生物信息学筛选与子宫内膜异位症早期诊断相关的自噬差异表达基因

Bioinformatics Screening of Autophagy Differentially Expressed Genes Related to Early Diagnosis of Endometriosis

【目的】探讨生物信息学筛选与子宫内膜异位症(EMT)早期诊断相关的自噬差异表达基因(ATDEGs)。【方法】使用limma包从Gene Expression Omnibus(GEO)数据库获取EMT和健康对照组(healthy control,HC)的差异表达基因(DEGs),并进行GSEA富集分析。通过加权基因共表达网络分析获得与EMT相关的功能模块,并对功能模块中的基因进行功能(GO)和通路(KEGG)富集分析。从Genecards数据库下载了ATDEGs。三部分取交集获取与EMT相关的ATDEGs,并构建了蛋白互作网络图;采用LASSO算法筛选出诊断基因,并通过受试者工作特征(ROC)曲线评估这些基因的诊断能力。【结果】本研究共计获得了62个ATDEGs,并采用Cytoscape的MCODE插件找到了一个最显著的模块,其中包括17个枢纽基因;LASSO分析筛选出7个诊断基因,这些基因在外部数据集中得到了验证,提示其具有较好的诊断能力。【结论】本研究结果有助于EMT的早期诊断和干预,并为进一步的临床工作和机制研究提供了理论基础。

【Objective】To explore the bioinformatics screening of autophagy differentially expressed genes(ATDEGs)related to the early diagnosis of Endometriosis(EMT).【Methods】We used the Limma package to obtain differentially expressed genes(DEGs)from the Gene Expression Omnibus(GEO)database for EMT and healthy control(HC),and conducted GSEA enrichment analysis.The functional modules related to EMT were obtained through the weighted Gene co-expression network analysis,and the genes in the functional modules were enriched with GO and KEGG.ATDEGs had been downloaded from the Genecards database.Taking the intersection of three parts,we obtained ATDEGs related to EMT and constructed a protein interaction network diagram;The LASSO algorithm was used to screen diagnostic genes and evaluate their diagnostic ability through the receiver operating characteristic(ROC)curve.【Results】This study obtained 62 ATDEGs and identified the most significant module using Cytoscape's MCODE plugin,which includes 17 hub genes;Further LASSO analysis identified 7 diagnostic genes,which were validated in external datasets,indicating their good diagnostic ability.【Conclusion】The results of this study contribute to the early diagnosis and intervention of EMT,and provide a theoretical basis for further clinical work and mechanism research.