Network-based metabolic characterization of renal cell carcinoma

Abstract

An emerging hallmark of cancer is metabolic reprogramming, which presents opportunities for cancer diagnosis and treatment based on metabolism. We performed a comprehensive metabolic network analysis of major renal cell carcinoma (Rcc) subtypes including clear cell, papillary and chromophobe by integrating transcriptomic data with the human genome-scale metabolic model to understand the coordination of metabolic pathways in cancer cells. We identified metabolic alterations of each subtype with respect to tumor-adjacent normal samples and compared them to understand the differences between subtypes. We found that genes of amino acid metabolism and redox homeostasis are significantly altered in RCC subtypes. Chromophobe showed metabolic divergence compared to other subtypes with upregulation of genes involved in glutamine anaplerosis and aspartate biosynthesis. A difference in transcriptional regulation involving HIF1A is observed between subtypes. We identified E2F1 and FOXM1 as other major transcriptional activators of metabolic genes in RCC. Further, the co-expression pattern of metabolic genes in each patient showed the variations in metabolism within RCC subtypes. We also found that co-expression modules of each subtype have tumor stage-specific behavior, which may have clinical implications. Major biological processes namely reproduction, development, wound healing and tissue regeneration require cell proliferation. Cells proliferate in response to growth-promoting stimulus however, under adverse conditions they move into a reversible, non-proliferating state termed quiescence. Cells gauge the strength of proliferative and anti-proliferative signals through multiple molecular players to make cellular decisions. Cancer is a proliferative disease that arises when the regulatory control of quiescence-proliferation reversible transition is lost. An emerging hallmark of cancer is metabolic reprogramming, which helps to meet the energy demand for cell growth and division. Initial studies by Otto Warburg pointed to aerobic glycolysis, however recent advances have started to reveal other metabolic alterations and plasticity of cancer metabolism 1,2. Understanding the differences in metabolism between normal and cancer cells can shed light on the adaptations that promote disease progression and may also facilitate the identification of therapeutic metabolic targets. Mutations or epigenetic alterations in cancer can influence the expression of metabolic genes. Studies have explored transcriptome data of different cancers to understand the transcriptional dysregulation of metabolic genes. These studies are based on data generated by The Cancer Genome Atlas (TCGA) program. A pan-cancer analysis of different cancer types found a convergent metabolic landscape with upregulated nucleotide synthesis and downregulated mitochondrial metabolism as the main features 3. Rosario et al. 4 analyzed the gene expression of metabolic pathways in Kyoto Encyclopedia of Genes and Genomes (KEGG) and found that pentose and glucuronate inter...

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