Association of transcriptional levels of folate-mediated one-carbon metabolism-related genes in cancer cell lines with drug treatment response

Min, Dong-Joon; Vural, Suleyman; Krushkal, Julia

doi:10.1016/j.cancergen.2019.05.005

Cited by 16 publications

(7 citation statements)

References 100 publications

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“…SHMT2 simultaneously converts serine and THF to glycine and 5,10-CH 2 -THF (Figure ). SHMT2 seems to promote chemosensitivity or resistance in a context-specific manner. , Outside of nutrient contributions, SHMT2 knockout caused mitochondrial translation to stall at specific methylated tRNAs, and this was dependent on its enzymatic activity . Protein translation initiation requires formylmethionyl tRNAs, and when SHMT2 is knocked out, 10-CHO-THF, a downstream product, is no longer available for conjugation by MTFMT .…”

Section: Shmt2mentioning

confidence: 99%

A Review of Small-Molecule Inhibitors of One-Carbon Enzymes: SHMT2 and MTHFD2 in the Spotlight

Cuthbertson

Arabzada

Bankhead

et al. 2021

ACS Pharmacol. Transl. Sci.

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Metabolic reprogramming is a key hallmark of cancer and shifts cellular metabolism to meet the demands of biomass production necessary for abnormal cell reproduction. One-carbon metabolism (1CM) contributes to many biosynthetic pathways that fuel growth and is comprised of a complex network of enzymes. Methotrexate and 5-fluorouracil were pioneering drugs in this field and are still widely used today as anticancer agents as well as for other diseases such as arthritis. Besides dihydrofolate reductase and thymidylate synthase, two other enzymes of the folate cycle arm of 1CM have not been targeted clinically: serine hydroxymethyltransferase (SHMT) and methylenetetrahydrofolate dehydrogenase (MTHFD). An increasing body of literature suggests that the mitochondrial isoforms of these enzymes (SHMT2 and MTHFD2) are clinically relevant in the context of cancer. In this review, we focused on the 1CM pathway as a target for cancer therapy and, in particular, SHMT2 and MTHFD2. The function, regulation, and clinical relevance of SHMT2 and MTHFD2 are all discussed. We expand on previous clinical studies and evaluate the prognostic significance of these critical enzymes by performing a pan-cancer analysis of patient data from the The Cancer Genome Atlas and a transcriptional coexpression network enrichment analysis. We also provide an overview of preclinical and clinical inhibitors targeting the folate pathway, the methionine cycle, and folate-dependent purine biosynthesis enzymes.

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Section: Shmt2mentioning

confidence: 99%

A Review of Small-Molecule Inhibitors of One-Carbon Enzymes: SHMT2 and MTHFD2 in the Spotlight

Cuthbertson

Arabzada

Bankhead

et al. 2021

ACS Pharmacol. Transl. Sci.

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“…A research report has shown that the overall levels of body cholesterol and the stomach cancer risk are inversely related [ 10 ]. Previous studies have shown that genes associated with metabolism (including isoenzymes in particular pathways) have a greater mutation in patients with cancer and show variability between various forms of cancer [ 11 , 12 ]. However, so far, no systematic reports on the association between the molecular mechanism and aberrant lipid and glucose metabolism, prognosis, and treatment of glioma have been published.…”

Section: Introductionmentioning

confidence: 99%

Identification of Four Metabolic Subtypes of Glioma Based on Glycolysis-Cholesterol Synthesis Genes

