Understanding postmenopausal osteoporosis
Postmenopausal osteoporosis is a common bone disorder that affects women after menopause and is characterized by reduced bone mass and increased fracture risk. The condition is closely linked to hormonal changes, particularly the decline in estrogen levels, which disrupts the balance between bone formation and bone resorption. As bone breakdown begins to outpace bone formation, bones gradually become weaker and more fragile.
Despite its clinical importance, postmenopausal osteoporosis is often diagnosed only after substantial bone loss has already occurred. Current diagnostic approaches rely largely on bone mineral density measurements, which do not fully capture the molecular changes that precede structural damage. This gap has driven interest in identifying blood-based molecular markers that could support earlier detection and provide insight into disease mechanisms.
In this context, the study analyzed here explores gene expression patterns in peripheral blood to identify potential diagnostic markers for postmenopausal osteoporosis, with a particular focus on genes related to N6-methyladenosine (m6A) RNA modification.
Why m6A-related genes matter
m6A is a common RNA modification that plays an important role in regulating RNA stability, translation, and degradation. Increasing evidence suggests that m6A regulators influence immune responses, cell differentiation, and metabolic processes, all of which are relevant to bone remodeling. However, their role in postmenopausal osteoporosis has not been well defined.
The authors set out to examine whether m6A-related gene expression patterns differ between women with postmenopausal osteoporosis and healthy controls, and whether these patterns could help identify robust diagnostic biomarkers.
Study design and analytical strategy
The researchers analyzed gene expression data from publicly available datasets of peripheral blood samples. One dataset was used to identify differentially expressed genes and build diagnostic models, while an independent dataset served for external validation. In addition, a small group of clinical samples was used for experimental validation using quantitative PCR.
The analysis followed a stepwise strategy. First, the authors identified m6A regulators that showed altered expression in postmenopausal osteoporosis. Next, they used consensus clustering to classify patients into molecular subtypes based on m6A regulator expression patterns. They then explored differences in immune-related gene signatures across these subtypes.
To identify diagnostic markers, the authors applied three machine-learning approaches—LASSO regression, support vector machine–recursive feature elimination, and random forest analysis—and focused on genes consistently selected across all methods.
Key findings
The study identified six m6A-related genes with altered expression in postmenopausal osteoporosis. Based on these regulators, patients could be grouped into three distinct molecular clusters, each showing different immune-related expression profiles. This suggests that m6A-associated gene regulation may be linked to immune processes in the disease.
From a broader set of genes associated with these clusters, three genes emerged as potential diagnostic markers: TUBB, CLTA, and FBXL5. Two of these genes, TUBB and CLTA, were expressed at lower levels in patients with postmenopausal osteoporosis, while FBXL5 showed higher expression.
Importantly, each of the three genes demonstrated moderate diagnostic performance on its own, and their predictive value improved when combined into a single model. These findings were consistent across the discovery dataset, an independent validation dataset, and experimental validation in blood samples.
What makes this study novel
A key contribution of this work is the integration of m6A biology with machine-learning approaches to identify blood-based diagnostic markers for postmenopausal osteoporosis. Rather than focusing on bone tissue, the study highlights molecular signals detectable in peripheral blood, which is more accessible in clinical settings.
Another novel aspect is the identification of molecular subtypes linked to immune-related signatures, suggesting that postmenopausal osteoporosis may involve heterogeneous regulatory mechanisms rather than a single uniform pathway.
Practical implications for research and clinical practice
While the study does not propose immediate clinical use, it provides a foundation for future research into minimally invasive diagnostic tools for postmenopausal osteoporosis. The identified genes could serve as candidates for further validation in larger and more diverse populations.
For researchers, the work illustrates how combining epigenetic regulators, immune-related analyses, and machine learning can uncover biologically meaningful patterns in complex diseases. For the field of osteoporosis research, it opens new directions for exploring how RNA modification and immune regulation contribute to disease development.
Looking ahead
The authors emphasize that further studies are needed to confirm these findings and clarify the biological roles of the identified genes in bone metabolism. Larger clinical cohorts and functional experiments will be essential to determine whether these markers can complement existing diagnostic methods.
Overall, this study adds a new molecular perspective to postmenopausal osteoporosis and highlights the potential of blood-based gene expression profiling to improve disease characterization and diagnosis.
