To investigate the mechanism of action of propofol in early-onset preeclampsia (EOPE). Integrated transcriptomics and network approaches were employed to screen disease targets, drug targets, and differentially expressed genes from the GSE74341 training set (7 EOPE, 5 controls) and GSE44711 validation set (8 EOPE, 8 controls). Signature genes were further selected using machine learning algorithms (LASSO, SVM, Boruta). Immune infiltration levels were evaluated via ssGSEA and their correlations with signature genes were analyzed. Molecular docking validation of binding affinity was performed using CB-DOCK2. The expression patterns of signature genes in EOPE patients were ultimately confirmed using RT-qPCR in a clinical cohort of 36 EOPE patients and 36 healthy pregnant women. Nineteen potential intersection targets of propofol intervention in the EOPE were identified. Machine learning analysis further pinpointed three signature genes (CASP1, FLT1, and CD68). Expression validation and ROC curve analysis confirmed their significant dysregulation, with area under the curve (AUC) values of 1.000, 1.000, and 0.886 in the training set and 0.891, 0.844, and 0.875 in the validation set, respectively, indicating good diagnostic performance. Immunoinfiltration analysis revealed significant alterations in 12 immune cell subsets in EOPE samples, which were closely associated with 3 signature genes. Molecular docking showed negative binding energies (from -5.0 to -5.8 kcal/mol), suggesting that propofol can spontaneously bind to all core targets with weak‑to‑moderate affinity, and the binding energy with CD68 was the lowest (-5.8 kcal/mol). RT-qPCR validation in the clinical cohort confirmed that the expression patterns of the three signature genes were consistent with the bioinformatics predictions. This study identifies CASP1, FLT1, and CD68 as potential targets through which propofol may modulate EOPE-related pathways. Further mechanistic and in vivo validation studies are required.
山东省济南市章丘区文博路2号
齐鲁师范学院 genelibs生信实验室
山东省济南市高新区舜华路750号
大学科技园北区F座4单元2楼
电话: 0531-88819269