Materials and Methods
This study retrived the TCGA-STAD cohort from the TCGA database ( https://portal.gdc.cancer.gov/ ). The TCGA-STAD cohort contained 32 normal tissue samples and 375 STAD tissue samples, of which 345 STAD samples containing intact clinical information were included in the cluster analysis, prognostic analysis and correlation analysis with clinical characteristics. The GSE84426 dataset ( Yoon et al., 2020 ), which comprised 76 GC samples bearing survival time for the validation of the prognostic model, was excavated from the GEO database ( https://www.ncbi.nlm.nih.gov/ ). RNA methylation genes (RMGs) were obtained according to previous reports ( Sun et al., 2020 ; Xie et al., 2020 ; Huang et al., 2021 ) and were enumerated in Table 1 .

The differentially expressed genes (DEGs) between control and STAD specimens in the TCGA-STAD cohort were defined utilizing the ‘limma’ R package ( Smyth, 2004 ). The cut-off criterion was |log 2 FoldChange(FC)| > 0.5 and p value < 0.05. The volcano map and heatmap were produced using the ‘ggplot2’ and ‘pheatmap’ R package. The differentially expressed RNA methylation genes (DERMGs) were obtained by crossing DEGs and RNA methylation genes (RMGs).

DERMGs associated with prognosis were selected by univariate Cox analysis. A total of 345 STAD cases in the TCGA-STAD cohort were classified depending on the expression of these genes applying the ‘ConsensusClusterPlus’ R package for consensus clustering ( Wilkerson & Hayes, 2010 ). The clustering results was verified by t-SNE dimension reduction analysis.

Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was execute through ‘clusterProfiler’ R package ( Yu et al., 2012 ). The p value < 0.05 and count ≥ 2 represented significance. GO analysis was comprised of biological processes (BP), cellular components (CC) and molecular functions (MF) correlated with genes.

The 345 STAD patients in the TCGA dataset were separated randomly into a training set (242 cases) and a test set (103 cases) at a 7:3 ratio. Through univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) logistic regression in the training set, the RNA methylation-relevant genes significantly associated with prognosis were filtered out. Using the formula: Riskscore = ∑ 1 n ⁡ c o e f ( g e n e i ) ∗ e x p r ( g e n e i ) and median of RNA methylation-relevant risk score, patients were separated into two risk subgroups (high risk and low risk). GSE84426 dataset was served as external validation set. Kaplan-Meier (K-M) curves (‘survminer’ package), receiver operating characteristic (ROC) analysis (‘survivalROC’ package), and risk curves (‘ggplot2’ package) were deployed to prove the predictive efficiency of RNA methylation-relevant risk signature.

The relevance between RNA methylation-relevant risk signature and clinicopathological factors, containing age, gender, T stage, N stage, M stage and pathological tumor stage was detected by Wilcoxon test or Kruskal-Wallis test and shown by violin plot. K-M analysis of the designated subtypes with different clinicopathological factors was also carried out. Cox regression analyses (Univariate and multivariate) were executed to recognize independent predictors of OS in the TCGA-STAD dataset. After that, a nomogram comprising the independent predictors of overall survival (OS) was created via the ‘cph’ function in R. The calibration curves and ROC curves (1-, 3- and 5-year) were drawn to affirm the predictive accuracy of the nomogram.

By using the log 2 (FC) of genes between two RNA methylation-relevant risk subgroups as the ranking criterion, Gene Set Enrichment Analysis (GSEA) was implemented by ‘clusterProfiler’ R package on the basis of GO and KEGG gene sets. The threshold for significantly enriched items and pathways was adjusted p value < 0.05.

The Tumor Immune Single Cell Hub (TISCH) ( http://tisch.comp-genomics.org/ ) ( Sun et al., 2021 ) is a database focused on the TME, collecting 76 tumor datasets from 27 cancers and involving single-cell transcriptomic profiles of almost 2 million cells. We used the GSE134520 dataset from the TISCH database to recognize the type and distribution of cells in TME, and the expression of RNA methylation-relevant prognostic genes in TME-associated cells.

TISIDB ( http://cis.hku.hk/TISIDB/ ) is a comprehensive repository portal of tumor-immune system interactions ( Ru et al., 2019 ) and was employed to analyze the Spearman correlations between RNA methylation-relevant prognostic genes and immune modulators.

The mutation of prognostic genes in the STAD samples of TCGA-STAD cohort were analyzed in cBioPortal ( https://www.cbioportal.org/ ) ( Unberath et al., 2019 ). The association of copy number variants (CNV) with the expression of prognostic genes was evaluated by chi-squared test.

Firstly, the one normal gastric epithelial cell line (GES-1) and three GC cell lines (MKN-45, MKN-7 and SNU-1) was fully lysed by the TRIzol Reagent according to the producer’s directions (Ambion, Austin, TX, USA). Chloroform (Chengdu Guerda rubber industry Co., LTD, Chengdu, China) was used to remove proteins and fat-soluble magazines, ice isopropanol (Chengdu Guerda rubber industry Co., LTD, Chengdu, China) was used to precipitate RNA, and 75% ethanol (Chengdu Colon Chemical Co., LTD, Chengdu, China) was used to further remove impurities. Then, the RNA concentration was detected with NanoPhotometer N50. After that, the reverse transcription of RNA into cDNA was done via the SweScript-First-strand-cDNA-synthesis-kit (Servicebio, Wuhan, China) relying on the manufacturers’ protocol (The four cell lines are all derived from iCell Bioscience Inc., a biotechnology company based in Shanghai, China). The qPCR reaction system was displayed in Table 2 . The reactions were implemented on a CFX96 real-time quantitative fluorescence PCR instrument. The amplification reactions were detailed in Table 3 . The primer sequences for PCR were tabulated in Table 4 . The expression was uniformized to the internal reference GAPDH and computed employing the 2 −ΔΔCq method ( Livak & Schmittgen, 2001 ).

All bioinformatic study were conducted using the R language, and the data from different groups were compared by the Wilcoxon test or Kruskal-Wallis test. Significance of differences in RT-qPCR was determined by t-test. P values less than 0.05 were deemed to be statistically significant if not noted above.