Results
The detailed flowchart is shown in Fig. S1 . In the TCGA-STAD dataset, a total of 4,937 DEGs (STAD vs normal), containing 3,647 up-regulated genes and 1,290 down-regulated genes in STAD, were uncovered ( Table S1 , Figure 1A ). Hence, 14 DERMGs (NOP2, DNMT1, YTHDF1, NSUN2, TRMT6, METTL1, WDR4, RBM15, KIAA1429, NSUN5, DNMT3B, TRMT10C, ALYREF and ZC3H13) between STAD and normal samples were determined by crossing the DEGs and RMGs ( Figure 1B ). The expression of DERMGs association with M1A and M5C modify, including TRMT10C, TRMT6, NOP2, NSUN2, NSUN5, DNMT1, DNMT3B, ALYREF, was shown in Figs. 1C and 1D . Meanwhile, the expression of DERMGs association with m6A and M7G modify, including RBM15, ZC3H13, KIAA1429, YTHDF1, METTL1 and WDR4, was revealed in Figs. 1E and 1F . All the 14 DERMGs presented a upward expression trend in STAD samples in comparison to normal samples. The GO enrichment analysis revealed that methylation, RNA modification, and tRNA-related pathways were notably enriched in these 14 DERMGs, such as RNA methylation, tRNA modification, mRNA ( Figure 1G ).

The above 14 DERMGs were then subjected into the univariate Cox analysis and four genes associated with overall survival (OS) of STAD patient ( p < 0.05) were identified, namely RBM15, DNMT1, WDR4 and ALYREF ( Figure 2A ). To probe the related subtypes in STAD, 345 STAD samples were clustered based on the expression of four RMGs using consensus cluster analysis. From the result, two subtypes were identified, designated as cluster 1 and cluster 2 ( Figs. 2B – 2E ). The cluster 1 contained 164 cases and cluster 2 contained 181 cases. The t-SNE result revealed that cluster 1 and cluster 2 could be clearly separated, further demonstrating the reliability of the clustering result ( Figure 2F ). Survival analysis revealed that prognosis differed significantly between the two DERMGs-related subtypes, and cluster 1 had a notable survival advantage ( Figure 2G ). The expression of RBM15, DNMT1, WDR4 and ALYREF were up-regulated in cluster 1 ( Figure 2H ). Since the hazard ratio (HR) of RBM15, DNMT1, WDR4 and ALYREF was all less than 1 ( Figure 2A ), above results indicated that RBM15, DNMT1, WDR4 and ALYREF were protective factors in influencing the survival of STAD patients.

Following this, we deployed the ‘limma’ package to mine the DEGs between cluster 1 and cluster 2 utilizing p value < 0.05 and |Log 2 FC| > 1 as screening criteria. As shown in Figs. S2A , S2B and Table S2 , 56 DEGs (10 up-regulated genes and 46 down-regulated genes) between cluster 1 and cluster 2 were mined. We performed functional enrichment analysis on the 56 genes, and a grand total of 40 GO items (12 BP items, 21 CC items and seven MF items) and 1 KEGG pathway were derived ( Table S3 ). The top eight items in each classification were exhibited in Figs. S2C and S2D . We noted that the DEGs were associated with digestion, maintenance of gastrointestinal epithelium and mitosis-related biological processes.

To dig the genes relevant to the overall survival (OS) of STAD patients, we incorporated the 56 DEGs between cluster 1 and cluster 2 into a univariate Cox analysis in the training set. Five out of the 56 genes were certificated as genes linked to patients’ OS in the training set ( p -value < 0.05) ( Figure 3A ). Subsequently, the five genes were further submitted to LASSO regression analysis. Four feature genes ( ACTA2 , SAPCD2 , PDK4 and APOD ) were picked out and each regression coefficient was computed ( Figs. 3B and 3C ). We then constructed a risk score model with the following formula: Riskscore = 0.279 × ACTA2 + (−0.16759) × SAPCD2 + 0.087985 × PDK4 + 0.0665 × APOD . Based on this formula, we calculated the risk score for each STAD patient in the training set (cutoff = 0.763) and test set (cutoff-0.705) and classified them into high- and low-risk subgroups based on median value respectively. K-M curves manifested that patients with higher risk had noteworthy worse survival than patients with lower risk ( Figs. 3D and 3E ). The AUC values OS in the training cohort were 0.625 (1-year), 0.604 (3-year) and 0.703 (5-year) respectively, reflecting a decent accuracy of the model ( Figure 3G ). Meanwhile, the AUC values of OS in the test set were 0.721 (1-year), 0.60 (3-year) and 0.683 (5-year) respectively ( Figure 3H ). Survival status manifested that as the RNA methylation-relevant risk score increases, patients suffered a relatively high risk of death ( Figs. 3J and 3K ). The expression heatmap manifested APOD , ACTA2 and PDK4 were highly expressed in the patients with higher risk. SAPCD2 were highly expressed in the patients with lower risk ( Figs. 3J and 3K ). To further confirm the reliability of the RNA methylation-relevant risk signature, the above analysis was carried out in the external validation set ( GSE84426 , cutoff = 3.848). The results of external validation set were consistent with the training set ( Figs. 3F , 3I and 3L ).

