Department of Gynecologic Oncology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, No. 251, Yaojiayuan Road, Chaoyang District, Beijing, China.
Sci Rep. 2024 Oct 29;14(1):25939. doi: 10.1038/s41598-024-77539-8.
Endometriosis (EMs) is the prevalent gynecological disease with the typical features of intricate pathogenesis and immune-related factors. Currently, there is no effective therapeutic intervention for EMs. Disulfidptosis, the cell death pattern discovered recently, may show close relationships to immunity and EMs. In this study, bioinformatics analysis was used to investigate the role of disulfide breakdown related genes (DRGs) in EMs. The EMs gene expression matrix was subjected to differential analysis for identifying overlap between differentially expressed genes (DEGs) in EMs and genes associated with disulfide poisoning. Immunoinfiltration analysis was performed. In addition, the association of hub genes with immune cells was examined. Multiple machine learning methods were employed to identify hub genes, construction of predictive models, and validation using external datasets and clinical samples. Totally 15 overlapping genes were identified. Immune-correlation analysis showed that NK cells played a vital role, and these 15 genes were closely related to NK cells. PDLIM1 was further determined as the hub gene through machine learning techniques. Clinical samples and external datasets were adopted for validating the performance in diagnosis. According to the above findings, we built the predictive model, and calculated the AUCs obtained from three external validation datasets to demonstrate the model accuracy. RT-qPCR and IHC analyses were applied to confirm the results. Colony formation was used to verify the effect of PDLIM1 on the proliferation of primary EMs cells. A strong correlation between disulfidptosis and EMs was identified in this study, highlighting its close correlation with the immune microenvironment. Moreover, our results shed new lights on exploring biomarkers and potential therapeutic targets for EMs.
子宫内膜异位症（EMs）是一种常见的妇科疾病，具有复杂的发病机制和免疫相关因素的典型特征。目前，EMs 没有有效的治疗干预措施。最近发现的细胞死亡方式二硫键破坏可能与免疫和 EMs 密切相关。在这项研究中，我们使用生物信息学分析来研究二硫键断裂相关基因（DRGs）在 EMs 中的作用。对 EMs 的基因表达矩阵进行差异分析，以确定 EMs 中差异表达基因（DEGs）与与二硫键中毒相关的基因之间的重叠。进行免疫浸润分析。此外，还检查了枢纽基因与免疫细胞的关联。使用多种机器学习方法识别枢纽基因、构建预测模型，并使用外部数据集和临床样本进行验证。总共鉴定出 15 个重叠基因。免疫相关性分析表明 NK 细胞起着至关重要的作用，这 15 个基因与 NK 细胞密切相关。通过机器学习技术进一步确定 PDLIM1 为枢纽基因。采用临床样本和外部数据集验证诊断性能。根据上述发现，我们构建了预测模型，并计算了三个外部验证数据集的 AUC 值，以证明模型的准确性。应用 RT-qPCR 和 IHC 分析来验证结果。集落形成用于验证 PDLIM1 对原发性 EMs 细胞增殖的影响。本研究确定了二硫键破坏与 EMs 之间的强相关性，突出了其与免疫微环境的密切相关性。此外，我们的结果为探索 EMs 的生物标志物和潜在治疗靶点提供了新的思路。
