New Method Enhanced Extraction of Protein Signatures of Renal Cell Carcinoma from Proteomics Data

In this study, we generated label-free data-independent
acquisition
(DIA)-based liquid chromatography (LC)-mass spectrometry (MS) proteomics
data from 261 renal cell carcinomas (RCC) and 195 normal adjacent
tissues (NAT). The RCC tumors included 48 nonclear cell renal cell
carcinomas (non-ccRCC) and 213 ccRCC. A total of 219,740 peptides
and 11,943 protein groups were identified, with 9,787 protein groups
per sample on average. We adopted a comprehensive approach to select
representative samples with different mutations, considering histopathological,
immune, methylation, and non-negative matrix factorization (NMF)-based
subtypes, along with clinical characteristics (gender, grade, and
stage) to capture the complexity and diversity of ccRCC tumors. We
identified a protein signature containing 55 proteins that distinguish
RCC tumors from NATs. Furthermore, a protein signature containing
39 proteins that differentiate different RCC tumor subtypes was also
identified. Our findings offer an extensive perspective of the proteomic
landscape in RCC, illuminating specific proteins that serve to distinguish
RCC tumors from NATs and among various RCC tumor subtypes.