Next, we further explored the relationship between the RNA methylation-relevant risk score and clinical characteristic factors by comparing risk scores across clinical characteristic subgroups and stratified survival analysis. As exhibited in Figure 4 , patients aged less than 60 years had significantly higher risk than those aged greater than 60 years. Stage I patients had significantly lower risk than those in Stage II–IV. The result of stratified survival analysis indicated that the risk score was an effective predictor of survival in N1–N3 stage patients, M0 stage patients, T3–T4 stage patients, stage III–IV patients, male patients and patients aged greater than 60 ( p value < 0.05, Fig. S3 ).

We conducted difference analysis on immune cells, molecular drugs and SNP mutations in the two RNA methylation-relevant risk subgroups of the training set. Tumor-infiltrating immune cells with CIBERSORT showed that naive B cells, memory B cells, activated CD4 memory T cells, follicular helper T cells, resting NK cells, monocytes, macrophages M0, resting mast cells, and activated mast cells had significant distinctions between the two risk subgroups ( Figs. S4A and S4B ). SNV waterfall plot showed that altered in 105 (89.74%) of 117 samples in high-risk subgroup ( Fig. S4C ), and altered in 114 (95.8%) of 119 samples in a low-risk group ( Fig. S4D ). The correlations between risk score and TMB between two risk groups showed significant differences ( Figs. S4E and S4F ). The drug sensitivity between the two risk groups was shown in Fig. S4G , 36 drug sensitivities were notably different in between the two risk subgroups ( Table S4 ). Finally, we analyzed the survival difference among TMB high-risk high group, TMB high-risk low group, TMB low-risk high group and TMB low-risk low group, the results showed significant difference ( Fig. S4H ).

The result of Cox analysis (univariate) demonstrated that the p values for stage, T stage, N stage, M stage and risk score were all less than 0.05 ( Figure 5A ) and the corresponding factors were enrolled in the multivariate Cox analysis. The forest plot of Cox regression analyses (multivariate) demonstrated that the risk score and stage were independent predictors of prognosis for patients with STAD ( Figure 5B ). The nomogram containing independent prognostic predictors was generated ( Figure 5C ). The C index of the nomogram was 0.671, and the calibration plots manifested that the nomogram was an accurate predictor ( Figure 5D ).

To probe possible reasons for the inconsistency in survival between the two RNA methylation-relevant subgroups, we proceeded with a GSEA analysis. A total of 2,658 GO entries and 132 KEGG pathways were derived ( Table S5 ). Top 10 GO entries and KEGG pathways were shown in Figs. 6A and 6B . We noted that biological processes such as ‘mitotic sister chromatid segregation’, ‘cell cycle checkpoint’, ‘DNA damage checkpoint’, ‘G2/M transition of mitotic cell cycle nuclear division’, ‘RNA splicing, via transesterification reactions’, ‘RNA splicing, via transesterification reactions with bulged adenosine as nucleophile’ were linked to the low-risk subgroup ( Figure 6A ). Meanwhile, ‘pathways in cancer’, ‘Olfactory transduction’, ‘Neuroactive ligand-receptor interaction’, ‘PI3K-Akt signaling pathway’, ‘MAPK signaling pathway’, ‘Cytokine-cytokine receptor interaction’, ‘Calcium signaling pathway’, ‘Ras signaling pathway’, ‘Proteoglycans in cancer’ and ‘Focal adhesion’ were correlated with the high-risk subgroup ( Figure 6B ). We speculated that cancer- and immune-related pathways played an vital role in influencing the prognosis of the high-risk subgroup.

