Links between Serine Biosynthesis Pathway and Epigenetics in Cancer Metabolism

Pyun

2020

Cells

280

204

Aberrant metabolism is a major hallmark of cancer. Abnormal cancer metabolism, such as aerobic glycolysis and increased anabolic pathways, has important roles in tumorigenesis, metastasis, drug resistance, and cancer stem cells. Well-known oncogenic signaling pathways, such as phosphoinositide 3-kinase (PI3K)/AKT, Myc, and Hippo pathway, mediate metabolic gene expression and increase metabolic enzyme activities. Vice versa, deregulated metabolic pathways contribute to defects in cellular signal transduction pathways, which in turn provide energy, building blocks, and redox potentials for unrestrained cancer cell proliferation. Studies and clinical trials are being performed that focus on the inhibition of metabolic enzymes by small molecules or dietary interventions (e.g., fasting, calorie restriction, and intermittent fasting). Similar to genetic heterogeneity, the metabolic phenotypes of cancers are highly heterogeneous. This heterogeneity results from diverse cues in the tumor microenvironment and genetic mutations. Hence, overcoming metabolic plasticity is an important goal of modern cancer therapeutics. This review highlights recent findings on the metabolic phenotypes of cancer and elucidates the interactions between signal transduction pathways and metabolic pathways. We also provide novel rationales for designing the next-generation cancer metabolism drugs.

Section: Metabolic Characteristics Of Cancersmentioning

confidence: 99%

Cancer Metabolism: Phenotype, Signaling and Therapeutic Targets

Pyun

2020

Cells

280

204

“…Additionally, glycine can be converted to threonine. This pathway provides precursors for purine biosynthesis and the tricarboxylic acid (TCA) cycle [20]. The TCA cycle generates ATP for protein production.…”

Section: Discussionmentioning

confidence: 99%

Transcriptome Analysis of Breast Muscle Reveals Pathways Related to Protein Deposition in High Feed Efficiency of Native Turkeys

Pezeshkian

Mirhoseini

Ghovvati

et al. 2022

Animals

Feed efficiency is important due to the high cost of food, which accounts for about 70% of the total cost of a turkey breeding system. Native poultry are an important genetic resource in poultry breeding programs. This study aimed to conduct a global transcriptome analysis of native male turkeys which have been phenotyped for high and low feed efficiency. Feed efficiency traits were recorded during the experimental period. After slaughter, the three most efficient and three least efficient male turkeys were selected for RNA-Seq analysis. A total of 365 genes with different expressions in muscle tissue were identified between turkeys with a high feed efficiency compared to turkeys with a low feed efficiency. In the pathway analysis of up-regulated genes, major pathways included the “metabolism of glycine, serine, and threonine”; the “adipocytokine signaling pathway” and the “biosynthesis of amino acids”. In the pathway analysis of down-regulated genes, the major pathways included “dorso-ventral axis formation” and “actin cytoskeleton regulation”. In addition, gene set enrichment analyses were performed, which showed that high feed efficiency birds exhibit an increased expression of genes related to the biosynthesis of amino acids and low feed efficiency birds an increased expression of genes related to the immune response. Furthermore, functional analysis and protein network interaction analysis revealed that genes including GATM, PSAT1, PSPH, PHGDH, VCAM1, CD44, KRAS, SRC, CAV3, NEDD9, and PTPRQ were key genes for feed efficiency. These key genes may be good potential candidates for biomarkers of feed efficiency in genetic selection in turkeys.

“…The abnormal activity of the serine and glycine signaling pathways provides the main guarantee for satisfying the abundant nutrient requirements of tumor cells (34). In addition, the serine signaling pathway can provide biological precursors for many substances, such as proteins, nucleic acids, fatty acids, and cell membranes for tumor cell proliferation (35). Some studies have shown that abnormal methylation in tumor cells may affect the gene regulation process of this pathway (36).…”

Section: Discussionmentioning

confidence: 99%

Combined screening analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma

Cao¹,

Zhang²,

Zhang³

et al. 2022

J Gastrointest Oncol

Background: Methylation plays an important role in hepatocellular carcinoma (HCC) by altering the expression of key genes. The aim of this study was to screen the aberrantly methylated-differentially expressed genes (DEGs) in HCC and elucidate their underlying molecular mechanism.Methods: Gene expression microarrays (GSE101685) and gene methylation microarrays (GSE44909) were selected. DEGs and differentially methylated genes (DMGs) were screened. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the Database for Annotation, Visualization, and Integrated discovery (DAVID). The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to analyze the functional protein-protein interaction (PPI) network. Molecular Complex Detection (MCODE) analysis was performed using the Cytoscape software. Hub genes were verified in The Cancer Genome Atlas (TCGA) database.Results: A total of 80 hypomethylation-high expression genes (Hypo-HGs) were identified. Pathway enrichment analysis showed DNA replication, cell cycle, viral carcinogenesis, and the spliceosome. The top 5 hub genes were minichromosome maintenance complex component 3 (MCM3), checkpoint kinase 1 (CHEK1), kinesin family member 11 (KIF11), PDZ binding kinase (PBK), and Rac GTPase activating protein 1 (RACGAP1). In addition, 189 hypermethylation-low expression genes (Hyper-LGs) were identified. Pathway enrichment analysis indicated enrichment in metabolic pathways, drug metabolismother enzymes, and chemical carcinogenesis. The top 5 hub genes were leukocyte immunoglobulin like receptor B2 (LILRB2), formyl peptide receptor 1 (FPR1), S100 calcium binding protein A9 (S100A9), S100 calcium binding protein A8 (S100A8), and myeloid cell nuclear differentiation antigen (MNDA). The methylation status and mRNA expression of MCM3, CHEK1, KIF11, PBK, and S100A9 were consistent in the TCGA database and significantly correlated with the prognosis of patients.Conclusions: Combined screening of aberrantly methylated-DEGs based on bioinformatic analysis may provide new clues for elucidating the epigenetic mechanism in HCC. Hub genes, including MCM3, CHEK1, KIF11, PBK, and S100A9, may serve as biomarkers for the precise diagnosis of HCC.