Prognostic value of RNA methylation-related genes in gastric adenocarcinoma based on bioinformatics

Introduction

One of the most common malignancies in the world is gastric cancer (GC). According to statistics, more than 1 million individuals are diagnosed with stomach cancer each year, accounting for 5.7 percent of all cancer diagnoses, and around 700,000 people die from it each year (Ye et al., 2021; Wong et al., 2021). GC, which is produced by malignant alterations in gastric gland cells and accounts for 90 percent to 95 percent of GC (Zhang et al., 2020), is the most frequent pathological form of GC. Despite the fact that the prognosis for GC has improved as a result of the advancement of numerous therapeutic options, including as surgery, chemotherapy, radiation and targeted therapy, the patients’ 5-year overall survival rate is still dismal (Qiu et al., 2020). As a result, it is critical to thoroughly comprehend GC’s molecular process and to investigate novel and dependable prognostic indicators.

In RNA, more than 160 chemical changes have been found (Zhang et al., 2021), and the m6A (N6 methyladenine) change in eukaryotic mRNA and lncRNA was detected as early as the 1970s, showing that methylation of RNA is a post-translational regulation mechanism (Zhou et al., 2020). The most frequent methylation modification of mRNA in eukaryotic cells is m6A, which has an impact on all phases of the RNA life cycle (Roundtree et al., 2017a). M6A RNA affects splicing (Tang et al., 2018; Yao et al., 2021), export (Roundtree et al., 2017b), stability and translation of mRNA posttranscriptionally (Huang et al., 2018). Five-methylcytosine (m5C) is found in the mRNA transcript’s untranslated region (UTR), and it has been linked to a variety of gene expression activities such as RNA export, ribosome assembly and translation (Pan, Huang & Xu, 2021). N1-methyladenosine (m1A), a critical posttranscriptional alteration in RNA, was discovered more than five decades ago and has now acquired general acceptance (Zhao et al., 2019). The majority of m1A is found at the translation initiation site of mRNA, which is close to the translation start point of mRNA (Gao et al., 2021). M7G controls mRNA export, translation and splicing by being located at the caps of mRNAs, as well as at particular internal sites in tRNAs and rRNAs (Pandolfini et al., 2019; Zhang et al., 2019).

RNA methylation-related proteins include methyltransferase (Writer), demethylase (Eraser), and RNA methylation-specific recognition protein (Reader) (Zhou et al., 2020). RNA methylation-related proteins have been shown to reversibly govern essential biological activities as RNA metabolism, processing and stem cell directed differentiation (Zhao et al., 2014). In recent years, RNA methylation has been linked to tumor genesis, progression and metastasis. For example, According to one study, DNMT1-mediated FOXO3a promoter hypermethylation reduces FOXO3a expression in breast cancer, and FOXO3a decreases breast cancer stem cell characteristics and tumorigenicity through lowering FOXM1/SOX2 signaling (Liu et al., 2020). The RNA-binding protein ALYREF binds to the m5C gene. PKM2 and ALYREF levels have been associated to poor outcomes in bladder cancer patients, suggesting that ALYREF and its target gene PKM2 might be helpful biomarkers for guiding early bladder cancer diagnosis (Wang et al., 2021). WDR4 levels are linked to cancer immunity and may be used as a prognostic biomarker for some cancers (Zeng et al., 2021). However, RNA methylation in GC is infrequently examined, and the overall prediction level in GC is insufficient.

We investigated the expression of RNA methylation regulators in STAD using the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and for the first time looked at the relationship between RNA methylation-related prognostic genes and clinicopathological features. To demonstrate the predictive usefulness of prognostic genes related with RNA methylation for Stomach adenocarcinoma (STAD), we created several tumor subgroup models and risk models. It was also highlighted how these differentially expressed genes relate to cell signaling pathways and the tumor immunological microenvironment.

Materials and Methods

Results

DERMGs in STAD

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, Fig. 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 (Fig. 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 (Fig. 1G).

Recognition of DERMGs-related subtypes in STAD

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 (Fig. 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 (Fig. 2F). Survival analysis revealed that prognosis differed significantly between the two DERMGs-related subtypes, and cluster 1 had a notable survival advantage (Fig. 2G). The expression of RBM15, DNMT1, WDR4 and ALYREF were up-regulated in cluster 1 (Fig. 2H). Since the hazard ratio (HR) of RBM15, DNMT1, WDR4 and ALYREF was all less than 1 (Fig. 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 |Log₂FC| > 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.

