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

Pan-cancer analyses attempt to discover similar features among multiple cancers to identify fundamental patterns common to cancer development and progression. A pan-cancer analysis integrating both protein expression and transcriptomic data is important because it can identify genes that are linked to proteins potentially responsible for a patient's status. This study aims to identify differentially expressed (DE) genes between early and advanced cases of multiple cancer types through the usage of RNA sequencing data. The relevance of these genes is further investigated by developing predictive models using K-nearest neighbor and linear discriminant analysis classifiers. The use of cancer-specific and non-cancer specific features resulted in several moderately performing models. Highlighted genes were further investigated to determine if they encoded for proteins identified in a previously conducted pan-cancer analysis. The results of this study suggest that a pan-cancer analysis may be highly complementary to standard analyses of individual cancers for identifying biologically relevant DE genes and can assist in developing effective predictive models for cancer progression.