Pan-cancer analysis for studying cancer stage using protein expression data

Pan-cancer analyses attempt to discover similar features among multiple cancers in order to identify fundamental patterns common to cancer development and progression. Pan-cancer analysis at the level of protein expression is particularly important because protein expression is more immediately related to patient phenotype than genomic or transcriptomic data. This study aims to analyze differentially expressed (DE) proteins between early and advanced cases of multiple cancer types through the usage of reverse-phase protein array data. The relevance of these proteins is further investigated by developing predictive models using K-nearest neighbor and linear discriminant analysis classifiers. The results of this study suggest that a pan-cancer analysis may be highly complementary to standard analysis of an individual cancer for identifying biologically relevant DE proteins, and can assist in developing effective predictive models for cancer progression.