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Objective <p>Osteoporosis poses a significant global health burden, characterized by reduced bone mass and an increased risk of fractures. Despite extensive research, the underlying molecular mechanisms driving osteoporosis remain incompletely understood. This study aimed to elucidate the roles of key regulatory genes and their causal mechanisms in osteoporosis through integrative bioinformatics analysis and Mendelian randomization (MR).</p> Methods <p>Gene expression data (GSE56815) was analysed to identify differentially expressed genes (GEGs) between high and low mineral density (BMD) groups. Weighted gene co-expression network analysis (WGCNA) and GEGs analysis were performed. Functional enrichment analysis and protein–protein interaction (PPI) network analysis were conducted to characterize the biological roles of identified genes. Mendelian randomization (MR) analysis assessed causal relationships between gene expression levels and osteoporosis risk. 30 subjects (10 each of healthy/moderate/severe osteoporosis) and 20 rats (Sham/Model/sh-RPS6/Model + sh-RPS6 groups) were included in this study. Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR), Western blot and immunohistochemical analysis were used to detect RPS6 expression; bone histopathology was analyzed by Hematoxylin and Eosin (HE) staining; bone metabolism markers (N-MID) and inflammatory factors (IL-6, IL-1β) were determined using ELISA.</p> Results <p>Differential gene expression analysis identified 2299 DEGs between high and low BMD samples. WGCNA revealed nine gene modules, with MEgrey exhibiting the strongest correlation with sample grouping. Integration of DEGs and WGCNA modules identified 572 candidate feature genes (CFGs) enriched in pathways relevant to bone metabolism. PPI network analysis identified 10 hub candidate feature genes (HCFGs), including HIST1H4J, POLR2F, POLR2H, HIST1H3A, TBP, RPS6, FAU, GTF2H4, HIST1H2AB, and RPL27. The results of the nomogram model suggest a high accuracy of the 10 HCFGs in predicting osteoporosis risk. MR analysis results in a causal relationship between RPS6 and osteoporosis risk. RPS6 expression was found to be disease severity-dependently elevated in osteoporosis patients (healthy group &lt; moderate group &lt; severe group, P &lt; 0.01). In animal models, RPS6 knockdown significantly improved osteoporosis pathological features: restoration of bone trabecular structure and reduction of bone marrow cavity; it also reversed bone metabolic imbalance (N-MID up-regulation) and inflammatory response (TNF-α and IL-6 down-regulation).</p> Conclusion <p>This integrative study combining bioinformatics, MR analysis, and experimental validation provides novel insights into the molecular mechanisms underlying osteoporosis. The findings highlight RPS6 as a key regulator promoting osteoporosis progression via modulation of bone remodeling and inflammatory responses, suggesting its potential as a promising therapeutic target and offering a foundation for future precision medicine strategies in osteoporosis management.</p>

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The Potential Role and Causal Mechanisms of Hub Genes in Osteoporosis: A Study Based on Bioinformatics and Mendelian Randomization Analysis

  • 期刊论文

  • 发表日期：

    2025年6月13日

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  • Bin Fu,
  • Guozhen Liu,
  • Leilei Lin,
  • Jianwei Si

摘要

Objective

Osteoporosis poses a significant global health burden, characterized by reduced bone mass and an increased risk of fractures. Despite extensive research, the underlying molecular mechanisms driving osteoporosis remain incompletely understood. This study aimed to elucidate the roles of key regulatory genes and their causal mechanisms in osteoporosis through integrative bioinformatics analysis and Mendelian randomization (MR).

Methods

Gene expression data (GSE56815) was analysed to identify differentially expressed genes (GEGs) between high and low mineral density (BMD) groups. Weighted gene co-expression network analysis (WGCNA) and GEGs analysis were performed. Functional enrichment analysis and protein–protein interaction (PPI) network analysis were conducted to characterize the biological roles of identified genes. Mendelian randomization (MR) analysis assessed causal relationships between gene expression levels and osteoporosis risk. 30 subjects (10 each of healthy/moderate/severe osteoporosis) and 20 rats (Sham/Model/sh-RPS6/Model + sh-RPS6 groups) were included in this study. Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR), Western blot and immunohistochemical analysis were used to detect RPS6 expression; bone histopathology was analyzed by Hematoxylin and Eosin (HE) staining; bone metabolism markers (N-MID) and inflammatory factors (IL-6, IL-1β) were determined using ELISA.

Results

Differential gene expression analysis identified 2299 DEGs between high and low BMD samples. WGCNA revealed nine gene modules, with MEgrey exhibiting the strongest correlation with sample grouping. Integration of DEGs and WGCNA modules identified 572 candidate feature genes (CFGs) enriched in pathways relevant to bone metabolism. PPI network analysis identified 10 hub candidate feature genes (HCFGs), including HIST1H4J, POLR2F, POLR2H, HIST1H3A, TBP, RPS6, FAU, GTF2H4, HIST1H2AB, and RPL27. The results of the nomogram model suggest a high accuracy of the 10 HCFGs in predicting osteoporosis risk. MR analysis results in a causal relationship between RPS6 and osteoporosis risk. RPS6 expression was found to be disease severity-dependently elevated in osteoporosis patients (healthy group < moderate group < severe group, P < 0.01). In animal models, RPS6 knockdown significantly improved osteoporosis pathological features: restoration of bone trabecular structure and reduction of bone marrow cavity; it also reversed bone metabolic imbalance (N-MID up-regulation) and inflammatory response (TNF-α and IL-6 down-regulation).

Conclusion

This integrative study combining bioinformatics, MR analysis, and experimental validation provides novel insights into the molecular mechanisms underlying osteoporosis. The findings highlight RPS6 as a key regulator promoting osteoporosis progression via modulation of bone remodeling and inflammatory responses, suggesting its potential as a promising therapeutic target and offering a foundation for future precision medicine strategies in osteoporosis management.