Department of Minimally Invasive Gynecologic Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing 100006, China.
Department of Minimally Invasive Gynecologic Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing 100006, China.
Reprod Biomed Online. 2021 Feb;42(2):429-441. doi: 10.1016/j.rbmo.2020.10.005. Epub 2020 Oct 14.
Is abnormal gene module expression in the eutopic endometrium related to the occurrence of endometriosis?
Nine datasets of normal and eutopic endometrium were searched and collected through the National Center for Biotechnology Information Gene Expression Omnibus, which included genome-wide expression studies of 71 normal cases and 142 endometriosis cases. Surrogate variable analysis was used for dataset integration. The network module and hub genes were selected by weighted gene co-expression network analysis. Machine learning was used to establish a diagnostic model of endometriosis.
A gene module that was most relevant to endometriosis was selected through weighted gene co-expression network analysis. After further analysis of this module, four hub genes that represent the function of this module were selected: SCAF11, KRAS, MDM2 and KIF3A. Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the four hub genes revealed that all of them were most highly correlated with genes enriched in the ubiquitin-mediated proteolysis pathway. Moreover, in the correlation analysis between hub genes and Jab1, SCAF11 was found to be closely related to Jab1. Furthermore, hub genes were effective indicators for clinical diagnosis. The deep machine learning diagnostic model based on hub genes was highly sensitive.
The gene module identified is highly correlated with endometriosis. The four hub genes in this module degrade p27 through the ubiquitin-mediated proteolysis pathway to regulate the endometrium cell cycle and affect the development of endometriosis. The hub genes and the deep learning model based on them are valuable for clinical diagnosis.
在位子宫内膜异常基因模块表达与子宫内膜异位症的发生有关吗？
通过美国国立生物技术信息中心基因表达综合数据库搜索并收集了 9 个正常和在位子宫内膜数据集，其中包括 71 例正常病例和 142 例子宫内膜异位症病例的全基因组表达研究。采用替代变量分析进行数据集整合。通过加权基因共表达网络分析选择网络模块和枢纽基因。采用机器学习建立子宫内膜异位症的诊断模型。
通过加权基因共表达网络分析选择了与子宫内膜异位症最相关的基因模块。进一步分析该模块后，选择了代表该模块功能的 4 个枢纽基因：SCAF11、KRAS、MDM2 和 KIF3A。对这 4 个枢纽基因的京都基因与基因组百科全书富集分析表明，它们都与泛素介导的蛋白水解途径中富集的基因高度相关。此外，在枢纽基因与 Jab1 的相关性分析中发现，SCAF11 与 Jab1 密切相关。此外，枢纽基因是临床诊断的有效指标。基于枢纽基因的深度学习诊断模型具有较高的敏感性。
鉴定的基因模块与子宫内膜异位症高度相关。该模块中的 4 个枢纽基因通过泛素介导的蛋白水解途径降解 p27，调节子宫内膜细胞周期，影响子宫内膜异位症的发生。基于这些枢纽基因的模型和深度学习模型对临床诊断具有重要价值。
