Gong Yining, Hao Dingjun, Zhang Yong, Tu Yongyong, He Baorong, Yan Liang
Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an 710054, China.
Institute of Orthopedic Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an 710054, China.
Biomedicines. 2023 Oct 4;11(10):2701. doi: 10.3390/biomedicines11102701.
Osteoporosis is common in postmenopausal women but is often asymptomatic until a fracture occurs, highlighting the importance of early screening and preventive interventions. This study aimed to develop molecular subtype risk stratification of postmenopausal osteoporosis and analyze the immune infiltration microenvironment. Microarray data for osteoporosis were downloaded and analyzed. Logistic and least absolute shrinkage and selection operator (LASSO) regression analyses were used to construct the molecular risk model. Circulating blood samples were collected from 10 enrolled participants to validate the key differentially expressed genes, and consistent clustering based on the expression profiles of candidate genes was performed to obtain molecular subtypes. Three key genes, , , and , were obtained as variables and used to construct the risk model. External experimental validation showed substantial differences in the three key genes between patients with osteoporosis and the controls ( < 0.05). Three subtypes were obtained based on dimensionality reduction clustering results. Cluster 3 had significantly more patients with low bone mineral density (BMD), whereas Cluster 2 had significantly more patients with high BMD ( < 0.05). This study introduced a novel molecular risk model and subtype classification system, which is an evidence-based screening strategy that will guide the active prevention, early diagnosis, and treatment of osteoporosis in high-risk postmenopausal women.
骨质疏松症在绝经后女性中很常见,但在骨折发生之前通常没有症状,这凸显了早期筛查和预防性干预的重要性。本研究旨在开发绝经后骨质疏松症的分子亚型风险分层并分析免疫浸润微环境。下载并分析了骨质疏松症的微阵列数据。使用逻辑回归和最小绝对收缩和选择算子(LASSO)回归分析来构建分子风险模型。从10名入组参与者中采集循环血样以验证关键差异表达基因,并基于候选基因的表达谱进行一致性聚类以获得分子亚型。获得了三个关键基因, 、 和 ,作为变量并用于构建风险模型。外部实验验证显示骨质疏松症患者与对照组之间这三个关键基因存在显著差异(<0.05)。基于降维聚类结果获得了三个亚型。第3组骨密度(BMD)低的患者明显更多,而第2组骨密度高的患者明显更多(<0.05)。本研究引入了一种新的分子风险模型和亚型分类系统,这是一种基于证据的筛查策略,将指导高危绝经后女性骨质疏松症的积极预防、早期诊断和治疗。