A First-Trimester Serum Proteomic Signature for Early Predic-tion of Preeclampsia: Integrated Untargeted and Targeted Mass Spectrometry with Machine Learning

ABSTRACT: First-trimester prediction of preeclampsia (PE) remains a major clinical challenge, par-ticularly outside specialized fetal medicine centers. This study aimed to identify and validate serum protein biomarkers for early PE prediction using an integrated proteo-mic approach. A prospective cohort of 64 first-trimester singleton pregnancies (32 fu-ture PE cases, 32 matched controls) was analyzed. Untargeted proteomics was per-formed using DIA-PASEF-MS, followed by targeted validation with MRM-MS. Ma-chine learning classifiers (support vector machines, SVM, and random forest) were trained on differentially abundant proteins (FDR < 0.01, VIP > 1.5). DIA-MS identified 33 protein markers associated with complement activation, IGF transport regulation, and platelet degranulation. An SVM model with a linear kernel achieved 95% accuracy (AUC = 0.95, sensitivity = 95%, specificity = 97%). Four markers (AFM, AHSG, C8A, IGHG1) were validated across plat-forms, confirming the discovery findings. Cross-platform correlation was high: 71% of overlapping proteins showed r > 0.5 (p < 0.001), with the highest concordance observed for potential PE marker AHSG (r = 0.8, p < 0.001). PRSS1 correlated negatively with pro-teinuria (r = −0.74), and IGHV1-46 corre-lated positively with gestational age at delivery (r = 0.72), linking the proteomic signa-ture to clinical severity. Integrated DIA-MS and MRM-MS proteomics yields a repro-ducible, high-performance serum signature for first-trimester PE prediction. The iden-tified markers reflect core pathophysiological path-ways and offer potential to aug-ment current FMF-based screening algorithms.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Serum

SUBMITTER: Alexander Brzhozovskiy  

LAB HEAD: Alexey Kononikhin

PROVIDER: PXD080843 | Pride | 2026-09-14

REPOSITORIES: Pride