Wang Hao, Zhang Zhaoyue, Li Haicheng, Li Jinzhao, Li Hanshuang, Liu Mingzhu, Liang Pengfei, Xi Qilemuge, Xing Yongqiang, Yang Lei, Zuo Yongchun
The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd., Hohhot, 010010, China.
Cell Biosci. 2023 Feb 28;13(1):41. doi: 10.1186/s13578-023-00991-y.
The placenta, as a unique exchange organ between mother and fetus, is essential for successful human pregnancy and fetal health. Preeclampsia (PE) caused by placental dysfunction contributes to both maternal and infant morbidity and mortality. Accurate identification of PE patients plays a vital role in the formulation of treatment plans. However, the traditional clinical methods of PE have a high misdiagnosis rate.
Here, we first designed a computational biology method that used single-cell transcriptome (scRNA-seq) of healthy pregnancy (38 wk) and early-onset PE (28-32 wk) to identify pathological cell subpopulations and predict PE risk. Based on machine learning methods and feature selection techniques, we observed that the Tuning ReliefF (TURF) score hybrid with XGBoost (TURF_XGB) achieved optimal performance, with 92.61% accuracy and 92.46% recall for classifying nine cell subpopulations of healthy placentas. Biological landscapes of placenta heterogeneity could be mapped by the 110 marker genes screened by TURF_XGB, which revealed the superiority of the TURF feature mining. Moreover, we processed the PE dataset with LASSO to obtain 497 biomarkers. Integration analysis of the above two gene sets revealed that dendritic cells were closely associated with early-onset PE, and C1QB and C1QC might drive preeclampsia by mediating inflammation. In addition, an ensemble model-based risk stratification card was developed to classify preeclampsia patients, and its area under the receiver operating characteristic curve (AUC) could reach 0.99. For broader accessibility, we designed an accessible online web server ( http://bioinfor.imu.edu.cn/placenta ).
Single-cell transcriptome-based preeclampsia risk assessment using an ensemble machine learning framework is a valuable asset for clinical decision-making. C1QB and C1QC may be involved in the development and progression of early-onset PE by affecting the complement and coagulation cascades pathway that mediate inflammation, which has important implications for better understanding the pathogenesis of PE.
胎盘作为母体与胎儿之间独特的交换器官，对人类成功妊娠和胎儿健康至关重要。由胎盘功能障碍引起的子痫前期（PE）会导致母婴发病和死亡。准确识别PE患者在治疗方案的制定中起着至关重要的作用。然而，传统的PE临床诊断方法误诊率较高。
在此，我们首先设计了一种计算生物学方法，利用健康妊娠（38周）和早发型PE（28 - 32周）的单细胞转录组（scRNA-seq）来识别病理细胞亚群并预测PE风险。基于机器学习方法和特征选择技术，我们发现与XGBoost混合的调整后的ReliefF（TURF）评分（TURF_XGB）实现了最佳性能，对健康胎盘的九个细胞亚群进行分类时，准确率为92.61%，召回率为92.46%。通过TURF_XGB筛选出的110个标记基因可以绘制胎盘异质性的生物学图谱，这揭示了TURF特征挖掘的优势。此外，我们用LASSO处理PE数据集以获得497个生物标志物。对上述两个基因集的整合分析表明，树突状细胞与早发型PE密切相关，C1QB和C1QC可能通过介导炎症来驱动子痫前期。此外，还开发了一种基于集成模型的风险分层卡来对子痫前期患者进行分类，其受试者操作特征曲线下面积（AUC）可达0.99。为了更广泛的可及性，我们设计了一个易于访问的在线网络服务器（http://bioinfor.imu.edu.cn/placenta）。
使用集成机器学习框架基于单细胞转录组的子痫前期风险评估是临床决策的宝贵资源。C1QB和C1QC可能通过影响介导炎症的补体和凝血级联途径参与早发型PE的发生和发展，这对于更好地理解PE的发病机制具有重要意义。
