Abstract
BACKGROUND: While early gastric cancer (EGC) is generally associated with a favorable prognosis, its clinical outcomes are notably heterogeneous. Unlike advanced gastric cancer requiring uniform intensive treatment, EGC lacks robust biomarkers to identify high-risk patients for personalized therapy. Although both N7-methylguanosine (m7G) modification and long non-coding RNAs (lncRNAs) are involved in tumorigenesis, the prognostic value of m7G-related lncRNAs in EGC remains poorly defined. METHODS: Based on TCGA-EGC data, we identified m7G-related lncRNAs with prognostic significance and constructed a prognostic model for m7G-related lncRNAs using univariate Cox and LASSO. The expression of the model genes was further validated by qRT-PCR. A series of statistical and bioinformatic analyses, including survival analysis, ROC curves, multivariate Cox regression, GSEA, immune infiltration, tumor mutation burden (TMB), and drug sensitivity assays, were performed to evaluate the prognostic performance and underlying biological characteristics of the signature. RESULTS: A six-m7G-related-lncRNA risk model effectively stratified EGC patients into high- and low-risk groups with significantly distinct overall survival. The risk score was confirmed as an independent prognostic factor by univariate and multivariate Cox analysis. Pathway differences between risk groups were primarily enriched in tumor microenvironment regulation terms. Notably, the high-risk group exhibited an immunosuppressive tumor microenvironment correlated with immune escape. TMB analysis showed a negative correlation between the risk score and TMB, with the high-risk and low-TMB subgroup exhibiting the poorest prognosis. In silico drug sensitivity analysis further suggested that high-risk patients may develop poorer sensitivity to five clinical drugs, including Cisplatin, Oxaliplatin, and Vinorelbine. CONCLUSION: The six-m7G-related-lncRNA signature represents a promising prognostic tool for EGC that may facilitate personalized risk stratification and guide clinical decision-making regarding adjuvant or immunotherapeutic strategies.