A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery

Hao Wang; Zhaoyue Zhang; Haicheng Li; Jinzhao Li; Hanshuang Li; Mingzhu Liu; Pengfei Liang; Qilemuge Xi; Yongqiang Xing; Lei Yang; Yongchun Zuo

doi:10.1186/s13578-023-00991-y

A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery

Cell Biosci. 2023 Feb 28;13(1):41. doi: 10.1186/s13578-023-00991-y.

Authors

Hao Wang^#^{1

2}, Zhaoyue Zhang^#³, Haicheng Li^#^{1

2}, Jinzhao Li¹, Hanshuang Li¹, Mingzhu Liu^{1

2}, Pengfei Liang¹, Qilemuge Xi¹, Yongqiang Xing⁴, Lei Yang⁵, Yongchun Zuo^{6

7}

Affiliations

¹ The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
² Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd., Hohhot, 010010, China.
³ School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
⁴ School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, 014010, China. xingyongqiang1984@163.com.
⁵ College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China. leiyang@hrbmu.edu.cn.
⁶ The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China. yczuo@imu.edu.cn.
⁷ Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd., Hohhot, 010010, China. yczuo@imu.edu.cn.

^# Contributed equally.

Abstract

Background: 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.

Results: 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 ).

Conclusion: 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.

Keywords: Feature selection; Machine learning; Marker genes; Preeclampsia risk; Web server.

Abstract

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