SYSTEMS-LEVEL CHARACTERIZATION OF METABOLIC REWIRING IN CANCER PROGRESSION

Abstract

This paper will explore the systems-level changes of metabolism that contribute to cancer progression with a combined transcriptomic and genomic characterization. The metabolic rewiring of cancer cells is far-reaching and is required to support unregulated proliferation, survival in harsh environments, and invasion. Nevertheless, the complicated interplay of the dysregulation of gene expression, the genomic instability and the changes in metabolic pathways has not been properly comprehended. In this paper, publicly available data sets were used in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to conduct an extensive multi-omics analysis on tumor and normal tissue samples. These studies employed RNA-seq to study differentially expressed genes and to characterize the genome through mutation profiling, single nucleotide polymorphism (SNP) analysis and copy number variation (CNV) analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to conduct the functional enrichment analysis to determine important changes in metabolic and signaling pathways. Moreover, a network analysis was also used to identify central hub genes that regulate metabolism during tumor progression using protein-protein interaction (PPI). The results showed a large-scale dysregulation of metabolic pathways related to glycolysis, oxidative phosphorylation, glutamine metabolism, lipid biosynthesis, PI3K-AKT, mTOR signaling, and hypoxia-related pathways. Oncogenic metabolic regulators (MYC, AKT1, HIF1A, KRAS, and mTOR) and tumor suppressor genes (TP53 and PTEN) had considerable transcriptional and genomic changes. Network analysis also established hub genes that are highly connected and coordinate proliferation, survival signaling, and metabolic adaptation in cancer cells. The systems-level framework offers more information about the molecular processes that underly cancer metabolic reprogramming and could be used to identify new biomarkers and therapeutic targets that can be used in precision oncology.