Development and validation of preeclampsia predictive models using key genes from bioinformatics and machine learning approaches - Li, Qian; Wei, Xiaowei; Wu, Fan; Qin, Chuanmei; Dong, Junpeng; Chen, Cailian; Lin, Yi (Frontiers in Immunology, 2024)
Background: Preeclampsia (PE) poses significant diagnostic and therapeutic challenges. This study aims to identify novel genes for potential diagnostic and therapeutic targets, illuminating the immune mechanisms involved. Methods: Three GEO datasets were analyzed, merging two for training set, and using the third for external validation. Intersection analysis of differentially expressed genes (DEGs) and WGCNA highlighted candidate genes. These were further refined through LASSO, SVM-RFE, and RF algorithms to identify diagnostic hub genes. Diagnostic efficacy was assessed using ROC curves. A predictive nomogram and fully Connected Neural Network (FCNN) were developed for PE prediction. ssGSEA and correlation analysis were employed to investigate the immune landscape. Further validation was provided by qRT-PCR on human placental samples. Result: Five biomarkers were identified with validation AUCs: CGB5 (AUC=0.663, 95%CI: 0.577-0.750), LEP (AUC=0.850, 95%CI: 0.792-0.908), LRRC1 (AUC=0.797, 95%CI: 0.728-0.867), PAPPA2 (AUC=0.839, 95%CI:0.775-0.902), and SLC20A1 (AUC=0.811, 95%CI: 0.742-0.880) also involved in crucial biological processes. The predictive nomogram exhibited significant capability (C-index 0.889), while FCNN achieved a best AUC of 0.927 (95% CI: 0.7782-1.000). Immune infiltration analysis underscored the critical role of T cell subsets, neutrophils, and natural killer cells in PE, with these hub genes closely linked to immune infiltration and the immunological mechanisms of PE's pathogenesis. Conclusion: CGB5, LEP, LRRC1, PAPPA2, and SLC20A1 are validated as key diagnostic biomarkers for PE. Nomogram and FCNN could credibly predict PE. Their association with immune infiltration underscores the crucial role of immune responses in PE pathogenesis.