背景:子宫内膜异位症(EMs)是一种未知发病机制的神秘疾病。二硫化物下垂,一种由二硫键应激导致的程序性细胞死亡的新鉴定形式,有机会治疗各种疾病。然而,二硫化物凋亡相关基因(DRGs)在EMs中的潜在作用仍然难以捉摸.本研究旨在深入探索参与EMs的关键二硫化物凋亡基因,并在生物信息学分析的基础上,从二硫键沉积的角度探索新的诊断标志物和候选治疗化合物,机器学习,和动物实验。
结果:对EMs在位和异位子宫内膜组织关键模块基因和差异表达基因(DEGs)的富集分析表明,EMs与二硫化物凋亡密切相关。然后,我们在在位和异位子宫内膜组织中获得了20和16个与二硫键下垂相关的DEGs,分别。蛋白质-蛋白质相互作用(PPI)网络揭示了基因之间复杂的相互作用,并在在位和异位子宫内膜组织中筛选了9个和10个hub基因,分别。此外,免疫浸润分析揭示了免疫细胞的明显差异,人类白细胞抗原(HLA)基因集,与健康对照相比,在位和异位子宫内膜组织中的免疫检查点。此外,上述hub基因与EMs的免疫微环境密切相关。此外,应用四种机器学习算法筛选在位和异位子宫内膜组织中的特征基因,包括二元逻辑回归(BLR),最小绝对收缩和选择运算符(LASSO),支持向量机递归特征消除(SVM-RFE),和极端梯度提升(XGBoost)。使用十倍交叉验证方法对80%的数据进行了模型训练和超参数调整,并在测试集中进行测试,这些测试通过六个指标(灵敏度,特异性,正预测值,负预测值,准确性,和曲线下的面积)。和七个异位标记基因(ACTB,GYS1,IQGAP1,MYH10,NUBPL,SLC7A11,TLN1)和五个异位特征基因(CAPZB,CD2AP,MYH10,OXSM,PDLIM1)最终基于机器学习进行识别。独立的验证数据集还显示了签名基因的高准确性(IQGAP1,SLC7A11,CD2AP,MYH10,PDLIM1)在预测EM中的作用。此外,我们根据异位标记基因筛选了12种针对EMs的特异性化合物,在EMs小鼠模型的异位病变中进一步验证了维甲酸对标记基因的药理学影响.
结论:本研究基于生物信息学分析,验证了二硫化物沉积与EMs之间的密切关系,机器学习,和动物实验。对EM中二硫键沉积的生物学机制的进一步研究有望在EM中寻找潜在的诊断生物标志物和革命性的治疗方法方面产生新的进展。
BACKGROUND: Endometriosis (EMs) is an enigmatic disease of yet-unknown pathogenesis.
Disulfidptosis, a novel identified form of programmed cell death resulting from disulfide stress, stands a chance of treating diverse ailments. However, the potential roles of
disulfidptosis-related genes (DRGs) in EMs remain elusive. This study aims to thoroughly explore the key disulfidptosis genes involved in EMs, and probe novel diagnostic markers and candidate therapeutic compounds from the aspect of
disulfidptosis based on bioinformatics analysis, machine learning, and animal experiments.
RESULTS: Enrichment analysis on key module genes and differentially expressed genes (DEGs) of eutopic and ectopic endometrial tissues in EMs suggested that EMs was closely related to disulfidptosis. And then, we obtained 20 and 16
disulfidptosis-related DEGs in eutopic and ectopic endometrial tissue, respectively. The protein-protein interaction (PPI) network revealed complex interactions between genes, and screened nine and ten hub genes in eutopic and ectopic endometrial tissue, respectively. Furthermore, immune infiltration analysis uncovered distinct differences in the immunocyte, human leukocyte antigen (HLA) gene set, and immune checkpoints in the eutopic and ectopic endometrial tissues when compared with health control. Besides, the hub genes mentioned above showed a close correlation with the immune microenvironment of EMs. Furthermore, four machine learning algorithms were applied to screen signature genes in eutopic and ectopic endometrial tissue, including the binary logistic regression (BLR), the least absolute shrinkage and selection operator (LASSO), the support vector machine-recursive feature elimination (SVM-RFE), and the extreme gradient boosting (XGBoost). Model training and hyperparameter tuning were implemented on 80% of the data using a ten-fold cross-validation method, and tested in the testing sets which determined the excellent diagnostic performance of these models by six indicators (Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, Accuracy, and Area Under Curve). And seven eutopic signature genes (ACTB, GYS1, IQGAP1, MYH10, NUBPL, SLC7A11, TLN1) and five ectopic signature genes (CAPZB, CD2AP, MYH10, OXSM, PDLIM1) were finally identified based on machine learning. The independent validation dataset also showed high accuracy of the signature genes (IQGAP1, SLC7A11, CD2AP, MYH10, PDLIM1) in predicting EMs. Moreover, we screened 12 specific compounds for EMs based on ectopic signature genes and the pharmacological impact of tretinoin on signature genes was further verified in the ectopic lesion in the EMs murine model.
CONCLUSIONS: This study verified a close association between
disulfidptosis and EMs based on bioinformatics analysis, machine learning, and animal experiments. Further investigation on the biological mechanism of disulfidptosis in EMs is anticipated to yield novel advancements for searching for potential diagnostic biomarkers and revolutionary therapeutic approaches in EMs.