The RNA methylation-relevant risk signature based on DEGs between cluster 1 and cluster 2

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) (Fig. 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 (Fig. 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 (Fig. 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).

Risk score and clinical features

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 Fig. 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).

Independent prognostic value of the RNA methylation-relevant risk signature

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 (Fig. 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 (Fig. 5B). The nomogram containing independent prognostic predictors was generated (Fig. 5C). The C index of the nomogram was 0.671, and the calibration plots manifested that the nomogram was an accurate predictor (Fig. 5D).

GSEA analysis

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 (Fig. 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 (Fig. 6B). We speculated that cancer- and immune-related pathways played an vital role in influencing the prognosis of the high-risk subgroup.

Prognostic genes and TME

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).

Mutation of prognostic genes in STAD

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 (Fig. 8A). The specific mutation sites for each gene were shown in Fig. 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 (Fig. 8C).

Discussion

GC is the major cause of mortality from neoplastic disorders (Wu et al., 2019), with gastric adenocarcinoma accounting for about 95% of stomach cancers (Liu et al., 2017). As has been shown in recent decades, the development of stomach cancer is a complex process involving several components, and different molecular manifestations have variable clinical prognostic repercussions for patients. Tumor staging in clinical practice only evaluates tumor size, histological classification and metastasis (Niimi et al., 2013), but not the patient’s gene expression level. Recent research has demonstrated that stomach cancer development is influenced by epigenetic changes (Zhang et al., 2018). Non-coding RNA, histone modification, RNA methylation and DNA methylation are all examples of epigenetics (Chen et al., 2016; Xie et al., 2021), with RNA methylation becoming a popular topic in recent years. It is a dynamic and reversible modification mechanism, with methyltransferases, demethylases and binding proteins regulating the whole process (Mo et al., 2020; Romanowska et al., 2021). Evidence is accumulating in GC, underscoring the influence of RNA methylation on the malignancy and prognosis of tumor cells. For example, A recent investigation has suggested that METTL3 enhances the proliferation, formation of colonies, migration, and invasion of GC cells in an m6A-dependent manner via the pre-B-cell leukemia homeobox 1 (PBX1)/GTP cyclohydrolase 1 (GCH1)/tetrahydrobiopterin (BH4) axis (Liu et al., 2022).

By downloading and retrieving a large number of GC samples from the TCGA database, we observed that the expression of 14 RNA methylation regulators was significantly altered in GC tissues. NOP2, DNMT1, YTHDF1, NSUN2, TRMT6, METTL1, WDR4, RBM15, KIAA1429, NSUN5, DNMT3B, TRMT10C, ALYREF and ZC3H13 are among the genes that have been identified. All 14 DERMGs were found to be elevated. According to our GO enrichment analysis, these DERMGs are significantly associated with RNA methylation, tRNA modification, and related processes. Previous research suggests that the expression of NOP2 primarily relies on the m5C methylation level, as it can promote cancer cell proliferation through m5C-dependent inhibition of Cyclin-Dependent Kinase Inhibitor 1B (CDKN1B, p27Kip1) (Mei et al., 2020; Yang et al., 2023). The m7G tRNA methylation facilitated by the METTL1/WDR4 complex selectively enhances the translation of specific cyclin and oncogenic transcripts, along with downstream pathway-related mRNAs, thus governing cell proliferation and apoptosis in conjunction with the abundant homologous codons of m7G tRNAs (Porta, Paglino & Mosca, 2014). Hence, we speculation that the elevated expression of these DERMGs may exert a significant impact on their target transcripts, especially those involved in cancer development, rendering them pivotal epigenetic regulatory factors. This mechanism could potentially serve as a common oncogenic driver in GC progression.

