Proteomic evaluation of urine from renal cell carcinoma using SELDI-TOF-MS and tree analysis pattern.

There is no useful marker in screening and early diagnosis for renal cell carcinomas (RCCs), especially in the urine. To screen for specific markers in the urine of RCCs patients, surface enhanced laser desorption and ionization time of flight mass spectrometry (SELDI-TOF-MS) was used and coupled with a tree analysis pattern to develop SELDI protein profiling of urine. Urine samples from 58 RCC patients, 45 healthy volunteers, and 56 patients with other urogenital diseases were analyzed using IMAC-Cu ProteinChip capable of specifically binding metal interesting proteins. Proteomic spectra were generated by mass spectrometry. Bioinformatic calculations were performed with Biomarker Wizard software 3.0 (Ciphergen). Four differentially expressed potential biomarkers from urine were identified with the relative molecular weights of 4020, 4637, 5070, and 5500. The discriminatory classifier with a panel of the four biomarkers determined in the training set could precisely detect 24 of 30 (sensitivity, 80.0%) RCC and 52 of 58 (specificity, 89.6%) non-RCC samples. Furthermore, a sensitivity of 67.8% (19/28) and a specificity of 81.4% (35/43) for the blinded test were obtained when comparing the RCC vs. non-RCC. So SELDI-TOF combined with a tree analysis pattern could potentially serve as a useful tool for diagnosis of RCC by urine samples.