Differential analysis of RNA methylation regulators in gastric cancer based on TCGA data set and construction of a prognostic model

Abstract

Background: Methylation is one of the common forms of RNA modification, which mainly include N6methyladenosine (m6A), C5-methylcytidine (m5C), and N1-methyladenosine (m1A). Numerous studies have shown that RNA methylation is associated with tumor development. We aim to construct prognostic models of gastric cancer based on RNA methylation regulators. Methods:The transcriptome and clinical data of gastric cancer and normal samples were obtained from the National Cancer Institute Genome Data Commons (NCI-GDC). Use Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis to construct risk models for different types of RNA methylation. Receiver operating characteristic (ROC) curves were generated to evaluate the predictive efficiency of risk characteristics. Cluster heat maps are used to assess the correlation with clinical information. Univariate and multivariate Cox analyses were used to analyze prognostic effects of risk scores.Gene Set Enrichment Analysis (GSEA) analyzes the functional enrichment of RNA methylation genes. And make a separate analysis of the data of Asians.Results: The expression of most of the 30 RNA methylation regulators were significantly different in cancer and paracancerous tissues (P<0.05). Three methylated genes (FTO, ALKBH5, and RBM15) were screened from m6A by LASSO Cox regression analysis. Five methylated genes (FTO, ALKBH5, TRMT61B, RBM15, and YXB1) were selected from the population, and were used to construct two risk ratio models.Survival analysis showed that the survival rate of patients in the low-risk group was significantly higher than that in the high-risk group (P<0.05). All ROC curves indicated that the predictive efficiency of risk characteristics was good [area under the ROC curve (AUC): 0.6-1].Cluster analysis reveals differences in clinical data between the two groups. Univariate and multivariate Cox regression results show that the risk score has independent prognostic value. GSEA showed that pathways such as cell cycle were significantly enriched in the low-risk group, while pathways such as calcium signaling pathway were significantly enriched in the high-risk group. In addition, three methylation models that can predict the prognosis of Asian gastric cancer patients were obtained. Li et al. Prognostic model of RNA methylation in gastric cancer

Construction of a survival nomogram for gastric cancer based on the cancer genome atlas of m6A-related genes

“…Therefore, the prognosis of GC is of great importance to patients. Currently, although there is a large amount of constructed models to predict the prognosis of GC patients, the variables included were confined, especially the m6A-related genes they incorporated were limited, and other important clinicopathological characteristics such as age, gender, and staging were ignored (Su et al, 2019;Zhang B. et al, 2020;Li et al, 2021;Yue et al, 2022). The AJCC staging system is still the most widely used method to assess the prognosis of patients with GC based on factors such as the location of the primary lesion, tumor size, and the presence of distant metastases (Kumagai and Sano, 2021).…”

A Novel RNA N1-methyladenosine Regulator Mediated Methylation Modification Pattern Can Impact the Immune Status and Predict the Prognosis and Drug Sensitivity of Lung Adenocarcinoma

“…TRMT6 and TRMT61A were negatively correlated with poor prognosis of HCC [58]. TRMT61B played a role in the development of gastric cancer, and the clinical results of patients with high expression of gastric cancer are poor [59]. These studies suggested that m1A regulators are widely involved in the initiation and development of cancer.…”

The m6A/m5C/m1A Regulated Gene Signature Predicts the Prognosis and Correlates With the Immune Status of Hepatocellular Carcinoma

“…In eukaryotic messenger RNA (mRNA) regulation, N 6methyladenosine (m6A), N 1 -methyladenosine (m1A), and 5methylcytosine (m5C) modifications exist. Several studies have confirmed that m6A, m1A, and m5C regulated genes play important roles in m6A, m1A, and m5C modifications (7)(8)(9)(10)(11). Recent research has found that m6A, m1A, and m5C regulated gene expression levels are associated with tumor progression (11)(12)(13)(14)(15)(16).…”