EMBRYO QUALITY PREDICTIVE MODELS BASED ON CUMULUS CELLS GENE EXPRESSION
Devjak R, Burnik Papler T, Verdenik I, Fon Tacer K, Vrtačnik Bokal E
*Corresponding Author: Rok Devjak, M.D., Ph.D., Division of Medical Oncology, Institute of Oncology, Zaloška 2, 1000 Ljubljana, Slovenia. Tel: +386-1-5879-282; Fax: +386-1-5879-303. E-mail: rdevjak@onko-i.si
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RESULTS

The baseline characteristics between the two embryo groups (high quality vs. low quality) were not altered significantly by age, BMI and patients’ distribution tests in a binary logistic regression model (Table 1). The pregnancy rate for the observed group of patients was 0.53 and delivery rate was 0.47. Because RNA level from four CC samples of low quality embryos was to low, only 32 CC samples were analyzed, therefore, 58 CC samples were included in the predictive model construction. The Mann-Whitney U test showed significant difference between CC gene expression of oocytes resulting in high quality and low quality embryos only in AMHR2 (p = 0.030). The data were then split according to AMHR2 expression (high or low, where the median was the cut-off value) and genes were further tested in each group (Table 2). In the AMHR2 gene, high expression group LIF was shown to differ significantly between high quality and low quality embryos (p = 0.033). Therefore, AMHR2 and LIF were taken for the construction of the embryo quality outcome prediction model. Binary Logistic Regression Model. The AMHR2 and LIF CC expression values were used to construct three different binary logistic regression models for predicting a high quality embryo outcome. First, AMHR2 and LIF CC expression values were used in separate models, and their prediction values yielded an AUC of 0.69 ± 0.08 and 0.63 ± 0.08, respectively. Then, both genes CC expression values were combined into one model in which the prediction value yielded an AUC of 0.72 ± 0.08. Decision Tree Model. The same procedure was used in a data mining protocol for constructing three decision trees with AMHR2 and LIF CC expression values. A simple data discretization was used for node splitting in the decision tree where expression values were stratified into two equal frequency intervals (high and low CC expression values). The decision tree model was tested using 50.0% of the data for learning and 50.0% data for testing. Testing was then repeated 100 times; median AUC and standard deviation were calculated. First, AMHR2 and LIF CC expression values were used to construct separate decision tree models, and their prediction values yielded an AUC of 0.67 ± 0.01 and 0.57 ± 0.02, respectively. Combining both genes resulted in a decision tree (Figure 1) with an AUC of 0.73 ± 0.03.



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