Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas

Abstract

Understanding metabolic dysregulation in different disease settings is vital for the safe and effective incorporation of metabolism-targeted therapeutics in the clinic. Here, using transcriptomic data for 10,704 tumor and normal samples from The Cancer Genome Atlas, across 26 disease sites, we present a novel bioinformatics pipeline that distinguishes tumor from normal tissues, based on differential gene expression for 114 metabolic pathways. We confirm pathway dysregulation in separate patient populations, demonstrating the robustness of our approach. Bootstrapping simulations were then applied to assess the biological significance of these alterations. We provide distinct examples of the types of analysis that can be accomplished with this tool to understand cancer specific metabolic dysregulation, highlighting novel pathways of interest, and patterns of metabolic flux, in both common and rare disease sites. Further, we show that Master Metabolic Transcriptional Regulators explain why metabolic differences exist, can segregate patient populations, and predict responders to different metabolism-targeted therapeutics.

Metabolism‐associated molecular classification of hepatocellular carcinoma

“…For example, it has been reported that metformin can prevent liver carcinogenesis (Shankaraiah et al , ) and treatment with metformin is associated with favorable prognosis in patients with HCC (Schulte et al , ). Determining the responders of metabolic therapies has proven to be challenging (Rosario et al , ). This study provided insights into predicting potential responders toward metabolic therapies.…”

“…Gene set variation analysis (GSVA) is a nonparametric and unsupervised gene set enrichment method that can estimate the score of certain pathway or signature based on transcriptomic data (Hanzelmann et al , ). The 115 metabolism‐relevant gene signatures and seven HCC progression‐relevant signatures were achieved from previously published studies (Desert et al , ; Rosario et al , ), and by using gsva r package, each sample received 120 scores corresponding to 115 metabolism signatures and seven progression‐relevant signatures. Subsequently, differential analysis was conducted based on the 113 metabolism scores using limma package in r software, and the signatures with an absolute log2 fold change (FC) > 0.2 (adjusted P < 0.05) were defined as differentially expressed signatures.…”

Emerging roles for UDP-glucuronosyltransferases in drug resistance and cancer progression

“…17,29,31,33,50,[53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69] These perturbed levels of UGTs are consistent with altered metabolic functions in tumours and suggest that UGTs might influence cancer progression, independent of exposure to therapeutic drugs (Table 1). Notably, in recent reports investigating metabolic perturbations present in the transcriptome and metabolome of multiple tumour types, 70,71 the genes and metabolites for which levels are most perturbed belong to the pentose and glucuronate interconversion pathway that includes all UGT genes. These observations support the concept of a perturbed UGT pathway in several cancers.…”

Targeting Metabolic Deregulation Landscapes in Breast Cancer Subtypes

“…Previous reports have also analyzed the relationship between transcriptional deregulation and metabolic changes in cancer (15,16). From these studies, some commonalities and differences arise.…”

“…However, despite important advances in experimental-omic techniques, comprehensive metabolomic mapping and fluxomics are still under-developed for the task of describing cellular metabolic processes comprehensively, although this should change in the upcoming years. Approaches to analyzing metabolic deregulation in cancer based on gene expression have been developed (15,16). Those extensive studies used differentially expressed genes for more than 20 types of cancer to distinguish deregulated metabolic pathways.…”