Table_1_Unraveling immunotherapeutic targets for endometriosis: a transcriptomic and single-cell analysis.docx

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.

背景：子宫内膜异位症（Endometriosis, EMs）是一种常见的妇科疾病，可对女性生活质量造成不良影响。目前其发病机制尚未阐明，且现有诊断方法存在局限。本研究旨在筛选可用于子宫内膜异位症诊断与治疗的潜在分子生物标志物。
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）及最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）等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间的相关性。此外，本研究借助单细胞RNA（single-cell RNA, scRNA）测序数据，分析了免疫细胞浸润程度及免疫核心基因的表达情况。最后，对3种核心基因分别与地诺孕素（dienogest）及戈舍瑞林（goserelin）进行分子对接，以筛选潜在药物作用靶点。
结果：差异表达基因显著富集于免疫应答、血管生成及雌激素相关生物学过程。CXCL12、ROBO3及SCG2被鉴定为核心免疫基因。逆转录聚合酶链反应（Reverse Transcription-Polymerase Chain Reaction, RT-PCR）结果证实，与hESCs细胞相比，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，上述基因具备较高的诊断价值。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为显著特征。CXCL12、ROBO3及SCG2的表达水平与免疫细胞浸润程度呈显著相关。分子对接结果提示，上述基因可作为潜在药物作用靶点。
结论：本研究阐明了子宫内膜异位症与免疫系统的关联，并筛选出潜在的免疫相关生物标志物。本研究结果为开发子宫内膜异位症的临床诊疗策略提供了重要参考依据。