绝经后骨质疏松症（PMOP）是一种常见的影响全球范围的骨骼疾病。老年女性骨质疏松骨折的风险增加对个体和社会造成了重大负担。然而，目前对PMOP缺乏可靠的诊断标志物和精确的治疗靶点仍然是一个主要挑战。从GEO数据库下载了与PMOP相关的数据集GSE7429、GSE56814、GSE56815和GSE147287。使用"limma"软件包确定了差异表达基因（DEGs）。采用WGCNA和机器学习选择与PMOP高度相关的关键模块基因。对所有DEGs和选择的关键中心基因进行了GSEA、DO、GO和KEGG富集分析。通过GeneMANIA数据库构建了蛋白质相互作用网络。ROC曲线和AUC值验证了关键基因在训练和验证数据集中的诊断价值。xCell免疫浸润和单细胞分析确定了关键基因在PMOP免疫反应中的功能。泛癌分析揭示了关键基因在肿瘤中的作用。共鉴定了1278个PMOP患者和健康对照组之间的DEGs。紫色模块和青色模块被选为关键模块，将DEGs和模块基因合并后，选择出112个共同基因。五种机器学习算法筛选出了三个关键基因（KCNJ2、HIPK1和ROCK1），并为这些关键基因构建了PPI网络。ROC曲线验证了ROCK1在既往PMOP训练集（AUC=0.73）和验证集（AUC=0.81）的诊断价值。针对低ROCK1患者进行了GSEA分析，得出最重要富集领域为蛋白质结合和免疫反应。在PMOP中，DCs和NKT细胞表达水平较高。泛癌分析显示低ROCK1表达与SKCM以及肾肿瘤（KIRP、KICH和KIRC）相关。ROCK1与PMOP的发病机制和免疫浸润显著相关，并影响肿瘤的发展、进展和预后，从而为PMOP和肿瘤提供了潜在的治疗靶点。然而，还需要进一步的实验室和临床证据，在临床应用ROCK1作为治疗靶点之前。

Postmenopausal osteoporosis (PMOP) is a prevalent bone disorder with significant global impact. The elevated risk of osteoporotic fracture in elderly women poses a substantial burden on individuals and society. Unfortunately, the current lack of dependable diagnostic markers and precise therapeutic targets for PMOP remains a major challenge.PMOP-related datasets GSE7429, GSE56814, GSE56815, and GSE147287, were downloaded from the GEO database. The DEGs were identified by "limma" packages. WGCNA and Machine Learning were used to choose key module genes highly related to PMOP. GSEA, DO, GO, and KEGG enrichment analysis was performed on all DEGs and the selected key hub genes. The PPI network was constructed through the GeneMANIA database. ROC curves and AUC values validated the diagnostic values of the hub genes in both training and validation datasets. xCell immune infiltration and single-cell analysis identified the hub genes' function on immune reaction in PMOP. Pan-cancer analysis revealed the role of the hub genes in cancers.A total of 1278 DEGs were identified between PMOP patients and the healthy controls. The purple module and cyan module were selected as the key modules and 112 common genes were selected after combining the DEGs and module genes. Five Machine Learning algorithms screened three hub genes (KCNJ2, HIPK1, and ROCK1), and a PPI network was constructed for the hub genes. ROC curves validate the diagnostic values of ROCK1 in both the training (AUC = 0.73) and validation datasets of PMOP (AUC = 0.81). GSEA was performed for the low-ROCK1 patients, and the top enriched field included protein binding and immune reaction. DCs and NKT cells were highly expressed in PMOP. Pan-cancer analysis showed a correlation between low ROCK1 expression and SKCM as well as renal tumors (KIRP, KICH, and KIRC).ROCK1 was significantly associated with the pathogenesis and immune infiltration of PMOP, and influenced cancer development, progression, and prognosis, which provided a potential therapy target for PMOP and tumors. However, further laboratory and clinical evidence is required before the clinical application of ROCK1 as a therapeutic target.