Variants of Genes Involved in Metabolism of Folate Among Patients with Breast Cancer: Association of TYMS 3R Allele with Susceptibility to Breast Cancer and Metastasis

The impact of folate pathway variants on the outcome of methotrexate therapy in rheumatoid arthritis patients

BACKGROUND

There are currently no validated criteria that entirely explain or predict response to methotrexate (MTX) treatment in rheumatoid arthritis (RA). We tried to identify the connection between three variants (RFC1 G80A (rs1051266), TYMS 2R/3R (rs34743033), and ATIC C347G (rs2372536)) in the folate pathway of MTX metabolism and the response to MTX monotherapy in a cohort of RA cases.

METHODS

A prospective study on 100 RA patients on MTX monotherapy was performed. Disease activity was measured at the start of treatment and 6 months after treatment with MTX. The patients were then split into two groups: those who responded to the treatment and those who did not. The molecular genetic study for the RFC1 (G80A) variant was employed via the PCR-restriction fragment length polymorphism (PCR-RFLP) technique, the ATIC (C347G) variant was performed using TaqMan allelic discrimination real-time PCR, and the tandem repeat sequences of TYMS (2R/3R) were amplified by conventional PCR and detected by agarose gel electrophoresis.

RESULTS

The genotype distribution of RFC-1 (G80A) showed significant variations among non-responders and responders in the recessive genetic model. A significant difference was found in TYMS (2R/3R) in the dominant and heterozygous genetic models. However, ATIC (C347G) genotype frequency did not exhibit substantial link with drug response in all genetic models. Furthermore, the genotype and allele rates of the analyzed variants did not show any significant association with adverse events in all genetic models.

CONCLUSION

The 80AA genotype of RFC-1 G80A and the 2R/3R or 3R/3R genotypes of TYMS 2R/3R are more vulnerable to the good consequences of MTX therapy. Key Points • Current recommendations support the gold standard role of MTX as a first-line monotherapy for RA patients. However, up to 40% of RA patients do not respond or exhibit partial response to MTX. • Persistent disease activity due to treatment unresponsiveness will affect the long-term outcomes in RA patients. • We aimed, through molecular genetic study, to identify the connection between three variants in the folate pathway of MTX metabolism and the response to methotrexate monotherapy in a cohort of RA patients.

Bioinformatics analysis of the potential mechanisms of Alzheimer's disease induced by exposure to combined triazine herbicides

BACKGROUND

The development of Alzheimer's disease (AD) is promoted by a combination of genetic and environmental factors. Notably, combined exposure to triazine herbicides atrazine (ATR), simazine (SIM), and propazine (PRO) may promote the development of AD, but the mechanism is unknown.

AIM

To study the molecular mechanism of AD induced by triazine herbicides.

METHODS

Differentially expressed genes (DEGs) of AD patients and controls were identified. The intersectional targets of ATR, SIM, and PRO for possible associations with AD were screened through network pharmacology and used for gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analysis. The binding potentials between the core targets and herbicides were validated by molecular docking and molecular dynamics.

RESULTS

A total of 1,062 DEGs were screened between the AD patients and controls, which identified 148 intersectional targets of herbicides causing AD that were screened by network pharmacology analysis. GO and KEGG enrichment analysis revealed that cell cycling and cellular senescence were important signalling pathways. Finally, the core targets EGFR, FN1, and TYMS were screened and validated by molecular docking and molecular dynamics.

CONCLUSION

Our results suggest that combined exposure to triazine herbicides might promote the development of AD, thereby providing new insights for the prevention of AD.

Potential molecular target screening and bioinformatics analysis of cholangiocarcinoma based on GEO database

BACKGROUND

Cholangiocarcinoma is a highly malignant tumor with a poor prognosis. Targeted therapy is important for the treatment of cholangiocarcinoma, and it is therefore of great clinical importance to identify novel molecular targets for targeted therapy of this malignancy.

AIM

To identify potential molecular targets for the treatment of cholangiocarcinoma and identify the key genes involved in cholangiocarcinoma by bioinformatics analysis.

METHODS

We downloaded two sets of cholangiocarcinoma expression profile data from GEO database. GEO2R online analysis tool was used to screen differentially expressed genes in cholangiocarcinoma tumor tissues and normal tissues, and we performed GO enrichment analysis, KEGG pathway analysis, and protein interaction network for differentially expressed genes. We used Cytoscape software to calculate key genes. The GEPIA database was used to verify the expression of hub genes in cholangiocarcinoma tissues.

RESULTS

A total of 158 differentially expressed genes were identified. GO enrichment analysis showed that these differential genes were mainly involved in the cellular response to zinc ion, negative regulation of growth, cell adhesion, metabolic process, and protein homotetramerization. They were enriched in exosomes, extracellular spaces, elastic fibers, and organelle membranes. The main molecular functions are related to heparin binding, cysteine-type endopeptidase inhibitor activity, protein homodimerization activity, receptor binding, and pyridoxal phosphate binding. KEGG pathway analysis showed that differential genes are mainly involved in processes such as mineral absorption, carbon and propanoate metabolism, PPAR signaling pathway, and fatty acid degradation. A protein interaction network diagram was constructed based on the String database, and the CytoHubba plug-in of the Cytoscape software was used to calculate the key genes. The key genes were all up-regulated ones. GEPIA analysis verified that the expression of key genes in cholangiocarcinoma tissues was significantly higher than that in normal tissues.

CONCLUSION

In this study, eight key genes related to cholangiocarcinoma were identified, including NUSAP1, TOP2A, RAD51AP1, MCM4, KIAA0101, CDCA5, TYMS, and ZWINT. These genes provide new ideas for in-depth study of the targeted therapy of cholangiocarcinoma, and are expected to become new molecular therapeutic targets.