Zhang Yi, Wu Lulu, Wen Xiang, Lv Xiuwei
Department of Gynecology, Second Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Changsha 410005, China.
Department of Integrated Traditional Chinese and Western Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, China.
Heliyon. 2023 Jul 15;9(7):e18277. doi: 10.1016/j.heliyon.2023.e18277. eCollection 2023 Jul.
The enigmatic nature of Endometriosis (EMS) pathogenesis necessitates investigating alterations in signaling pathway activity to enhance our comprehension of the disease's characteristics.
Three published gene expression profiles (GSE11691, GSE25628, and GSE7305 datasets) were downloaded, and the "combat" algorithm was employed for batch correction, gene expression difference analysis, and pathway enrichment difference analysis. The protein-protein interaction (PPI) network was constructed to identify core genes, and the relative enrichment degree of gene sets was evaluated. The Lasso regression model identified candidate gene sets with diagnostic value, and a risk scoring diagnostic model was constructed for further validation on the GSE86534 and GSE5108 datasets. CIBERSORT was used to assess the composition of immune cells in EMS, and the correlation between EMS diagnostic value gene sets and immune cells was evaluated.
A total of 568 differentially expressed genes were identified between eutopic and ectopic endometrium, with 10 core genes in the PPI network associated with cell cycle regulation. Inflammation-related pathways, including cytokine-receptor signaling and chemokine signaling pathways, were significantly more active in ectopic endometrium compared to eutopic endometrium. Diagnostic gene sets for EMS, such as homologous recombination, base excision repair, DNA replication, P53 signaling pathway, adherens junction, and SNARE interactions in vesicular transport, were identified. The risk score's area under the curve (AUC) was 0.854, as indicated by the receiver operating characteristic (ROC) curve, and the risk score's diagnostic value was validated by the validation cohort. Immune cell infiltration analysis revealed correlations between the risk score and Macrophages M2, Plasma cells, resting NK cells, activated NK cells, and regulatory T cells.
The risk scoring diagnostic model, based on pathway activity, demonstrates high diagnostic value and offers novel insights and strategies for the clinical diagnosis and treatment of Endometriosis.
子宫内膜异位症（EMS）发病机制的神秘本质使得有必要研究信号通路活性的改变，以加深我们对该疾病特征的理解。
下载了三个已发表的基因表达谱（GSE11691、GSE25628和GSE7305数据集），并采用“combat”算法进行批次校正、基因表达差异分析和通路富集差异分析。构建蛋白质-蛋白质相互作用（PPI）网络以识别核心基因，并评估基因集的相对富集程度。Lasso回归模型识别具有诊断价值的候选基因集，并构建风险评分诊断模型，在GSE86534和GSE5108数据集上进行进一步验证。使用CIBERSORT评估EMS中免疫细胞的组成，并评估EMS诊断价值基因集与免疫细胞之间的相关性。
在位内膜和异位内膜之间共鉴定出568个差异表达基因，PPI网络中有10个核心基因与细胞周期调控相关。与炎症相关的通路，包括细胞因子-受体信号通路和趋化因子信号通路，在异位内膜中比在位内膜中显著更活跃。鉴定出了EMS的诊断基因集，如同源重组、碱基切除修复、DNA复制、P53信号通路、黏着连接以及囊泡运输中的SNARE相互作用。接受者操作特征（ROC）曲线显示，风险评分的曲线下面积（AUC）为0.854，验证队列验证了风险评分的诊断价值。免疫细胞浸润分析揭示了风险评分与M2巨噬细胞、浆细胞、静息自然杀伤细胞、活化自然杀伤细胞和调节性T细胞之间的相关性。
基于通路活性的风险评分诊断模型具有较高的诊断价值，为子宫内膜异位症的临床诊断和治疗提供了新的见解和策略。
