Large-Scale Proteomics Reveals New Candidate Biomarkers for Late-Onset Preeclampsia

Area of Science:

• Biochemistry and Molecular Biology
• Obstetrics and Gynecology
• Proteomics

Background:

• Preeclampsia presents as early-onset or late-onset forms.
• Placental growth factor (PlGF) and fms-like tyrosine kinase-1 (sFlt-1) are potential biomarkers but lack specificity.
• Identifying reliable biomarkers for late-onset preeclampsia is crucial for timely diagnosis and management.

Purpose of the Study:

• To identify and validate novel protein biomarkers for late-onset preeclampsia using a large-scale proteomic approach.
• To evaluate the diagnostic accuracy of proteomic models compared to existing biomarkers like sFlt-1 and PlGF.
• To investigate the temporal changes in protein profiles throughout gestation in relation to preeclampsia development.

Main Methods:

• Quantified approximately 7000 proteins in 673 samples from 89 late-onset preeclampsia patients and 91 controls across three gestational periods (T1, T2, T3).
• Employed elastic net and random forest models for prediction, with cross-validation for evaluation.
• Utilized differential abundance analysis and functional profiling to identify and interpret protein alterations.

Main Results:

• Observed a significant increase in protein differential abundance with advancing gestation, identifying 806 proteins at T3 associated with angiogenesis, cell adhesion, and extracellular matrix remodeling.
• Validated dysregulated proteins, including FAAH2, SIGLEC6, IL17RC, HTRA1, and sFlt-1, in an independent Norwegian dataset.
• Random forest models using 20 proteins achieved high accuracy at T3 (AUC 0.83), outperforming sFlt-1 and PlGF, and showed improved accuracy when combined with maternal characteristics (AUC 0.87).

Conclusions:

• Confirms the role of abnormal trophoblast invasion, angiogenesis, and extracellular matrix remodeling in late-onset preeclampsia.
• Highlights novel protein alterations consistently found across different patient cohorts.
• Demonstrates the potential of proteomics combined with clinical data for early and accurate diagnosis of late-onset preeclampsia.