胎儿生长受限和子痫前期共有的糖基化相关分子机制和免疫浸润模式

摘要(中文译文)

胎盘功能障碍和免疫失调是胎儿生长受限（FGR）和子痫前期（PE）等疾病的核心环节。尽管异常胎盘糖基化已被认为与多种妊娠并发症相关，但与胎盘病理相关的糖基化相关基因（GRGs）的具体表达模式及其在驱动母胎界面免疫失衡中的潜在作用仍知之甚少。首先，基于两个GEO数据集（GSE114691和GSE203507）进行的差异表达基因（DEG）鉴定和基因集富集分析揭示了疾病相关通路。加权基因共表达网络分析（WGCNA）进一步鉴定了与疾病状态显著相关的糖基化相关基因模块。采用三种机器学习算法（LASSO、随机森林和支持向量机）筛选诊断关键基因，并构建了预测列线图。使用受试者工作特征曲线、校准曲线和决策曲线分析评估诊断模型的效能。采用CIBERSORT评估FGR、PE和PE+FGR胎盘及蜕膜中的免疫细胞浸润特征。此外，单细胞RNA测序验证了关键诊断基因在不同滋养细胞亚群中的表达模式，并使用CellChat分析了细胞间通讯网络。此外，本研究在真实世界样本中验证了关键基因的诊断效能。经批次校正和两个数据集整合后，共分析104份样本，包括对照组（n=26）、PE组（n=28）、FGR组（n=23）和PE+FGR组（n=27）。糖基化相关通路在疾病组中富集。WGCNA模块与糖基化基因取交集获得97个重叠基因，通过三种机器学习算法从中鉴定出五个核心基因。基于这些基因的诊断模型在各疾病组中显示出良好的预测性能。免疫浸润分析提示多种免疫细胞类型参与疾病发病机制。真实世界验证显示B3GNT2和ST6GAL1的表达在正常组与PE/FGR组之间存在显著差异。鉴定出五个候选枢纽基因（B3GNT2、ST6GAL1、GALNT11、GCNT4和LARGE2），并构建了用于FGR、PE和PE+FGR诊断的列线图。蛋白水平验证证实B3GNT2和ST6GAL1在FGR和PE患者胎盘组织中显著下调；GALNT11、GCNT4和LARGE2的mRNA水平验证在独立临床样本中提供。这些枢纽基因在胎盘和蜕膜中均与关键免疫细胞类型相关。

Abstract

Placental dysfunction and immune dysregulation are central to disorders such as fetal growth restriction (FGR) and preeclampsia (PE). Although aberrant placental glycosylation has been implicated in various pregnancy complications, the specific expression patterns of glycosylation-related genes (GRGs) associated with placental pathology and their potential role in driving immune imbalance at the maternal-fetal interface remain poorly understood. First, differentially expressed gene (DEG) identification and gene set enrichment analysis based on two GEO datasets (GSE114691 and GSE203507) revealed disease-associated pathways. Weighted gene co-expression network analysis (WGCNA) further identified glycosylation-related gene modules significantly associated with disease status. Three machine learning algorithms (LASSO, random forest, and support vector machine) were employed to screen diagnostic key genes, and a predictive nomogram was constructed. The efficacy of the diagnostic model was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis. CIBERSORT was used to assess immune cell infiltration characteristics in the placenta and decidua of FGR, PE, and PE+FGR. Furthermore, single-cell RNA sequencing validated the expression patterns of key diagnostic genes across different trophoblast subsets, and cell-cell communication networks were analyzed using CellChat. In addition, this study validated the diagnostic efficacy of key genes in real-world samples. Following batch correction and integration of two datasets, 104 samples were analyzed, including controls (n=26), PE (n=28), FGR (n=23), and PE+FGR (n=27). Glycosylation-related pathways were enriched in disease groups. Intersection of WGCNA modules with glycosylation genes yielded 97 overlapping genes, from which five core genes were identified using three machine learning algorithms. A diagnostic model based on these genes showed good predictive performance across disease groups. Immune infiltration analysis implicated multiple immune cell types in disease pathogenesis. Real-world validation of expression of B3GNT2 and ST6GAL1 differed significantly between normal and PE/FGR groups. Five candidate hub genes (B3GNT2, ST6GAL1, GALNT11, GCNT4, and LARGE2) were identified and a nomogram was constructed for FGR, PE, and PE+FGR diagnosis. Protein-level validation confirmed significant downregulation of B3GNT2 and ST6GAL1 in placental tissues from FGR and PE patients; mRNA-level validation of GALNT11, GCNT4, and LARGE2 is provided in independent clinical samples. These hub genes were associated with key immune cell types at both the placenta and decidua.

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