Prognostic analysis of patients with gastric cancer based on N-methyladenosine modification patterns and tumor microenvironment characterization.

BACKGROUND

Cancers arise from genetic and epigenetic abnormalities that affect oncogenes and tumor suppressor genes, compounded by gene mutations. The N6-methyladenosine (mA) RNA modification, regulated by methylation regulators, has been implicated in tumor proliferation, differentiation, tumorigenesis, invasion, and metastasis. However, the role of mA modification patterns in the tumor microenvironment of gastric cancer (GC) remains poorly understood.

MATERIALS AND METHODS

In this study, we analyzed mA modification patterns in 267 GC samples utilizing 31 mA regulators. Using consensus clustering, we identified two unique subgroups of GC. Patients with GC were segregated into high- and low-infiltration cohorts to evaluate the infiltration proportions of the five prognostically significant immune cell types. Leveraging the differential genes in GC, we identified a "green" module via Weighted Gene Co-expression Network Analysis. A risk prediction model was established using the LASSO regression method.

RESULTS

The "green" module was connected to both the mA RNA methylation cluster and immune infiltration patterns. Based on "Module Membership" and "Gene Significance", 37 hub genes were identified, and a risk prediction model incorporating nine hub genes was established. Furthermore, methylated RNA immunoprecipitation and RNA Immunoprecipitation assays revealed that YTHDF1 elevated the expression of DNMT3B, which synergistically promoted the initiation and development of GC. We elucidated the molecular mechanism underlying the regulation of DNMT3B by YTHDF1 and explored the crosstalk between mA and 5mC modification.

CONCLUSION

mA RNA methylation regulators are instrumental in malignant progression and the dynamics of tumor microenvironment infiltration of GC. Assessing mA modification patterns and tumor microenvironment infiltration characteristics in patients with GC holds promise as a valuable prognostic biomarker.