Following that, we used univariate COX analysis to find four genes, RBM15, DNMT1, WDR4 and ALYREF, that were linked with overall survival (OS) in STAD patients. RBM15, DNMT1, WDR4 and ALYREF were identified as protective factors affecting STAD patient survival. Previous research has demonstrated that silences DNMT1 gene activity and leads to the activation of a number of tumor-related target genes (Chen et al., 2018). WDR4 is a methyltransferase that participates in cell cycle progression, signaling, gene control and apoptosis (Michaud et al., 2000; Arrondel et al., 2019). WDR4 gene expression was found to be down-regulated in advanced renal papillary carcinoma but constant in advanced rectal adenocarcinoma and GC in one study (Zeng et al., 2021). WDR4 expression was also found to be a protective factor in rectal adenocarcinoma in the corresponding OS outcome study. ALYREF is thought to be a preventive factor against colon cancer (Huang et al., 2022). This is in line with the findings of our research. RBM15 may have a function as an oncogene in lung adenocarcinoma, according to relevant publications (Li et al., 2020); Su, Huang & Hu (2019) discovered that RBM15 may contribute to the malignant evolution of GC and alter clinical prognosis. This might be due to the fact that RBM15 influences the formation of malignant tumors via a variety of molecular processes.

Furthermore, we discovered that the RNA methylation genes APOD, ACTA2, PDK4 and SAPCD2 are linked to the prognosis of GC. Simultaneously, these four genes have been shown to reliably predict patient prognosis in predictive risk profiles. Patients who meet the criteria for being classified as high-risk have a bad prognosis. These data show that RNA methylation-associated prognostic genes may have predictive prognostic value as well, and as a result, they might be employed as a potential prognostic predictor in patients with stomach cancer.

APOD, ACTA2, PDK4 and SAPCD2 have all been linked to the development of cancer in several studies. APOD is a protein that is generated in many different regions of the body (Braesch-Andersen et al., 2014). APOD-related complexes deliver cholesteryl esters to the liver and encourage their breakdown (Utermann et al., 1980). Additionally, apolipoproteins in the blood may provide lipids to cancer cells, giving them energy for expansion and invasion (Xu et al., 2007). Compared to women without breast cysts, those with breast cysts had a threefold higher chance of developing BC (Zhou & Luo, 2020). Because APOD is the most abundant component of cyst fluid, it is possible that APOD plays a role in the development of breast cancer (Sánchez et al., 1992). APOD was substantially expressed in the high-risk group, increased in colon cancer tissues, and had potential predictive value, according to a correlation study in the TCGA database against colon cancer (Liang, Su & Wu, 2021). Hu et al. (2018) discovered that APOD expression was substantially connected with the diagnosis of GC as a risk factor, and that it might be a possible prognostic biomarker for GC. ACTA2 promotes cell mechanical tension and shape maintenance, as well as tumor cell metastasis (Lambrechts, Van Troys & Ampe, 2004). Patients with high ACTA2 expression in lung adenocarcinoma had considerably increased distant metastases and a poor prognosis, according to one research (Lee et al., 2013). ACTA2 may hasten the formation of human tumors by acetylating late 6-phosphogluconate dehydrogenase (6PGD) at K76 and K294 (Shan et al., 2014). ACTA2 inhibition causes an intracellular buildup of unesterified oxysterols, which limits the development of hepatoma cell lines and xenografts (Lu et al., 2013). He et al. (2022) discovered that greater ACTA2 expression was related with a worse clinical outcome in the GC high-risk group. PDK4 belongs to a group of isozymes (PDK1-4) that help the body convert to aerobic glycolysis by transferring pyruvate metabolism from the mitochondria to the cytoplasm for glycolysis (Woolbright et al., 2018). Because most cancers have some degree of hypoxia, Hypoxia-inducible factor 1 (HIF-1) is connected to a poor prognosis in bladder cancer and is assumed to be the key mediator of transcriptional control of aerobic glycolysis in cancer (Chai et al., 2008; Denko, 2008), could be the main transcription factor driving PDK4 upregulation in cancer. PDK4 is a biomarker of GC prognosis and a prospective therapeutic target, according to Liu, Zhang & Suo (2021). It is also linked to the quantity of invading immune cells and patients’ poor prognosis, and it is a biomarker of GC prognosis as well as a viable therapeutic target in GC. PDK4 may have predictive relevance in various malignancies, such as liver and colorectal cancer (Leclerc et al., 2017; Deng et al., 2018; Qin et al., 2020). Furthermore, we discovered for the first time, based on the existing prognostic model, that SAPCD2 expression was connected with survival in STAD patients and was recognized as a protective prognostic factor. SAPCD2 is a recently discovered highly conserved mammalian gene that regulates cell cycle and chromosomal segregation (Mao et al., 2014). This gene is substantially expressed in GC, glioma, liver cancer and other tumor tissues, according to relevant research, and is linked to tumor malignancy, invasion and metastasis (Wan et al., 2014; Ni et al., 2015; Jayanthi, Das & Saxena, 2020). We think that SAPCD2’s role in the formation of STAD is complicated and dynamic, but the molecular mechanism has to be investigated further. Overall, Our study suggests that the RNA methylation-related risk signatures we constructed may serve as prognostic indicators for clinical treatment decisions in GC patients.