Zhang

Xiao

Guo

et al. 2022

Computational and Mathematical Methods in Medicine

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Based on alterations in gene expression associated with the production of glycolysis and cholesterol, this research classified glioma into prognostic metabolic subgroups. In this study, data from the CGGA325 and The Cancer Genome Atlas (TCGA) datasets were utilized to extract single nucleotide variants (SNVs), RNA-seq expression data, copy number variation data, short insertions and deletions (InDel) mutation data, and clinical follow-up information from glioma patients. Glioma metabolic subtypes were classified using the ConsensusClusterPlus algorithm. This study determined four metabolic subgroups (glycolytic, cholesterogenic, quiescent, and mixed). Cholesterogenic patients had a higher survival chance. Genome-wide investigation revealed that inappropriate amplification of MYC and TERT was associated with improper cholesterol anabolic metabolism. In glioma metabolic subtypes, the mRNA levels of mitochondrial pyruvate carriers 1 and 2 (MPC1/2) presented deletion and amplification, respectively. Differentially upregulated genes in the glycolysis group were related to pathways, including IL-17, HIF-1, and TNF signaling pathways and carbon metabolism. Downregulated genes in the glycolysis group were enriched in terpenoid backbone biosynthesis, nitrogen metabolism, butanoate metabolism, and fatty acid metabolism pathway. Cox analysis of univariate and multivariate survival showed that risks of glycolysis subtypes were significantly higher than other subtypes. Those results were validated in the CGGA325 dataset. The current findings greatly contribute to a comprehensive understanding of glioma and personalized treatment.

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“…GART has been shown to be related to digestive cancer by mediating a metastatic cascade [ 76 ]. Elevated expression of GART, which is associated with chemosensitivity to multiple drugs, has been used as a target for anti-cancer drugs [ 77 – 79 ]. The depletion of GART can inhibit cell proliferation and blocked mitosis.…”

Section: Discussionmentioning

confidence: 99%

Identification of miRNAs and genes for predicting Barrett’s esophagus progressing to esophageal adenocarcinoma using miRNA-mRNA integrated analysis

Yao

Luo

et al. 2021

PLoS ONE

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Barrett’s esophagus (BE) is defined as any metaplastic columnar epithelium in the distal esophagus, which predisposes to esophageal adenocarcinoma (EAC). Yet, the mechanism through which BE develops to EAC still remain unclear. Moreover, the miRNA-mRNA regulatory network in distinguishing BE from EAC still remains poorly understood. To identify differentially expressed miRNAs (DEMs) and genes (DEGs) between EAC and BE from tissue samples, gene expression microarray datasets GSE13898, GSE26886, GSE1420 and miRNA microarray datasets GSE16456, GSE20099 were downloaded from Gene Expression Omnibus (GEO) database. GEO2R was used to screen the DEMs and DEGs. Pathway and functional enrichment analysis were performed by DAVID database. The protein–protein interaction (PPI) network was constructed by STRING and been visualized by Cytoscape software. Finnal, survival analysis was performed basing TCGA database. A total of 21 DEMs were identified. The enriched functions and pathways analysis inclued Epstein-Barr virus infection, herpesvirus infection and TRP channels. GART, TNFSF11, GTSE1, NEK2, ICAM1, PSMD12, CTNNB1, CDH1, PSEN1, IL1B, CTNND1, JAG1, CDH17, ITCH, CALM1 and ITGA6 were considered as the hub-genes. Hsa-miR-143 and hsa-miR-133b were the highest connectivity target gene. JAG1 was predicted as the largest number of target miRNAs. The expression of hsa-miR-181d, hsa-miR-185, hsa-miR-15b, hsa-miR-214 and hsa-miR-496 was significantly different between normal tissue and EAC. CDH1, GART, GTSE1, NEK2 and hsa-miR-496, hsa-miR-214, hsa-miR-15b were found to be correlated with survival.

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Association of transcriptional levels of folate-mediated one-carbon metabolism-related genes in cancer cell lines with drug treatment response

Cited by 16 publications

References 100 publications

A Review of Small-Molecule Inhibitors of One-Carbon Enzymes: SHMT2 and MTHFD2 in the Spotlight

A Review of Small-Molecule Inhibitors of One-Carbon Enzymes: SHMT2 and MTHFD2 in the Spotlight

Identification of Four Metabolic Subtypes of Glioma Based on Glycolysis-Cholesterol Synthesis Genes

Identification of miRNAs and genes for predicting Barrett’s esophagus progressing to esophageal adenocarcinoma using miRNA-mRNA integrated analysis

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