Classification and Regression Tree Model for the Differential Diagnosis of Preeclampsia Based on Clinicopathological Features and miR Signatures

Abstract:
Preeclampsia (PE) is a pregnancy complication
characterized by the onset of high blood pressure after 20 weeks of gestation
with proteinuria and abnormal liver enzymes. The early diagnosis and
prophylactic use of aspirin can reduce the long-term complications of PE. In
the current study, we utilized machine learning tools for the differential
diagnosis of EOPE and LOPE based on demographic, clinical, and biochemical
data. We employed SYBR green-based real-time PCR to study the differential
expression of hsa-miR-4743-5p, miR-149-5p, miR-331-5p, and miR-483-5p in both
forms of PE. A classification and regression tree (CART) model was developed to
differentiate between EOPE and LOPE. This was achieved by determining
thresholds of systolic blood pressure (SBP), Diastolic Blood Pressure (DBP), Mean
Arterial Pressure (MAP), Body Mass Index (BMI), urine protein, and SGOT. The
RT-PCR-based DEM profile identified an association of miR-4743-5p with both
forms of PE; miR-149-5p with EOPE, and miR-331-5p and miR-483-5p with LOPE.
MiRDip analysis revealed that genes targeted by these miRs influence TGF beta
signaling in EOPE; cholesterol and lipid homeostasis and NOTCH2 signaling in
LOPE. In conclusion, SBP, MAP, BMI, urine protein, DBP, and SGOT are key
determinants of EOPE and LOPE. The DEM profile clearly distinguished EOPE and
LOPE.
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