Several investigations have shown that genetic alterations often result in phenotypic abnormalities linked to carcinogenesis (Binang et al., 2020; Chen et al., 2021). CNV changes have been shown to disrupt genes involved in cell cycle regulation, the complement system and antigen presentation, potentially contributing to cancer (Brown et al., 2016). APOD and ACTA2 were deleted, and four prognostic genes, APOD, ACTA2, PDK4 and SAPCD2, were mutated and substantially related to CNV, according to our analysis. Following this study’s GSEA analysis, certain biological mechanisms have been found to have an important part in the onset and progression of cancer. Chromosome instability is a critical diagnostic sign for most malignancies (Olson et al., 2010); it is caused by abnormal cell division during mitosis or by changes in gene expression levels following chromosomal separation. Defects that guide proper and accurate chromosomal separation during mitosis can cause chromosome aneuploidy and instability, which can lead to malignant tumor formation and spread (Zhu et al., 2015). A cell cycle checkpoint is a crucial quality control measure that ensures that cell cycle events proceed normally. Cell growth will become uncontrollable if the regulatory method fails (Yin et al., 2018). Loss of checkpoint control of cell cycle pathways occurs with cancer growth, and their dysregulation may impact the efficiency of cancer treatment (Zhang et al., 2011; Ho et al., 2017). Furthermore, the quick transition of the G2/M phase during mitosis promotes GC cell growth (Lim et al., 2020). By controlling cell cycle progression and tumor growth, the PI3K/AKT signaling pathway increases the onset and progression of GC (Pereira et al., 2020). Many studies have shown that the MAPK/ERK signaling pathway is the primary molecular mechanism behind liver carcinogenesis (Zender et al., 2010), and that HCC can be efficiently generated by activating oncogenes or inhibiting tumor suppressor genes in conjunction with MAPK/ERK signaling (Zhang, Budker & Wolff, 1999). The inflammatory response has been linked to cytokine–cytokine receptor interactions, and cancer and inflammation have a very strong connection (Kan et al., 2019). Pro-tumor inflammation accelerates the growth of tumor cells and, to some extent, reduces anti-tumor immunity (Yu, Pardoll & Jove, 2009).

TME, which includes tumors, extracellular matrix, lymph, blood vessels and interstitial cells, plays a crucial part in the development, invasion and metastasis of tumors (Palicelli et al., 2021). According to one research, myofibroblasts are the most common stromal cell type in most malignancies, and they have been proven to aid tumor development by secreting a range of growth factors (Sharma, Evans & Hemers, 2016). At the same time, myofibroblasts can produce a variety of pro-tumor factors and directly participate in promoting metastasis (Karnoub et al., 2007; Seiler et al., 2020). Hwang et al. (2008) discovered that pancreatic tumor fibroblasts have a key role in tumor cell proliferation, motility, invasion and treatment resistance. By stimulating epithelial cell transition into mesenchymal cells and secreting energy-rich substances to enhance cancer cell development, fibroblasts boost prostate cancer aggressiveness. Fibroblasts also promote capillary morphogenesis and endothelial cell proliferation (Taddei et al., 2014). Disruptions in the quantity of mucous cells in the antrum and homeostasis have been reported to increase the probability of GC (Yu et al., 2016). Glandular mucus cells release glandular mucin, help make gastric mucin, and subsequently form a mucus gel layer to protect the stomach mucosa from the outside world (Ota & Katsuyama, 1992). At the same time, glandular mucin secreted by glandular mucus cells can inhibit helicobacter pylori infection to prevent GC, and can also be used as an inhibitor of differentiated gastric adenocarcinoma (Nakayama, 2014). In this study, we found that ACTA2, APOD and PDK4 were mainly expressed in myofibroblasts and fibroblasts respectively, while SAPCD2 was mainly expressed in alveolar mucous cells and glandular mucous cells of gastric adenocarcinoma. We speculated that ACTA2, APOD and PDK4 were risk factors. It may promote the onset and progression of STAD via the aforementioned mechanism, while SAPCD2 plays a protective role.

