Introduction. Renal Cancer (RC) represents 3% of all human cancer and up to now no blood or urine test is available for its diagnosis. We applied complementary proteomic analysis of tissue and biological fluids of RC patients to identify a set of tissue-derived RC specific biomarkers released in biological fluids of RC patients.

Methods. Thirty-three early stage Renal Cancer (RC) patients and 34 matched healthy subjects (HS) were enrolled. Tissue specimens, urine and serum samples were collected from each patient. Ten micrograms of urine proteins were analyzed by CM10 ProteinChip array and PCS-400 Protein-Chip Reader (Bio-Rad). The mass spectra were managed by ProteinChip DataManager and Biomarker Pattern software (BPS®) for non supervised (clustering) and supervised statistical (Classification and Regression Tree -CART) analysis. One among the best predictors identified by BPS analysis was further purified by 2D-PAGE and sequenced by MALDI-TOF-TOF/MS.

Results. Twenty-seven mass peaks were differently expressed in tissue and urine samples of RC pts. CART analysis allowed to build up a Classification Tree that, measuring the abundance of three predictors (8956 and 6873 and 3438 m/z), was able to correctly classify, in the independent testing set, 100% RC and 75% HS patients reaching a diagnostic power of 88%. Of note, urine classification model was unable to identify RC patients when applied to serum samples. One over 3 RC putative biomarkers was purified and identified by Mass Spectrometry.

Conclusions. Proteomic screening of RC urine and tissue allowed to classify RC and HS pts with 88% accuracy. Among differently excreted RC proteins, 3 mass peaks (8956 and 6873 and 3438 m/z ) were able to correctly identify an independent cohort of RC pts. One of the putative biomarkers (8956 m/z) was identified by Mass Spectrometry. The identification of the other markers and their validation in urine of a larger cohort of RC patients will disclose their potential role for RCC non invasive diagnosis.