Abstract
Clear cell renal cell carcinoma is the most prevalent of all reported kidney cancer cases, and currently there are no markers for early diagnosis. This has stimulated great research interest recently because early detection of the disease can significantly improve the low survival rate. Combining the proteome, glycoproteome, and N-glycome data from clear cell renal cell carcinoma plasma has the potential of identifying candidate markers for early diagnosis and prognosis and/or to monitor disease recurrence. Here, we report on the utilization of a multi-dimensional fractionation approach (12P-M-LAC) and LC-MS/MS to comprehensively investigate clear cell renal cell carcinoma plasma collected before (disease) and after (non-disease) curative nephrectomy (n = 40). Proteins detected in the subproteomes were investigated via label-free quantification. Protein abundance analysis revealed a number of low-level proteins with significant differential expression levels in disease samples, including HSPG2, CD146, ECM1, SELL, SYNE1, and VCAM1. Importantly, we observed a strong correlation between differentially expressed proteins and clinical status of the patient. Investigation of the glycoproteome returned 13 candidate glycoproteins with significant differential M-LAC column binding. Qualitative analysis indicated that 62% of selected candidate glycoproteins showed higher levels (upregulation) in M-LAC bound fraction of disease samples. This observation was further confirmed by released N-glycans data in which 53% of identified N-glycans were present at different levels in plasma in the disease vs non-disease samples. This striking result demonstrates the potential for significant protein glycosylation alterations in clear cell renal cell carcinoma cancer plasma. With future validation in a larger cohort, information derived from this study may lead to the development of clear cell renal cell carcinoma candidate biomarkers.