Integrative data mining to identify novel candidate serum biomarkers for pre-eclampsia screening.

Objective: Pre-eclampsia (PE) is a serious complication that affects approximately 2% of pregnant women worldwide. At present, there is no sufficiently reliable test for early detection of PE in a screening setting that would allow timely intervention. To help future experimental identification of serum biomarkers for early onset PE, we applied a data mining approach to create a set of candidate biomarkers.

Methods: We started from the disease etiology, which involves impaired trophoblast invasion into the spiral arteries. On the basis of this, we used a three-stage filtering strategy consisting of selection of tissue-specific genes, textmining for further gene prioritization, and identifying blood-detectable markers.

Results: This approach resulted in 38 candidate biomarkers. These include the best three first-trimester serum biomarkers for PE found to date LGALS13 (placental protein 13, PP13), PAPPA (pregnancy-associated plasma protein-A, PAPP-A), and PGF (placental growth factor, PlGF), as well as five proteins previously identified as biomarker after the first-trimester or disease onset. This substantiates the effectiveness of our approach and provides an important indication that the list will contain several new biomarkers for PE.

Conclusions: We anticipate this list can serve in prioritization of future experimental studies on serum biomarkers for early onset PE.