In recent years, immune infiltration has been a research focus. PDK4 expression was shown to be favorably linked with the numbers of several invading immune cells, including CD4⁺T cells, B cells and dendritic cells, as well as macrophages (Chai et al., 2008). APOD was shown to be a risk factor and substantially linked with lymphoid infiltration in a biologic information investigation on colon cancer (Liang, Su & Wu, 2021). Many immunosuppressive agents, such as csF1R, ADORA2A, TGFBR1 and BTLA, were discovered to be related with APOD, ACTA2, PDK4 and SAPCD2 in this research. In addition to immunosuppressive agents, we discovered immune stimulators such as CD48, CXCR4, C10 and PVR. APOD, ACTA2, PDK4 and SAPCD2 are thought to be essential in the immunomodulation of STAD. As a result, combining inhibitors and activators might possibly improve STAD patients’ anti-cancer effects.

Pharmacological chemotherapy is still often used to treat GC patients, but the development of chemoresistance during treatment is a key factor in patient fatalities. Clinically, patients with different tumor stages often respond differently to various chemotherapy treatments. We carried out chemotherapy sensitivity analysis for high and low risk categories so that GC patients might have superior antitumor chemotherapy regimens. Dasatinib, one of the most extensively studied tyrosine kinase inhibitors in clinical research, was initially developed as a dual inhibitor targeting BCR-ABL and SRC (Li et al., 2010). In other cancers, Dasatinib is capable of impeding cell migration and invasion through the inhibition of SRC, which blocks the transmission of downstream signals to proteins like focal adhesion kinase (FAK), responsible for adhesion, as well as MAPK and p27, which are involved in regulating the cell cycle (Johnson et al., 2005; Nam et al., 2005). In GC, dasatinib leads to a notable rise in actin accumulation at the cell cortex, which is also indicative of an enhanced cell-cell adhesion (Fenton, Hutchens & Denning, 2015; Montenegro et al., 2020). BIBW2992 not only directly inhibits HER2 receptor activation in GC but also suppresses its downstream signaling pathways, such as PI3K/AKT/mTOR and MAPK (Keller et al., 2018). Additionally, BIBW2992 induces apoptosis and arrests the cell cycle at the G1 phase (Chen et al., 2019). However, the effectiveness and applicability of these drugs still need validation through clinical trials.

We summarized four RNA methylation regulators, divided them into two subclasses according to their expression, and then analyzed them through the study of RNA methylation. We believe that more characteristic genes are often not conducive to large-scale clinical application (Li et al., 2021). Therefore, the four prognostic genes related to RNA methylation that we analyzed will be conducive to the successful implementation of clinical practice. These prognostic genes express differently in the high-risk and low-risk groups, which indicates that they are important in the incidence rate and progression of STAD. We established a risk model to study the clinical relevance of prognostic genes associated with RNA methylation. The AUC value and ROC curve show that compared with previous studies (Guan et al., 2020), the risk model we built has more advantages in predicting patient survival. In addition, this study found that the expression of prognostic genes is related to patient survival, which is an independent factor in the prognosis of STAD. At the same time, we also found that stage is an independent predictor, and we created a nomogram as a clear reference. It is worth noting that compared with the previous study (Guan et al., 2020; Wang, Meng & Ma, 2021), we increased the use of real-time PCR experiments to verify the expression of characteristic genes. In conclusion, this is the first time to study the predictive significance of RNA methylation related disease subtypes and RNA methylation related genes in STAD. These genes were linked to STAD prognosis and have many research prospects and may be good biological targets.

While our findings are promising, we must acknowledge that our research has significant limitations. First, bigger samples are required in clinical investigations to confirm the clinical importance of the built characteristics as independent prognostic markers of STAD, which will be the subject of future study. Second, while we validated the differential expression of prognostic genes in clinical samples through qRT-PCR, further investigations involving the establishment of animal or cell models will facilitate our understanding of the mechanisms underlying these prognostic genes in STAD. Additionally, the findings in our study, including the applicability of the prognostic model and the efficacy of drugs with varying sensitivities, require validation in clinical samples to demonstrate their clinical relevance. These areas represent the focal points of our future research endeavors.

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