The translation of the preceding English text in Chinese:
理解绝经后骨质疏松
绝经后骨质疏松是一种常见的骨骼疾病，主要发生在女性绝经之后，其特征是骨量减少以及骨折风险增加。该疾病与激素变化密切相关，尤其是雌激素水平的下降，这种变化会打破骨形成与骨吸收之间的平衡。当骨吸收速度超过骨形成速度时，骨骼会逐渐变得脆弱，更容易发生骨折。
尽管具有重要的临床意义，绝经后骨质疏松往往在已经发生明显骨量丢失之后才被诊断。目前的诊断方法主要依赖骨密度测量，而这一方法并不能充分反映结构性损伤出现之前的分子层面变化。正是由于这一局限性，研究人员开始关注基于血液的分子标志物，以期支持更早期的检测，并加深对疾病机制的理解。
在这一背景下，本文所分析的研究通过检测外周血中的基因表达模式，探索绝经后骨质疏松的潜在诊断标志物，尤其关注与 N6-甲基腺苷（m6A）RNA 修饰相关的基因。
为什么 m6A 相关基因很重要
m6A 是一种常见的 RNA 修饰形式，在调控 RNA 稳定性、翻译和降解过程中发挥重要作用。越来越多的证据表明，m6A 调控因子会影响免疫反应、细胞分化以及代谢过程，而这些过程都与骨重塑密切相关。然而，m6A 在绝经后骨质疏松中的作用仍缺乏系统研究。
作者旨在探讨绝经后骨质疏松患者与健康对照人群之间，m6A 相关基因表达模式是否存在差异，以及这些差异是否有助于识别可靠的诊断生物标志物。
研究设计与分析策略
研究人员分析了来源于公开数据库的外周血基因表达数据。其中一个数据集用于筛选差异表达基因并构建诊断模型，另一个独立数据集用于外部验证。此外，研究还使用了一小组临床血液样本，通过定量 PCR 进行实验验证。
分析采用了分步骤的策略。首先，作者鉴定了在绝经后骨质疏松中表达发生改变的 m6A 调控因子。随后，基于这些 m6A 调控因子的表达模式，利用一致性聚类方法将患者划分为不同的分子亚型。接着，研究比较了这些亚型之间免疫相关基因特征的差异。
在诊断标志物筛选阶段，作者应用了三种机器学习方法——LASSO 回归、支持向量机递归特征消除以及随机森林分析，并重点关注在所有方法中均被一致选中的基因。
主要研究发现
研究共鉴定出 6 个在绝经后骨质疏松中表达异常的 m6A 相关基因。基于这些调控因子，患者可被划分为 3 个不同的分子聚类，每个聚类均表现出不同的免疫相关表达特征。这一结果提示，m6A 相关的基因调控可能与疾病中的免疫过程有关。
在与这些聚类相关的更大基因集合中，研究筛选出了 3 个潜在的诊断标志物：TUBB、CLTA 和 FBXL5。其中，TUBB 和 CLTA 在绝经后骨质疏松患者中的表达水平较低，而 FBXL5 的表达水平较高。
值得注意的是，这三个基因单独使用时均表现出中等的诊断能力，而将它们组合成一个模型后，其预测性能得到了提升。这些结果在发现数据集、独立验证数据集以及血液样本的实验验证中均保持一致。
本研究的新颖之处
本研究的一个重要贡献在于，将 m6A 生物学与机器学习方法相结合，用于识别绝经后骨质疏松的血液诊断标志物。研究并未局限于骨组织，而是强调了在外周血中即可检测到的分子信号，这在临床实践中更具可操作性。
另一个新颖之处在于识别了与免疫相关特征相联系的分子亚型，这表明绝经后骨质疏松可能涉及多种不同的调控机制，而非单一的疾病通路。
对科研与临床实践的意义
尽管该研究尚未提出可立即应用于临床的诊断方案，但它为未来开发微创诊断工具奠定了基础。所识别的基因可作为候选标志物，在更大规模和更具多样性的人群中进一步验证。
对于研究人员而言，该研究展示了如何通过整合表观遗传调控因子、免疫相关分析和机器学习方法，揭示复杂疾病中的生物学模式。对于骨质疏松研究领域而言，该工作为探索 RNA 修饰和免疫调控在疾病发生发展中的作用提供了新的方向。
未来展望
作者强调，还需要进一步研究来验证这些发现，并阐明相关基因在骨代谢中的生物学功能。更大规模的临床队列研究以及功能实验，将有助于确定这些标志物是否能够补充现有的诊断方法。
总体而言，该研究为理解绝经后骨质疏松提供了新的分子层面视角，并突出了基于血液的基因表达分析在改善疾病表征和诊断方面的潜力。
Reference:
Yue Tan, Yujing Wang, Qin Zhu, Yan Xue, Xuhao Ji, Zhenkun Li, Jiawen Shen, Chengming Sun, Shiqi Ren, Chenlin Zhang, Jianfeng Chen
Identification and validation of TUBB, CLTA, and FBXL5 as potential diagnostic markers of postmenopausal osteoporosis.
Biomol Biomed [Internet]. 2025 Aug. 11 [cited 2025 Dec. 16];26(2):354–367.
Available from: https://www.bjbms.org/ojs/index.php/bjbms/article/view/12019
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