Abstract
393 Background: The clinical use of molecular expression profiles could result in more accurate and objective diagnoses of cancers as well as prognoses of disease or response to the treatment. Unsupervised hierarchical clustering (UHC) analysis is a common method to profile the molecular expression of tissue microarray data. Methods: TM4: a free, open-source system for microarray data management and analysis was used in order to identify expression patterns of interest in our cohort of renal cell carcinomas (RCC) (n=80). We investigated 5 pathological predictors: (a) the histological type, (b) Fuhrman grade, (c) depth of infiltration, (d) metastasis in lymph node, (e) TNM stages, and 27 immunohistochemical molecular predictors involved in different pathways of tumor development and progression including: p53 and VHL tumor suppressor proteins; Bcl-2 and Survivin antiapoptotic proteins; Hif1-alpha and Notch proteins as a transcription factors; EGFR, PDGFR-α and VEGF proteins involved in tumor growth and proliferation; Glut proteins involved in tumor cell metabolism. Results: Unfavorable prognosis was significantly correlated with pathological predictors. Clear RCC histological subtype had a worse prognosis than the other ones studied to be accompanied by increased expression of Hif1-alpha and CAIX (p<0.001 in both cases) in patients with a number of metastatic nodes weighed (p<0.001). We found in Fuhrman Grade III samples a higher expression of VEGF (p=0.029). UHC analysis was done using average method with Spearman rank test to group the data with different predictor profiles. A preliminary analysis by hierarchical clustering of RCC produced three separate clusters groups. Conclusions: UHC based on a extended immunoprofile might be a useful, promising and powerful tool for further translational studies and should lead us to define a diagnostic and prognostic signature for RCC. Our laboratory is currently involved in this issue. No significant financial relationships to disclose.
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