Genes involved with folate uptake and distribution and their association with colorectal cancer risk

Folate status is an important predictor of colorectal cancer risk. Common genetic variants in genes involved in regulating cellular folate levels might also predict risk, but there are limited data on this issue. We conducted a family-based case-control association study of variants in four genes involved in folate uptake and distribution: FOLR1, FPGS, GGH and SLC19A1, using 1,750 population-based and 245 clinic-based cases of pathologically confirmed colorectal cancer and their unaffected relatives participating in the Colon Cancer Family Registries. Standardized questionnaires, administered to all participants, collected information on risk factors and diet. Standard molecular techniques were used to determine microsatellite instability (MSI) status on cases. tagSNPs (n = 29) were selected based on coverage as assessed by pairwise r2. We found no evidence that tagSNPs in these genes were associated with risk of colorectal cancer. For the SLC19A1-rs1051266 (G80A, Arg27His) missense polymorphism, the A/A genotype was not associated with risk of colorectal cancer using population-based (OR = 1.00; 95% CI = 0.81-1.23) or clinic-based (OR = 0.75; 95% CI = 0.44-1.29) families compared to the G/A and G/G genotypes. We found no evidence that the association between any tagSNP and CRC risk was modified by multivitamin use, folic acid use and dietary folate intake and total folate intake. The odds ratios were similar, irrespective of MSI status, tumor subsite and family history of colorectal cancer. In conclusion, we found no significant evidence that genetic variants in FOLR1, GGH, FPGS and SLC19A1 are associated with the risk of colorectal cancer.

Interaction between glycolysis‒cholesterol synthesis axis and tumor microenvironment reveal that gamma-glutamyl hydrolase suppresses glycolysis in colon cancer

Background

Metabolic reprogramming is a feature of cancer. However, colon cancer subtypes based on the glycolysis‒cholesterol synthesis axis have not been identified, and little is known about connections between metabolic features and the tumor microenvironment.

Methods

Data for 430 colon cancer cases were extracted from The Cancer Genome Atlas, including transcriptome data, clinical information, and survival outcomes. Glycolysis and cholesterol synthesis-related gene sets were obtained from the Molecular Signatures Database for a gene set variation analysis. The relationship between the genomic landscape and immune landscape were investigated among four metabolic subtypes. Hub genes were determined. The clinical significance of candidate hub gene was evaluated in 264 clinical samples and potential functions were validated in vitro and in vivo.

Results

Colon cancer cases were clustered into four metabolic subtypes: quiescent, glycolytic, cholesterogenic, and mixed. The metabolic subtypes differed with respect to the immune score, stromal score, and estimate score using the ESTIMATE algorithm, cancer-immunity cycle, immunomodulator signatures, and signatures of immunotherapy responses. Patients in the cholesterogenic group had better survival outcomes than those for other subtypes, especially glycolytic. The glycolytic subtype was related to unfavorable clinical characteristics, including high mutation rates in TTN, APC, and TP53, high mutation burden, vascular invasion, right colon cancer, and low-frequency microsatellite instability. GGH, CACNG4, MME, SLC30A2, CKMT2, SYN3, and SLC22A31 were identified as differentially expressed both in glycolytic-cholesterogenic subgroups as well as between colon cancers and healthy samples, and were involved in glycolysis‒cholesterol synthesis. GGH was upregulated in colon cancer; its high expression was correlated with CD4⁺ T cell infiltration and longer overall survival and it was identified as a favorable independent prognostic factor. The overexpression of GGH in colon cancer-derived cell lines (SW48 and SW480) inhibited PKM, GLUT1, and LDHA expression and decreased the extracellular lactate content and intracellular ATP level. The opposite effects were obtained by GGH silencing. The phenotype associated with GGH was also validated in a xenograft nude mouse model.

Conclusions

Our results provide insight into the connection between metabolism and the tumor microenvironment in colon cancer and provides preliminary evidence for the role of GGH, providing a basis for subsequent studies.