Reproductive Medical Center, Department of Gynecology and Obstetrics, Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
Department of Ultrasonography, Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
J Assist Reprod Genet. 2023 May;40(5):1147-1161. doi: 10.1007/s10815-023-02769-0. Epub 2023 Mar 17.
The objective of this study was to investigate the key glycolysis-related genes linked to immune cell infiltration in endometriosis and to develop a new endometriosis (EMS) predictive model.
A training set and a test set were created from the Gene Expression Omnibus (GEO) public database. We identified five glycolysis-related genes using least absolute shrinkage and selection operator (LASSO) regression and the random forest method. Then, we developed and tested a prediction model for EMS diagnosis. The CIBERSORT method was used to compare the infiltration of 22 different immune cells. We examined the relationship between key glycolysis-related genes and immune factors in the eutopic endometrium of women with endometriosis. In addition, Gene Ontology (GO)-based semantic similarity and logistic regression model analyses were used to investigate core genes. Reverse real-time quantitative PCR (RT-qPCR) of 5 target genes was analysed.
The five glycolysis-related hub genes (CHPF, CITED2, GPC3, PDK3, ADH6) were used to establish a predictive model for EMS. In the training and test sets, the area under the curve (AUC) of the receiver operating characteristic curve (ROC) prediction model was 0.777, 0.824, and 0.774. Additionally, there was a remarkable difference in the immune environment between the EMS and control groups. Eventually, the five target genes were verified by RT-qPCR.
The glycolysis-immune-based predictive model was established to forecast EMS patients' diagnosis, and a detailed comprehension of the interactions between endometriosis, glycolysis, and the immune system may be vital for the recognition of potential novel therapeutic approaches and targets for EMS patients.
本研究旨在探讨与子宫内膜异位症免疫细胞浸润相关的关键糖酵解相关基因，并建立一种新的子宫内膜异位症（EMS）预测模型。
从基因表达综合数据库（GEO）公共数据库中创建训练集和测试集。我们使用最小绝对值收缩和选择算子（LASSO）回归和随机森林方法鉴定了 5 个糖酵解相关基因。然后，我们开发并测试了一种用于 EMS 诊断的预测模型。使用 CIBERSORT 方法比较了 22 种不同免疫细胞的浸润情况。我们检查了子宫内膜异位症患者在位内膜中关键糖酵解相关基因与免疫因子的关系。此外，还使用基因本体论（GO）基于语义相似性和逻辑回归模型分析来研究核心基因。对 5 个靶基因的反转录实时定量 PCR（RT-qPCR）进行了分析。
使用 5 个糖酵解相关枢纽基因（CHPF、CITED2、GPC3、PDK3、ADH6）建立了 EMS 的预测模型。在训练集和测试集中，ROC 预测模型的曲线下面积（AUC）分别为 0.777、0.824 和 0.774。此外，EMS 组和对照组之间的免疫环境存在显著差异。最终，通过 RT-qPCR 验证了 5 个靶基因。
建立了基于糖酵解-免疫的预测模型来预测 EMS 患者的诊断，深入了解子宫内膜异位症、糖酵解和免疫系统之间的相互作用可能对识别潜在的新型治疗方法和 EMS 患者的治疗靶点至关重要。
