Novel tissue markers in renal neoplasms using molar-scale quantitative proteomics via the total protein approach normalised with protein deglycase DJ-1 (PARK7).

Mass spectrometry-based proteomics enables large-scale protein quantification but is hindered by inter- and intra-laboratory variability, complicating data integration and biomarker discovery. This study aims to develop an optimised normalisation strategy using the ubiquitously expressed protein deglycase DJ-1 (PARK7) as an internal standard, combined with the Total Protein Approach (TPA) to improve data comparability in renal neoplasms proteomic datasets. We analysed the MS-based proteomics data of renal tissues from clear cell renal cell carcinoma (ccRCC, n = 7), papillary renal cell carcinoma (pRCC, n = 5), chromophobe renal cell carcinoma (chRCC, n = 5), renal oncocytoma (RO, n = 5), and control normal adjacent tissue (NAT, n = 5). Protein concentrations were calculated using the Total Protein Approach, and the data were normalised to PARK7 expression using a TPA reference value of 34.1 pmol/mg. TPA-PARK7 normalisation showed a trend towards reducing interquartile ranges. After normalisation, 95 % of biomarkers from non-normalised datasets remained statistically significant. Among these, 31 % of previously proposed candidate biomarkers retained their ability to distinguish between conditions, with histologically validated biomarkers (TUBB3, LAMP1, and HK1) showing improved differentiation. Additionally, 322 new statistically significant proteins were identified, and 18 new potential biomarkers for renal neoplasm were detected exclusively after TPA-PARK7 normalisation. Our findings demonstrate that using PARK7 as an internal standard, combined with the TPA, significantly enhances the statistical robustness and reliability of protein quantification in mass spectrometry-based proteomics. This normalisation strategy reduces interlaboratory variability, preserves biomarker differentiation capability, and enables novel biomarker discovery. By reducing variability, this method enhances cross-study comparability and supports the advancement of clinically relevant biomarker discovery.