目的 探讨子宫内膜异位症（endometriosis，EMs）的基因网络和潜在诊断标志物，并通过临床血清样本验证其诊断效能，解析核心基因与免疫微环境的关联性。方法 整合基因表达汇编（gene expression omnibus，GEO）数据库中的GSE7305（10例健康对照，10例EMs）和GSE51981（34例健康对照，49例EMs）转录组数据集，经ComBat算法校正批次效应，筛选差异表达基因（differentially expressed genes，DEGs）。利用加权基因共表达网络分析（weighted gene co-expression network analysis，WGCNA）识别与EMs相关的分子模块，结合113种机器学习算法构建诊断模型，通过自助法、校准曲线及临床决策曲线验证模型效能。采用CIBERSORT分析免疫细胞浸润，结合单样本基因集富集分析（single-sample gene set enrichment analysis， ssGSEA）和沙普利加性解释（Shapley Additive exPlanations，SHAP）分析，阐释核心基因功能及诊断贡献度。并通过选取2025年3月至2025年5月在中国医科大学附属盛京医院就诊的EMs患者30例和同期就诊的非EMs对照者20例，采用酶联免疫吸附试验（ELISA）、聚合酶链式反应（PCR）和免疫印迹（Western blot）等方法验证血清中核心基因及其编码蛋白的表达。结果 与健康人群相比，EMs患者共鉴定出711个DEGs（上调308个，下调403个）。WGCNA分析显示：greenyellow模块与EMs表型显著正相关，该模块的58个DEGs按差异倍数绝对值排序取前10个通过113种机器学习模型进行评估，其中随机森林（random forest，RF）对EMs的诊断效能最优（C指数=0.991），筛选出核心基因C10orf54、CALCOCO1、ADAT1和KIF21A。SHAP分析显示C10orf54对EMs疾病预测贡献度最高，列线图模型显示C10orf54在EMs疾病进展中权重最高。临床样本验证C10orf54的mRNA在EMs患者血清中表达下调（P<0.05），其编码蛋白VISTA在EMs患者血清表达显著升高（P<0.0001）。ssGSEA显示C10orf54参与调节铜离子应激及细胞周期等通路。免疫浸润表明EMs患者记忆B细胞、CD8⁺ T细胞等活化免疫细胞比例升高，且C10orf54表达与活化免疫细胞呈正相关。结论 C10orf54可作为EMs的潜在诊断标志物，其表达异常与免疫微环境失调密切相关，为EMs的早期诊断和免疫靶向治疗提供了新的分子靶点。

Objective To explore the gene network underlying EMs，identify potential diagnostic biomarkers，validate their diagnostic efficacy using clinical serum specimens，and elucidate the association between core genes and immune microenvironment. Methods Transcriptomic datasets GSE7305 （10 healthy controls， 10 EMs cases） and GSE51981 （34 healthy controls， 49 EMs cases） from the Gene Expression Omnibus （GEO） database were integrated. Batch effects were corrected by the ComBat algorithm， and differentially expressed genes were screened. Weighted Gene Co-expression Network Analysis （WGCNA） was used to identify EMs-related molecular modules，and 113 machine learning algorithms were employed to construct a diagnostic model， whose efficacy was validated by bootstrap， calibration curves and clinical decision curves. CIBERSORT was applied to analyze immune cell infiltration; combined with single-sample Gene Set Enrichment Analysis （ssGSEA） and Shapley Additive exPlanations （SHAP） analysis， the functions of core genes and their diagnostic contributions were clarified. Additionally， 30 EMs patients and 20 non-EMs controls who visited Shengjing Hospital of China Medical University from March to May 2025 were selected， and the expression of core genes and their encoded proteins in serum was verified by enzyme-linked immunosorbent assay （ELISA）， polymerase chain reaction （PCR） and Western blot. Results Compared with healthy people，a total of 711 DEGs were identified in EMs patients （308 upregulated；403 downregulated）. WGCNA indicated that the greenyellow module had the strongest positive correlation with EMs phenotype. The top 10 genes of the 58 DEGs in this module，ranked by absolute fold change，were evaluated through 113 machine learning models，with random forest （RF） demonstrating optimal diagnostic efficacy for EMs （C-index=0.991）. Subsequently，core genes C10orf54，CALCOCO1，ADAT1，and KIF21A were screened out. SHAP analysis indicated C10orf54 as the primary contributor to disease prediction，and the nomogram model showed that C10orf54 had the highest weight in the disease progression of EMs. Clinical specimens validated that the expression of C10orf54 mRNA was downregulated in the serum of EMs patients （P<0.05）， and C10orf54-encoded protein VISTA was significantly upregulated in the serum of EMs patients（P<0.0001）. ssGSEA revealed that C10orf54 was involved in the regulation of copper ion stress and cell cycle. Immune cell infiltration analysis indicated that the proportions of activated immune cells such as memory B cells and CD8⁺ T cells in EMs patients were increased； what's more，C10orf54 expression was positively correlated with activated immune cells. Conclusions C10orf54 may serve as a potential diagnostic biomarker for EMs. Its aberrant expression is closely associated with dysregulation of the immune microenvironment，providing a novel molecular target for early diagnosis and immune-targeted therapy of EMs.