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Gender inequality in osteoarthritis: a bioinformatics perspective
Zhenye Guo1, Zhengquan Dong2, Liyun Cheng2
1Department of Orthopaedics, Second Hospital of Shanxi Medical University, No.382, Wuyi Road, Xinghualing District, Taiyuan, 030000, Shanxi, China.
Clinical Rheumatology
|November 19, 2025
Summary
This study reveals key genes and pathways contributing to osteoarthritis's higher prevalence and severity in females. Findings offer potential gender-specific diagnostic markers and therapeutic targets for osteoarthritis.
Area of Science:
- Genomics
- Molecular Biology
- Biomedical Research
Background:
- Osteoarthritis (OA) exhibits higher prevalence and severity in females compared to males.
- Understanding the genetic basis of this gender disparity is crucial for targeted interventions.
Purpose of the Study:
- To identify key genes and molecular pathways responsible for the observed gender differences in osteoarthritis.
- To uncover potential gender-specific diagnostic markers and therapeutic targets for OA.
Main Methods:
- Integrated analysis of three Gene Expression Omnibus (GEO) microarray datasets (GSE12021, GSE55457, GSE55584).
- Identification of differentially expressed genes (DEGs) and enrichment analyses (Gene Ontology, KEGG pathways).
- Construction and analysis of protein-protein interaction (PPI) networks to identify hub genes, validated by RT-qPCR.
Main Results:
- Identified 90 DEGs in female OA patients and 375 in male OA patients compared to controls.
- Discovered 24 common DEGs (15 upregulated, 9 downregulated) between genders.
- Identified distinct sets of hub genes in females (9) and males (5), with RT-qPCR validation of key genes like CTGF, CX3CR1, IL6, and JUN.
Conclusions:
- The identified gender-specific genes and pathways are implicated in the higher incidence and severity of OA in females.
- These findings highlight potential gender-specific diagnostic biomarkers and therapeutic strategies for osteoarthritis.
- The study provides a foundation for developing personalized treatments for OA based on gender.
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