Department of Fetal Medicine & Prenatal Diagnosis Center, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.
Department of Gynecology and Obstetrics, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Front Immunol. 2024 Sep 4;15:1381795. doi: 10.3389/fimmu.2024.1381795. eCollection 2024.
Fetal growth restriction (FGR) occurs in 10% of pregnancies worldwide. Placenta dysfunction, as one of the most common causes of FGR, is associated with various poor perinatal outcomes. The main objectives of this study were to screen potential diagnostic biomarkers for FGR and to evaluate the function of immune cell infiltration in the process of FGR.
Firstly, differential expression genes (DEGs) were identified in two Gene Expression Omnibus (GEO) datasets, and gene set enrichment analysis was performed. Diagnosis-related key genes were identified by using three machine learning algorithms (least absolute shrinkage and selection operator, random forest, and support vector machine model), and the nomogram was then developed. The receiver operating characteristic curve, calibration curve, and decision curve analysis curve were used to verify the validity of the diagnostic model. Using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), the characteristics of immune cell infiltration in placental tissue of FGR were evaluated and the candidate key immune cells of FGR were screened. In addition, this study also validated the diagnostic efficacy of TREM1 in the real world and explored associations between TREM1 and various clinical features.
By overlapping the genes selected by three machine learning algorithms, four key genes were identified from 290 DEGs, and the diagnostic model based on the key genes showed good predictive performance (AUC = 0.971). The analysis of immune cell infiltration indicated that a variety of immune cells may be involved in the development of FGR, and nine candidate key immune cells of FGR were screened. Results from real-world data further validated TREM1 as an effective diagnostic biomarker (AUC = 0.894) and TREM1 expression was associated with increased uterine artery PI (UtA-PI) (p-value = 0.029).
Four candidate hub genes (SCD, SPINK1, TREM1, and HIST1H2BB) were identified, and the nomogram was constructed for FGR diagnosis. TREM1 was not only associated with a variety of key immune cells but also correlated with increased UtA-PI. The results of this study could provide some new clues for future research on the prediction and treatment of FGR.
胎儿生长受限（FGR）在全球 10%的妊娠中发生。胎盘功能障碍是 FGR 最常见的原因之一，与各种不良围产结局相关。本研究的主要目的是筛选 FGR 的潜在诊断生物标志物，并评估免疫细胞浸润在 FGR 过程中的功能。
首先，在两个基因表达综合（GEO）数据集识别差异表达基因（DEGs），并进行基因集富集分析。使用三种机器学习算法（最小绝对收缩和选择算子、随机森林和支持向量机模型）鉴定与诊断相关的关键基因，并建立列线图。使用受试者工作特征曲线、校准曲线和决策曲线分析曲线来验证诊断模型的有效性。使用估计相对 RNA 转录物子集的细胞类型鉴定（CIBERSORT）评估 FGR 胎盘组织中免疫细胞浸润的特征，并筛选 FGR 的候选关键免疫细胞。此外，本研究还验证了 TREM1 在真实世界中的诊断效果，并探讨了 TREM1 与各种临床特征之间的关系。
通过重叠三种机器学习算法选择的基因，从 290 个 DEGs 中鉴定出 4 个关键基因，基于关键基因的诊断模型具有良好的预测性能（AUC = 0.971）。免疫细胞浸润分析表明，多种免疫细胞可能参与 FGR 的发生，筛选出 9 个 FGR 的候选关键免疫细胞。真实世界数据的结果进一步验证了 TREM1 作为一种有效的诊断生物标志物（AUC = 0.894），且 TREM1 的表达与增加的子宫动脉搏动指数（UtA-PI）相关（p 值 = 0.029）。
鉴定出 4 个候选关键基因（SCD、SPINK1、TREM1 和 HIST1H2BB），并构建了 FGR 诊断的列线图。TREM1 不仅与多种关键免疫细胞相关，还与增加的 UtA-PI 相关。本研究结果可为 FGR 的预测和治疗提供新的线索。
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