Abstract / Summary
Early‑onset pre‑eclampsia (ePE) is a severe subtype of pre‑eclampsia (PE) commonly attributed to placental dysfunction. Although plasma proteins are advantageous for ePE prediction, prior studies with limited detection depth may overlook key low‑abundance protein candidates. Herein, we employed a novel deep 4D‑DIA‑MS platform of proteomics to identify novel plasma biomarkers of ePE. A proteomic cohort including 11 ePE patients and 11 healthy controls (HCs) matched for maternal and gestational ages were subjected to low-abundance plasma proteome quantification on a novel deep 4D-DIA-MS platform. Differentially expressed proteins were identified by comparisons between ePE and HCs, followed by functional enrichment analyses and protein-protein interaction. Two clinical cohorts were applied to verify the proteomic signatures and predictive performance of the identified candidates. The predictive models for ePE were established based on the novel biomarkers and internally validated. There were significant differences in the composition and functional profiles of plasma proteins between ePE patients and HCs. The upregulated plasma proteins in ePE were mainly enriched in biological processes including cell migration and angiogenesis, whereas the downregulated proteins were primarily involved in lipid metabolism, blood coagulation and complement activation. Two candidate biomarkers, CRH and ESM1, were significantly upregulated in plasma samples of ePE patients, which were also confirmed in clinical cohort 1 and 2. Individually, CRH and ESM1 yielded satisfactory predictive power for ePE. Combined predictive models integrating CRH, ESM1 and clinical information were developed and internally validated, with acceptable overall predictive efficacy for ePE. Candidate biomarkers CRH and ESM1 demonstrate promising predictive performance for ePE. Our constructed combined predictive model may benefit ePE prediction in the future. These findings provide novel molecular evidence and candidate biomarkers for the early prediction of ePE.