Using Biologic Knowledge to Discover Molecular Correlations Between Human Renal Cell Carcinoma Pathways.

451 Background: Renal cell carcinoma (RCC) is known to be resistant to chemotherapy. There is need for the identification of biomarkers capable to determine RCC prognosis factors and metastatic potential obtainable from non-invasive or minimally invasive techniques. Our aim was to derive predictive models which could predict more accurately than any one factor alone. Methods: To studythe cascade of events leading to the formation and progression of RCC, we assessed 29 markers by immunohistochemistry and qRT-PCR using tissue micro-array (TMA). Results: Multivariate logistic regression showed the best proteins combination for node status (NOTCH1 and GLUT5) and pelvis invasion (EGFR and DLL3). ROC curve analyses were made to analyse the accuracy of the best candidate proteins; it should be noted NOTCH1 and GLUT5 for node status prediction (AUC=0.833, 95% CI, 0.744-0.922; p<0.001) and EGFR and DLL3 for pelvis invasion (AUC=0.777, 95% CI, 0.631-0.922; p=0.007). Furthermore, we carried out the correlation between these candidate proteins and all mRNA measured in order to deepen in the cellular transcripts traffic associated with them. To highlight the correlation between high DLL3 protein levels and low Hif1-β expression, and the negative correlation between GLUT5 protein and low levels of Baxβ. Conclusions: In the age of individual therapy, the approach to percutaneous image-guided RCC biopsy procedures plays an expanded role. Applying a 2 mm punch needle for constructing a TMA we could describe for the first time how are combined and correlated 29 markers in regression equations to predict in the most optimal way a number of pathological variables associated with RCC. [Table: see text]