Machine learning-based investigation of the cancer protein secretory pathway.

ABSTRACT: Deregulation of the protein secretory pathway (PSP) is linked to many hallmarks of cancer, such as promoting tissue invasion and modulating cell-cell signaling. The collection of secreted proteins processed by the PSP, known as the secretome, is often studied due to its potential as a reservoir of tumor biomarkers. However, there has been less focus on the protein components of the secretory machinery itself. We therefore investigated the expression changes in secretory pathway components across many different cancer types. Specifically, we implemented a dual approach involving differential expression analysis and machine learning to identify PSP genes whose expression was associated with key tumor characteristics: mutation of p53, cancer status, and tumor stage. Eight different machine le

SUBMITTER: Saghaleyni R 

PROVIDER: S-EPMC8049480 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature