Discovery of pathway-independent protein signatures associated with clinical outcome in human cancer cohorts.

ABSTRACT: Proteomic data provide a direct readout of protein function, thus constituting an information-rich resource for prognostic and predictive modeling. However, protein array data may not fully capture pathway activity due to the limited number of molecules and incomplete pathway coverage compared to other high-throughput technologies. For the present study, our aim was to improve clinical outcome prediction compared to published pathway-dependent prognostic signatures for The Cancer Genome Atlas (TCGA) cohorts using the least absolute shrinkage and selection operator (LASSO). RPPA data is particularly well-suited to the LASSO due to the relatively low number of predictors compared to larger genomic data matrices. Our approach selected predictors regardless of their pathway membership and opti

SUBMITTER: Konate MM 

PROVIDER: S-EPMC9652455 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature