Understanding of Metabolic Protein Expression Changes in Patient Cancer Datasets

Music City Center 

Metabolic alterations in cancer cells are a fundamental characteristic of tumorigenesis. However, limited research has been performed to identify metabolic expression adaptation signatures at the protein level in cancer datasets. In this study, metabolic gene expression datasets from the National Cancer Institute (NCI) Proteomic Data Commons were analyzed to evaluate metabolic protein abundance changes across cancers. In addition, sub-system level metabolic pathway alterations and how they correlate with cancer progression were investigated. Patient metadata, including cancer subtypes, pathological stage, and race/ethnicity, were used to identify features of metabolic protein adaptations driving classifications across tumor subtypes, during cancer progression, and across different patient populations. Gene set enrichment analysis (GSEA) and machine learning approaches were applied to examine protein alterations associated with sub-system metabolic pathways. Understanding metabolic gene expression changes in cancer as the result of metabolic adaptations will enhance our knowledge of cancer biology and highlight functionally important metabolic processes.

Metabolic Adaptations

Cancer Metabolism

Proteomic Data Analysis

GSEA

Machine Learning 

Section on Statistics and Data Science Education