AccScience Publishing / EJMO / Online First / DOI: 10.36922/EJMO025240252
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ORIGINAL RESEARCH ARTICLE

Identification and validation of relevant diagnostic biomarkers for osteoporosis by Weighted Gene Co-expression Network Analysis and machine learning

Cuicui Zhou1,2 ,  Zarina Awang1 ,  Farra Aidah Jumuddin1*
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1 Department of Clinical Medicine, Faculty of Medicine, Lincoln University College, Petaling Jaya, Selangor, Malaysia
2 Department of Orthopaedic Surgery, The Second Affiliated Hospital of Nanyang Medical College, Nanyang, Henan, China
EJMO, 025240252 https://doi.org/10.36922/EJMO025240252
Received: 9 June 2025 | Revised: 25 July 2025 | Accepted: 29 July 2025 | Published online: 9 September 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Introduction: Osteoporosis (OP) is a systemic metabolic bone disease characterized by complex pathogenesis and high prevalence. Current diagnostic and therapeutic approaches have limited effectiveness, and new biomarkers are needed to improve the treatment and diagnosis of OP.

Objective: The present study aimed to identify novel diagnostic biomarkers for OP through integrated bioinformatics analysis.

Methods: We performed an integrative bioinformatics analysis combining Weighted Gene Co-expression Network Analysis and machine learning on two Gene Expression Omnibus datasets (GSE35958, GSE35956). Differentially expressed genes (DEGs) were identified using “limma” package of R software, followed by module construction and key gene screening via Least Absolute Shrinkage and Selection Operator (LASSO) regression. Functional enrichment, immune infiltration, and drug prediction analyses were conducted to explore biological mechanisms and therapeutic potential.

Results: Differential expression analysis identified 1,020 DEGs, from which 10 co-expression modules were constructed. The blue module demonstrated the strongest correlation with OP (r = 0.99, p<0.0001). LASSO regression analysis prioritized seven candidate genes (LOC286177, nucleobindin 1 [NUCB1], peroxisomal biogenesis factor 19 [PEX19], metastasis associated 1 [MTA1], DRA aassociated protein 1 [DRAP1], protocadherin gamma A1 [PCDHGA1], and pre-mRNA processing factor 39 [PRPF39]), with subsequent validation confirming NUCB1, PEX19, MTA1, DRAP1, and PCDHGA1 as robust diagnostic biomarkers (Area under the curve > 0.85). Functional enrichment implicated these genes in endoplasmic reticulum stress, Wnt/β-catenin signaling, and immune regulatory pathways. Immune profiling further revealed significant perturbations in T-cell and macrophage populations in OP. The Coremine Medical database was leveraged to predict potential therapeutic agents, including both small-molecule and phytochemical candidates.

Conclusion: The present study identified NUCB1, PEX19, MTA1, DRAP1, and PCDHGA1 as promising OP diagnostic markers and explored their roles in bone metabolism. The findings offer insights for early diagnosis and targeted therapy but require further clinical validation.

Keywords
Osteoporosis
Gene Expression Omnibus
Weighted Gene Co-expression Network Analysis
Least Absolute Shrinkage and Selection Operator
Biomarker
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
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Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing
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