Machine learning-based investigation of the cancer protein secretory pathway
Journal article, 2021

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 learning algorithms were included in the analysis to enable comparison between methods and to focus on signals that were robust to algorithm type. The machine learning approach was validated by identifying PSP genes known to be regulated by p53, and even outperformed the differential expression analysis approach. Among the different analysis methods and cancer types, the kinesin family members KIF20A and KIF23 were consistently among the top genes associated with malignant transformation or tumor stage. However, unlike most cancer types which exhibited elevated KIF20A expression that remained relatively constant across tumor stages, renal carcinomas displayed a more gradual increase that continued with increasing disease severity. Collectively, our study demonstrates the complementary nature of a combined differential expression and machine learning approach for analyzing gene expression data, and highlights key PSP components relevant to features of tumor pathophysiology that may constitute potential therapeutic targets.

Author

Rasool Saghaleyni

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Muhammad Azam Sheikh

Chalmers, Computer Science and Engineering (Chalmers), CSE Verksamhetsstöd, Data Science Research Engineers

Pramod Bangalore

Greenbyte AB

Jens B Nielsen

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

BioInnovation Institute

Jonathan Robinson

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology, CSBI

PLoS Computational Biology

1553-734X (ISSN) 1553-7358 (eISSN)

Vol. 17 4 e1008898

Subject Categories

Medical Genetics

Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)

Cancer and Oncology

DOI

10.1371/journal.pcbi.1008898

PubMed

33819271

More information

Latest update

5/28/2021