Background: Energy metabolism reprogramming (EMR) exerts a critical role in tumor progression and activation of tumor-associated immune cells. Understanding the structural changes upon metabolic networks of both cancer cells and immune settings is vital for the potential and effective incorporation of metabolism-targeted therapeutics clinically.
Methods: In the present study, we used 33 tumor types’ transcriptome data from TCGA & UCSC and proteomic data from CPTAC to elaborate the EMR under the “Warburg Effect” in cancer. We assessed the role of metabolic enzymes in prognosis by redrawing the metabolic network and proportional hazards model (Cox) analysis. Based on machine learning, we identified determinants of tumor immune subtypes and used a scoring scheme for the correlation between immune cell infiltration and metabolic enzymes. Considering the immunophenotype relationship, we illustrated a novel bioinformatics horizontal alignment method.
Results: Systematic profiling of EMR would shed light on the common and divergent metabolic characteristics between tumor cells as well as the tumor-associated microenvironment, and whether the metabolic characteristics of these cells remain stable or change in course of tumor progression, indicating metabolic plasticity.
Conclusions: This article reviewed the recent understanding of metabolic changes in tumor progression and tumor-associated microenvironment’ phenotype and function, which could help clinical doctors to understand EMR in tumor progression and treatment resistance.