DataSheet_3_Unraveling immunotherapeutic targets for endometriosis: a transcriptomic and single-cell analysis.xlsx

BackgroundEndometriosis (EMs), a common gynecological disorder, adversely affects the quality of life of females. The pathogenesis of EMs has not been elucidated and the diagnostic methods for EMs have limitations. This study aimed to identify potential molecular biomarkers for the diagnosis and treatment of EMs.
MethodsDifferential gene expression (DEG) and functional enrichment analyses were performed using the R language. WGCNA, Random Forest, SVM-REF and LASSO methods were used to identify core immune genes. The CIBERSORT algorithm was then used to analyse the differences in immune cell infiltration and to explore the correlation between immune cells and core genes. In addition, the extent of immune cell infiltration and the expression of immune core genes were investigated using single-cell RNA (scRNA) sequencing data. Finally, we performed molecular docking of three core genes with dienogest and goserelin to screen for potential drug targets.
ResultsDEGs enriched in immune response, angiogenesis and estrogen processes. CXCL12, ROBO3 and SCG2 were identified as core immune genes. RT-PCR confirmed that the expression of CXCL12 and SCG2 was significantly upregulated in 12Z cells compared to hESCs cells. ROC curves showed high diagnostic value for these genes. Abnormal immune cell distribution, particularly increased macrophages, was observed in endometriosis. CXCL12, ROBO3 and SCG2 correlated with immune cell levels. Molecular docking suggested their potential as drug targets.
ConclusionThis study investigated the correlation between EMs and the immune system and identified potential immune-related biomarkers. These findings provided valuable insights for developing clinically relevant diagnostic and therapeutic strategies for EMs.

研究背景：子宫内膜异位症（Endometriosis, EMs）是一种常见的妇科疾病，会对女性的生活质量造成显著不良影响。目前其发病机制尚未阐明，且现有临床诊断方法存在一定局限性。本研究旨在筛选可用于EMs诊断与治疗的潜在分子生物标志物。

研究方法：本研究采用R语言开展差异基因表达（Differential gene expression, DEG）分析与功能富集分析。通过加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA）、随机森林（Random Forest）、支持向量机-递归特征消除（Support Vector Machine-Recursive Feature Elimination, SVM-REF）以及最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）等多种方法筛选核心免疫基因。随后采用CIBERSORT算法分析免疫细胞浸润差异，并探究免疫细胞与核心基因之间的相关性。此外，本研究利用单细胞RNA（single-cell RNA, scRNA）测序数据，对免疫细胞浸润程度及免疫核心基因的表达水平进行验证。最后，本研究对3种核心基因分别与地诺孕素（dienogest）、戈舍瑞林（goserelin）进行分子对接，以筛选潜在药物靶点。

研究结果：筛选得到的差异基因主要富集于免疫应答、血管生成及雌激素调控相关生物学过程。最终鉴定出CXCL12、ROBO3与SCG2作为核心免疫基因。实时荧光定量聚合酶链反应（Reverse Transcription Polymerase Chain Reaction, RT-PCR）实验证实，相较于hESCs细胞，12Z细胞中CXCL12与SCG2的表达水平显著上调。受试者工作特征（Receiver Operating Characteristic, ROC）曲线分析显示，上述基因具有较高的诊断价值。子宫内膜异位症患者体内存在免疫细胞分布异常，尤以巨噬细胞浸润增加最为显著。CXCL12、ROBO3与SCG2的表达水平与免疫细胞浸润程度呈显著相关性。分子对接结果提示，这三种基因可作为潜在药物靶点。

研究结论：本研究阐明了子宫内膜异位症与免疫系统的相关性，并筛选得到潜在的免疫相关生物标志物。本研究结果为开发针对EMs的临床诊断与治疗策略提供了重要的理论依据与研究思路。