Page 269 - EJMO-9-3

Eurasian Journal of Medicine

and Oncology

ORIGINAL RESEARCH ARTICLE
Identification and validation of relevant

diagnostic biomarkers for osteoporosis by
Weighted Gene Co-expression Network Analysis

and machine learning

1
Cuicui Zhou 1,2 , Zarina Awang 1 , and Farra Aidah Jumuddin *
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

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.
*Corresponding author: Methods: We performed an integrative bioinformatics analysis combining Weighted
Farra Aidah Jumuddin Gene Co-expression Network Analysis and machine learning on two Gene Expression
(farraaiadah@lincoln.edu.my) Omnibus datasets (GSE35958, GSE35956). Differentially expressed genes (DEGs) were
Citation: Zhou C, Awang Z, identified using “limma” package of R software, followed by module construction and
Jumuddin FA. Identification and key gene screening via Least Absolute Shrinkage and Selection Operator (LASSO)
validation of relevant diagnostic regression. Functional enrichment, immune infiltration, and drug prediction analyses
biomarkers for osteoporosis by
Weighted Gene Co-expression were conducted to explore biological mechanisms and therapeutic potential.
Network Analysis and machine Results: Differential expression analysis identified 1,020 DEGs, from which 10
learning. Eurasian J Med Oncol. co-expression modules were constructed. The blue module demonstrated the
2025;9(3):261-276.
doi: 10.36922/EJMO025240252 strongest correlation with OP (r = 0.99, p<0.0001). LASSO regression analysis
prioritized seven candidate genes (LOC286177, nucleobindin 1 [NUCB1], peroxisomal
Received: June 09, 2025 biogenesis factor 19 [PEX19], metastasis associated 1 [MTA1], DRA aassociated
Revised: July 25, 2025 protein 1 [DRAP1], protocadherin gamma A1 [PCDHGA1], and pre-mRNA processing
Accepted: July 29, 2025 factor 39 [PRPF39]), with subsequent validation confirming NUCB1, PEX19, MTA1,
DRAP1, and PCDHGA1 as robust diagnostic biomarkers (Area under the curve > 0.85).
Published online: September 9, Functional enrichment implicated these genes in endoplasmic reticulum stress, Wnt/
2025
β-catenin signaling, and immune regulatory pathways. Immune profiling further
Copyright: © 2025 Author(s). revealed significant perturbations in T-cell and macrophage populations in OP. The
This is an Open-Access article
distributed under the terms of the Coremine Medical database was leveraged to predict potential therapeutic agents,
Creative Commons Attribution including both small-molecule and phytochemical candidates.
License, permitting distribution, Conclusion: The present study identified NUCB1, PEX19, MTA1, DRAP1, and
and reproduction in any medium, PCDHGA1 as promising OP diagnostic markers and explored their roles in bone
provided the original work is
properly cited. metabolism. The findings offer insights for early diagnosis and targeted therapy but
require further clinical validation.
Publisher’s Note: AccScience
Publishing remains neutral with
regard to jurisdictional claims in Keywords: Osteoporosis; Gene Expression Omnibus; Weighted Gene Co-expression
published maps and institutional Network Analysis; Least Absolute Shrinkage and Selection Operator; Biomarker
affiliations.

Volume 9 Issue 3 (2025) 261 doi: 10.36922/EJMO025240252