin3, Liu Jiajia1, Zhu Xiang1, Yang Yang1, 2   

  1. 1Department of Trauma Center, 3Department of Hand Surgery, Affiliated Hospital of Nantong University, Nantong 226001, Jiangsu Province, China; 2Medical School of Nantong University, Nantong 226001, Jiangsu Province, China

  • Received:2024-03-09 Accepted:2024-04-09 Online:2025-03-18 Published:2024-07-06

  • Contact: Yang Yang, Master, Attending physician, Department of Trauma Center, Affiliated Hospital of Nantong University, Nantong 226001, Jiangsu Province, China; Medical School of Nantong University, Nantong 226001, Jiangsu Province, China

  • About author:Zhao Jiacheng, Master candidate, Physician, Department of Trauma Center, Affiliated Hospital of Nantong University, Nantong 226001, Jiangsu Province, China; Medical School of Nantong University, Nantong 226001, Jiangsu Province, China

  • Supported by:

    Jiangsu Research Hospital Project, No. YJXYY202204-YSB20 (to YY); Nantong Municipal Health Commission Science and Technology Project, No. MS2023016 (to YY); Jiangsu Postgraduate Research and Innovation Project, No. KYCX23-3433 (to ZJC)


Abstract: BACKGROUND: Primary osteoporosis has a high incidence, but the pathogenesis is not fully understood. Currently, there is a lack of effective early screening indicators and treatment programs.
OBJECTIVE: To further explore the mechanism of primary osteoporosis through comprehensive bioinformatics analysis.

METHODS: The primary osteoporosis data were obtained from the gene expression omnibus (GEO) database, and the differentially expressed genes were screened for Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. In addition, the differentially expressed genes were subjected to protein-protein interaction network to determine the core genes related to primary osteoporosis, and the least absolute shrinkage and selection operator algorithm was used to identify and verify the primary osteoporosis-related biomarkers. Immune cell correlation analysis, gene enrichment analysis and drug target network analysis were performed. Finally, the biomarkers were validated using qPCR assay.

RESULTS AND CONCLUSION: A total of 126 differentially expressed genes and 5 biomarkers including prostaglandins, epidermal growth factor receptor, mitogen-activated protein kinase 3, transforming growth factor B1, and retinoblastoma gene 1 were obtained in this study. GO analysis showed that differentially expressed genes were mainly concentrated in the cellular response to oxidative stress and the regulation of autophagy. KEGG analysis showed that autophagy and senescence pathways were mainly involved. Immunoassay of biomarkers showed that prostaglandins, retinoblastoma gene 1, and mitogen-activated protein kinase 3 were closely related to immune cells. Gene enrichment analysis showed that biomarkers were associated with immune-related pathways. Drug target network analysis showed that the five biomarkers were associated with primary osteoporosis drugs. The results of qPCR showed that the expression of prostaglandins, epidermal growth factor receptor, mitogen-activated protein kinase 3, and transforming growth factor B1 in the primary osteoporosis sample was significantly increased compared with the control sample (P < 0.001), while the expression of retinoblastoma gene 1 in the primary osteoporosis sample was significantly decreased compared with the control sample (P < 0.001). Overall, the study screened and validated five potential biomarkers of primary osteoporosis, providing a reference basis for further in-depth investigation of the pathogenesis, early screening and diagnosis, and targeted treatment of primary osteoporosis.
Key words: primary osteoporosis, biomarker, bioinformatics, drug target network, protein-protein interaction network


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