中-重度子宫内膜异位症特征基因筛选

doi:10.3969/j.issn.1006-7795.2024.02.021

摘要/Abstract

摘要： 目的筛选中-重度子宫内膜异位症（endometriosis，EM）相关特征基因。方法从GEO数据库下载EM相关基因表达数据，筛选中-重度EM与正常子宫内膜之间差异表达基因（differentially expressed genes，DEGs)。应用最小绝对值收缩与选择算子（least absolute shrinkage and selection operator,LASSO）及支持向量机递归特征消除（support vector machine recursive feature elimination，SVM-RFE）两种机器学习方法筛选中-重度EM相关基因，并通过受试者工作特征（receiver operating characteristic,ROC）曲线评估特征基因的诊断价值。应用CIBERSORT对中-重度EM相关免疫细胞浸润特征以及特征基因与免疫细胞浸润相关性进行分析。结果在中-重度EM与正常子宫内膜之间共筛出6个显著上调基因和67个显著下调基因。通过两种机器学习方法及验证组基因表达差异分析，筛选出8个候选特征基因（GSTT2、FOS、ADAT1、FKBP10、OLFM4、BPIFB1、VTCN1和MS4A8），其中仅ADAT1诊断价值较高，ROC曲线下面积（area under the curve, AUC）在实验组及验证组中分别为0.796 (95% CI：0.713~0.872)，0.93 (95% CI： 0.800~1.000)。免疫细胞浸润分析结果显示，与正常子宫内膜相比，中-重度EM中浆细胞及滤泡辅助性T细胞浸润程度显著升高。ADAT1与活化树突细胞、γδT细胞、CD4记忆活化的T细胞、嗜酸性粒细胞、中性粒细胞和幼稚的B细胞呈正相关，与浆细胞、CD8⁺T细胞、静息肥大细胞、调节性T细胞和单核细胞呈负相关。结论 ADAT1可能作为EM加重的特征基因，浆细胞及滤泡辅助性T细胞在EM进展中可能发挥重要作用。

关键词: 子宫内膜异位症, 生物信息学分析, 机器学习, 免疫浸润, ADAT1

Abstract: Objective To identify potential diagnostic markers for moderate or severe endometriosis（EM）.Methods Two publicly available gene expression profiles (GSE51981 and GSE7305 datasets) from human EM and control samples were downloaded from the GEO database. Differentially expressed genes (DEGs) were screened between 48 moderate or severe EM and 71 control samples. The Least absolute shrinkage and selection operator (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analysis were performed to identify candidate biomarkers. The area under the receiver operating characteristic (AUC) curve value was obtained and used to evaluate discriminatory ability. The expression level and diagnostic value of the biomarkers in EM were further validated in the GSE7305 dataset. The compositional patterns of the 22 types of immune cell fraction in EM were estimated based on the merged cohorts by using CIBERSORT. Results A total of 73 DEGs were identified between samples with moderate-to-severe EM and normal controls. These DEGs were significantly enriched in malignant tumors and immune-related pathways. Thirteen candidate gene biomarkers were further screened by using two machine learning methods, LASSO regression model and SVM-RFE analysis. Among them, the ADAT1 gene showed high diagnostic value for moderate-to-severe EM, which was validated in the validation dataset. Immune infiltration analysis showed that the levels of plasma cells and T cells follicular helper were significantly higher in moderate-to-severe EM than those in the normal group. The diagnostic marker gene ADAT1 was positively correlated with activated dendritic cells, T cells gamma delta, T cells CD4 memory activated, eosinophils, neutrophils, and B cells naive. In contrast, ADAT1 was negatively correlated with plasma cells, T cells CD8, T cells regulatory and monocytes.Conclusion ADAT1 may be a diagnostic biomarker for moderate-to-severe EM, providing new insights into the occurrence, progression, and molecular mechanisms of EM.

Key words: endometriosis, bioinformatics, machine learning, immune infiltration, ADAT1

中图分类号:

R737.33

姜艳, 赵轩宇, 隋峰. 中-重度子宫内膜异位症特征基因筛选[J]. 首都医科大学学报, 2024, 45(2): 322-332.

iang Yan, Zhao Xuanyu, Sui Feng. Identifying diagnostic gene biomarkers of moderate or severe endometriosis[J]. Journal of Capital Medical University, 2024, 45(2): 322-332.

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