As TME plays a role in gastric carcinogenesis and progression, we analyzed the expression of RNA methylation-relevant prognostic genes in TME-associated cells using the GSE134520 dataset in the TISCH database. As shown in the Figs. 7A and 7B , we analyzed nine cell clusters in the GSE134520 dataset, and Figs. 7A and 7B showed the number and distribution of the nine cell clusters. Further analysis revealed that ACTA2 was mainly expressed in myofibroblasts and fibroblasts ( Figs. 7C and 7G ), APOD had the highest expression in fibroblasts ( Figs. 7D and 7H ), PDK4 was mainly expressed in fibroblasts, myofibroblasts and malignant ( Figs. 7E and 7I ), and SAPCD2 was mainly expressed in pit mucous and gland mucous ( Figs. 7F and 7J ).

The immune system exerts an important role in influencing both gastric carcinogenesis and progression as well as patient prognosis. Therefore, we analyzed the correlation between RNA methylation-relevant prognostic genes and immune factors. The results showed that ACTA2 was significantly positively correlated with the immunoinhibitors CSF1R , PDCD1LG2 , TGFB1 , TGFBR1 and immunostimulators CD48 , CXCL12 , CXCR4 , ENTPD1 , TMEM173 , TNFRSF13B , TNFSF4 ( Figs. S5A – S5K , S6A and S7A ). APOD was significantly positively related to the immunoinhibitors ADORA2A , CSF1R , PDK4 and the immunostimulators TGFBR1 , C10orf54 , CXCL12 , CXCR4 , ENTPD1 , TNFRSF13B ( Figs. S5L – S5S , S6B and S7B ). PDK4 was significantly positively associated with immunoinhibitors TGFBR1 , and immunostimulators C10orf54 , CXCL12 , CXCR4 , ENTPD1 , TNFRSF13B ( Figs. S8A – S8F , S6C and S7C ). SAPCD2 was significantly negatively related to the immunoinhibitors BTLA , CSF1R and immunostimulators C10orf54 , CD28 , CD40LG , CD48 , CXCR4 , IL6R ( Figs. S8G – S8O , S6D and S7D ). SAPCD2 and the immunostimulator PVR was significantly positively correlated ( Figs. S8P and S7D ).

The cBioPortal website was used to analyze the mutation status of the four prognostic genes in the STAD sample. Four prognostic genes all had missense mutations and amplification, and ACTA2 and APOD had deletions ( Figure 8A ). The specific mutation sites for each gene were shown in Figure 8B . We further analyzed the effect of CNV on the expression of RNA methylation-relevant prognostic genes and detected that the expression of RNA methylation-relevant prognostic genes was significantly associated with CNV ( Figure 8C ).

To investigate the interaction among prognostic genes, the protein-protein interaction (PPI) network including four prognostic genes and 20 genes that interact with them was constructed via GeneMANIA database. Figure S9A indicated all four prognostic genes interacted strongly with 20 genes, especially with APOA2 , GC , and MYL12A . In addition, PDK4 , PDP1 , and PDRP were mainly involved in functions such as regulation of sulfur metabolic process, regulation of nucleotide biosynthetic process, and acetyl-CoA metabolic process. In addition, we explored the correlation of four prognostic genes and 14 DERMGs. The results showed that PDK4 , APOD and ACTA2 were negatively correlated with DERMGs, while SAPCD2 was positively correlated, except for ZC3H13 ( Fig. S9B ).

As illustrated in Table S1 , SAPCD2 was up-regulated and ACTA2 , PDK4 and APOD were presented reduced expression in STAD tissues compared to control tissues. To further affirm the expression of prognostic genes, we collected one normal gastric epithelial cell line (GES-1) and three GC cell lines (MKN-45, MKN-7 and SNU-1), extracted RNA, and executed RT-qPCR. As exhibited in Fig. S10 , we detected that SAPCD2 was highly expressed in GC cell lines, while ACTA2 , PDK4 and APOD were lowly expressed in GC cell lines. The experimental result proved that the expression trends of prognostic genes were comparable to with the result from public database. Moreover, we test the methylation levels of the three feature genes and found they differed in methylation sites, ACTA2 detected one differentially methylated site, APOD had a total of two methylated sites, only one of which differed, and PDK4 had two differentially methylated sites ( Fig. S11 ).