Molecular expression profiling and pathway analysis of formalin-fixed paraffin-embedded primary renal tumor specimens

448 Background: Many studies have demonstrated genetic and environmental factors lead to renal cell carcinoma (RCC) occurring during a protracted period of tumorigenesis. It seemed suitable identify and characterize potential molecular markers which might provide rapid and effective possibilities for early detection of RCC. The purpose from this analysis was to derive predictive models which could predict more accurately than any one factor alone. Methods: We assessed using quantitative real-time PCR (qPCR) with SYBR Green the profile of 32 predictive markers involved in the cascade of events leading to the formation and progression of this disease to evaluate their involvement in oncogenesis. RNA of quality was obtained in 90% of samples (N=80, 52/80 clear RCC, 6/80 papillary RCC and 14/80 chromophobe RCC) to carry out the study of gene expression. GenEx software was used for qPCR data processing and analysis. The potential correlation between mRNA expression and the pathological features of the study subjects was assessed by Pearson Chi-squared. Linear rather than logistic regression models were used (SPSS statistics 21.0 software). Additionally, a knowledgebases of biological pathways, Reactome and Snow (Babelomics), were used to superimpose our quantitative expression data. Results: The best gene predictors related to different pathological variables (histological type, tumor size, Fuhrman grade, number of involved nodes, renal pelvis invasion, lymphatic vessels invasion, and rupture of the renal capsule): Hif1-α (p < 0.001), Hif1-β (p = 0.035), DLL1 (p = 0 .012), DLL3 (p< 0.001), c-Kit (p = 0.002), PDGFR-β (p =0.002), PDGFR-α (p = 0.026), Bax-β (p = 0.005), Survivin (p = 0.028), Notch1 (p = 0.006), Notch2 (p = 0.007), Notch3 (p = 0.016), Notch4 (p = 0.005), EGFR (p = 0.029), Glut3 (p = 0.026), Glut5 (p = 0.036), FH (p = 0.018), CA9 (p = 0.030), and VHL-1(p = 0.015). Conclusions: Our data suggested that altered expression of certain members involved in the main pathways which feed RCC and their downstream targets analyzed together could improve the diagnostic accuracy for renal cancer by combining determinations of mRNA markers.