Genomic Analysis of Cellular Senescence and Osteoporosis Target Genes and Small Molecule Screening
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摘要:
目的 利用公共数据库中的数据探索衰老与骨质疏松的共同靶基因，筛选有潜在治疗作用的小分子化合物。 方法 从公共基因表达数据库（gene expression omnibus，GEO）中下载骨质疏松数据集GSE56814和GSE56815并合并，从Genecard数据库和Cell age数据库中选取衰老基因并合并，使用R软件对数据进行清洗，筛选出差异基因，制作火山图，进行加权基因共表达筛选出关键模块和基因以及使用机器学习方法筛选靶基因，利用微生信平台绘制韦恩图，Cytoscape软件进行蛋白互作结果可视化，使用STRING 数据库进行差异编码蛋白的相互作用分析，利用Cmap数据库进行小分子化合物的预测。绘制受试者工作特征曲线，找出最有意义的靶基因，在此基础上建立逻辑回归模型并绘制列线图。 结果 使用limma包共筛选出127个差异基因。对差异基因进行加权基因共表达网络分析，筛选出103个高表达基因。采用拉索回归、支持向量机和随机森林模型3种机器学习方法，最终获得4个靶基因：FOXO3、HIRA、CBX5、RAD1。列线图和校准曲线显示出良好的预测效果。使用Cmap数据库筛选出有潜在治疗作用的5种小分子化合物：樱黄素（O-甲基化异黄酮）、XMD-885、杠柳次苷、依维莫司和XMD- 1150 。结论 FOXO3、HIRA基因目前已证实是衰老和骨质疏松共同的靶基因，樱黄素对衰老和骨质疏松有潜在治疗作用。 Abstract:Objective To explore common target genes related to aging and osteoporosis using data from public databases and to screen for small molecule compounds with potential therapeutic effects. Methods Osteoporosis datasets GSE56814 and GSE56815 were downloaded and merged from the public gene expression database （Gene Expression Omnibus （GEO）） . Senescent genes were selected and merged from the genecard database and the cell age database. Data cleaning and differential gene selection were performed using R software, and a volcano plot was created. Weighted gene co-expression network analysis was performed to screen out key modules and genes, and the machine learning methods were employed to screen target genes. A Venn diagram was drawn using the the Microbioinformatics platform, and cytoscape software was used to visualise protein interactions results. Interaction analysis of differentially encoded proteins was carried out using the STRING database, and prediction of small molecule compounds was carried out using Cmap database. Finally, Receiver operating characteristic （ROC） curves were plotted to find out the most meaningful target genes, followed by the establishment of a logistic regression model and the creation of a nomogram. Results A total of 127 differential genes were screened using the limma package. Weighted gene co-expression network analysis was performed on the differential genes and 103 highly expressed genes were screened. Three machine learning methods, lasso regression, support vector machine and random forest model, were used to finally obtain four target genes: FOXO3, HIRA, CBX5 and RAD1. The nomogram and calibration curves showed good predictive results. Five small molecule compounds with potential therapeutic effects were identified using the Cmap database: sakuraflavin （O-methylated isoflavone）, XMD-885, kangaroo hyoscyamine, everolimus and XMD-1150. Conclusion FOXO3 and HIRA genes have now been confirmed to be the common target genes for aging and osteoporosis, and sakuraflavin has potential therapeutic effects on both conditions. -
Key words:
- Cellular senescence /
- Osteoporosis /
- Target genes /
- Small molecule compounds /
- Genomic analysis
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图 2 差异基因结果及交集
A：差异基因火山图结果；B：骨质疏松与衰老基因交集。
Figure 2. Differential gene results and intersections
图 3 加权基因共表达分析结果
A：样本聚类图；B:软阈值；C：基因聚类树；D：模块与临床相关性。
Figure 3. Weighted gene co-expression analysis results
图 5 17个交集基因的GO-KEGG富集分析
A：BP；B：CC；C：MF；D：KEGG。
Figure 5. GO-KEGG Enrichment analysis of 17 intersecting genes
图 6 机器学习筛选靶基因
A：17个基因的LASSO系数路径图；B：交叉验证曲线；C：SVM-RFE；D：随机森林15个基因重要性；E：三种机器学习方法筛选基因的交集。
Figure 6. Machine learning screening of target genes
图 7 4个靶基因的受试者工作曲线
A：CBX5基因的ROC；B：FOXO3基因的ROC；C：HIRA基因的ROC；D：RAD1基因的ROC。
Figure 7. ROC curves for the four target genes
图 8 4个靶基因的列线图与ROC曲线
A：4个靶基因列线图；B：逻辑回归模型的预测效果ROC曲线。
Figure 8. Nomogram and ROC curves for the four target genes
表 1 CMap分析发现的潜在衰老和骨质疏松症药物小分子化合物
Table 1. Potential small molecular compounds for aging and osteoporosis found in CMap analysis
连通性评分 药物名称 说明 −98.39 prunetin Breast cancer resistance protein inhibitor −97.82 XMD-885 Leucine rich repeat kinase inhibitor −97.64 periplocymarin Apoptosis stimulant −96.95 everolimus MTOR inhibitor −96.37 XMD- 1150 Leucine rich repeat kinase inhibitor 
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