骨质疏松症与甲状腺功能亢进的关系

目的 基于临床试验结合生物信息学分析骨质疏松症与甲亢的相互关系。方法 ①选取40例甲亢女性患者（观察组）及40例甲功正常女性（对照组）为研究对象，比较其T值及骨密度；②分别以骨质疏松及甲亢在相关人类疾病数据库中筛选靶基因，整合后映射得出交集靶点，通过STRING v11.0平台得出PPI网络并筛选出核心靶点，通过Metascape数据库进行GO富集分析及KEGG通路分析。结果 观察组的T值及骨密度均低于对照组（P<0.01），骨质疏松与甲亢的核心交集靶点为IL6、TNF、INS、IL1B、ALB、IL10、LEP等，核心靶点集中作用于细胞因子-细胞因子受体的相互作用、Jak-STAT通路、I型糖尿病、HIF-1通路、PI3K-Akt通路、脂肪细胞因子通路等。结论 骨质疏松症与甲亢在复杂基因网络背景下存在一些高度重合的基因表达，所涉及的信号通路能够同时对两种疾病产生干预作用，这提示两种疾病的分子机制存在密切联系，可能为药物同时调控两种疾病提示潜在靶点。在临床试验的基础上应用生物信息学具有较高的置信度和参考价值，为探索疾病间关系提供了新方向与新思路。

Relationship between osteoporosis and hyperthyroidism

Objective To investigate the relationship between osteoporosis and hyperthyroidism based on clinical trials and bioinformatics. Methods (1) 40 female hyperthyroidism patients (observation group) and 40 female patients with normal thyroid function (control group) were selected as the research subjects. Their T-score and BMD were compared. (2) Data of patients with osteoporosis or hyperthyroidism were screened from related disease databases. The two sets of predicted targets were mapped to obtain the intersection targets. The PPI network of targets was obtained through STRING v11.0 platform. The core targets were selected according to the degree value. GO enrichment analysis and KEGG pathway analysis were carried out through Metascape database. Results T-score and BMD in the observation group were lower than those in the control group (P<0.01). The core intersection targets of osteoporosis and hyperthyroidism were IL6, TNF, INS, IL1B, ALB, IL10, LEP, etc. The core target acted on the cytokine-cytokine receptor interaction, Jak-STAT signaling pathway, type I diabetes mellitus, HIF-1 signaling pathway, PI3K-Akt signaling pathway, and adipocytokine signaling pathway, etc. Conclusion Osteoporosis and hyperthyroidism have some highly overlapping gene expressions under the background of complex gene networks. The signal pathways involved can simultaneously function on the two diseases. These results suggest that the molecular mechanisms of the two diseases are closely related and may suggest potential targets for drug regulation of both diseases at the same time. The application of bioinformatics on the basis of clinical trials has high confidence and reference value, which provides new direction and new idea for exploring the relationship among diseases.