Risk Score Prediction Model of Prognosis in GC Patients by Age and Gender Combined With m6A Modification Genes FTO and RBM15

Background: Gastric cancer (GC) has a high mortality rate. N6-methyladenosine (m6A) is involved in the development of GC. Age and gender are associated with GC incidence and survival. This study aimed to explore the risk score prediction model of prognosis in GC patients by age and gender combined with m6A modification genes.Methods: Data on m6A modification gene expression and clinical information downloaded from the Cancer Genome Atlas (TCGA) database were used to construct the risk score prediction model. Cox and least absolute shrinkage and selection operator (LASSO) regression were performed to identify clinical characteristics and m6A modification genes associated with prognosis. A risk score prediction model was established based on multivariate Cox regression analysis. The Gene Expression Omnibus (GEO) database was used to validate this model.Results: Most of the m6A modification genes were upregulated in GC tumor tissues compared with that in normal tissues and were correlated with clinical characteristics including grade, stage status, and T status. The risk score prediction model was established based on age, gender, FTO, and RBM15. GC patients were divided into high- or low-risk groups based on the median risk score. Patients with a high risk score had poor prognosis. Multivariate Cox regression indicated that risk score was an independent prognostic factor for GC patients. The data from GSE84437 verified the predictive value of this model.Conclusion: The risk score prediction model based on age and gender combined with m6A modification genes FTO and RBM15 was an independent prognostic factor for GC patients.

Automated summary

This study constructed a risk score prediction model for gastric cancer patients based on age, gender, and m6A modification genes FTO and RBM15. The risk score was identified as an independent prognostic factor, and patients with high risk scores had poorer prognosis. The model's predictive value was verified using data from the GEO database.