Table3_Integrated transcriptomic analysis and machine learning for characterizing diagnostic biomarkers and immune cell infiltration in fetal growth restriction.xls

BackgroundFetal 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.MethodsFirstly, 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.ResultsBy 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).ConclusionFour 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.

背景：胎儿生长受限（FGR）是全球范围内妊娠的10%所面临的问题。胎盘功能障碍，作为FGR最常见的病因之一，与多种不良围产期结局相关。本研究的主要目标在于筛选FGR的潜在诊断生物标志物，并评估免疫细胞浸润在FGR进程中的作用。方法：首先，在两个基因表达综合数据库（GEO）数据集中识别差异表达基因（DEGs），并执行基因集富集分析。通过三种机器学习算法（最小绝对收缩和选择算子、随机森林和支持向量机模型）确定了与诊断相关的关键基因，并据此开发了列线图。利用接受者操作特征曲线、校准曲线和决策曲线分析曲线验证了诊断模型的有效性。通过基于估计RNA转录本相对子集的细胞类型识别（CIBERSORT），评估了FGR胎盘组织中免疫细胞浸润的特征，并筛选出FGR的候选关键免疫细胞。此外，本研究还验证了TREM1在现实世界中的诊断效能，并探索了TREM1与各种临床特征之间的关联。结果：通过三种机器学习算法选择的基因重叠，从290个DEGs中确定了四个关键基因，基于关键基因构建的诊断模型表现出良好的预测性能（AUC = 0.971）。免疫细胞浸润分析表明，多种免疫细胞可能参与FGR的发展，并筛选出九个FGR的候选关键免疫细胞。来自现实世界的数据进一步验证了TREM1作为有效的诊断生物标志物（AUC = 0.894），并且TREM1的表达与子宫动脉PI（UtA-PI）的增加相关（p值 = 0.029）。结论：确定了四个候选枢纽基因（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，而且与UtA-PI的增加相关。本研究的结果可能为FGR预测和治疗方面的未来研究提供一些新的线索。