Common biochemical properties of metabolic genes recurrently dysregulated in tumors

Abstract

Background: Tumor initiation and progression are associated with numerous metabolic alterations. However, the biochemical drivers and constraints that contribute to metabolic gene dysregulation are unclear. Methods: Here, we present MetOncoFit, a computational model that integrates 142 metabolic features that can impact tumor fitness, including enzyme catalytic activity, pathway association, network topology, and reaction flux. MetOncoFit uses genome-scale metabolic modeling and machine-learning to quantify the relative importance of various metabolic features in predicting cancer metabolic gene expression, copy number variation, and survival data. Results: Using MetOncoFit, we performed a meta-analysis of 9 cancer types and over 4500 samples from TCGA, Prognoscan, and COSMIC tumor databases. MetOncoFit accurately predicted enzyme differential expression and its impact on patient survival using the 142 attributes of metabolic enzymes. Our analysis revealed that enzymes with high catalytic activity were frequently upregulated in many tumors and associated with poor survival. Topological analysis also identified specific metabolites that were hot spots of dysregulation. Conclusions: MetOncoFit integrates a broad range of datasets to understand how biochemical and topological features influence metabolic gene dysregulation across various cancer types. MetOncoFit was able to achieve significantly higher accuracy in predicting differential expression, copy number variation, and patient survival than traditional modeling approaches. Overall, MetOncoFit illuminates how enzyme activity and metabolic network architecture influences tumorigenesis.

Probing altered enzyme activity in the biochemical characterization of cancer

“…Interestingly, the kcat and differential expression connection was also useful in predicting survival, supportive of the idea of metabolic rewiring giving tumors an adaptive edge [136].…”

“…As systematic testing of the enzymatic activity of the myriad genetic alterations reported in TCGA is often not practical, there is a great desire to develop computational tools that predict driving versus passenger genetic alterations. For example, MetOncoFit uses metabolic modeling and machine learning to analyze TCGA data to integrate and predict the catalytic and network topological features driving the metabolic reprogramming that drives tumors [ 136 ]. Oruganty et al showed that k cat was the best predictor examined for changes in enzyme expression levels in all cancer types, with increased k cat correlating with metabolic enzyme gene up-regulation, primarily through increased copy number or increased gene expression, and decreased k cat correlating with down-regulation [ 136 ].…”

Comparative sera proteomics analysis of differentially expressed proteins in oral squamous cell carcinoma

“…The dysregulation of serine-type endopeptidase activity may lead to degradation of the extracellular matrix and imbalance of cellular homeostasis in cancers ( Poddar, Maurya & Saxena, 2017 ). Additionally, other functional processes such as binding, catalytic, and transportation are known to be associated with carcinogenesis ( Basten & Giles, 2013 ; Oruganty et al, 2020 ).…”

Nutrient Sensing by Histone Marks: Reading the Metabolic Histone Code Using Tracing, Omics, and Modeling

“…However, machine‐learning algorithms are data‐driven. Hybrid approaches that integrate machine‐learning and mechanistic modeling [ 82,83 ] can enable us to effectively harness large‐scale metabolic and epigenomic datasets in the future.…”