Abnormal N6-methyladenosine (m6A) modification levels caused by METTL3 have been identified to be a critical regulator in human cancers, and its roles in the immune microenvironment and the relationship between targeted therapy and immunotherapy sensitivity in gastric cancer (GC) remain poorly understood. In this study, we assessed the transcriptome-wide m6A methylation profile after METTL3 overexpression by m6A sequencing and RNA sequencing in BGC-823 cells. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to analyze the function of core targets of METTL3. Eighteen methylation core molecules were identified in GC patients by combining transcriptome and methylome sequencing. GC patients can be separated into two subtypes based on the expression of 18 methylation core molecules. Furthermore, subgroup analysis showed that patients with different subtypes had a different OS, PFS, stage, grade, and TMB. Gene set enrichment analysis (GSEA) showed that immune-related pathways were enriched among subtype A. The ESTIMATE analysis suggested that the extent of infiltration of immune cells was different in two subtypes of GC patients. Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA) database also showed that there were significant differences in the efficacy of immunotherapy among different types of GC patients. Altogether, our results reveal that METTL3-mediated m6A methylation modification is associated with the immune microenvironment and the effects of immunotherapy in GC patients. Our findings provide novel insights for clinicians in the diagnosis and optimal treatment of GC patients.

Objective. DNA damage response (DDR) is a complex system that maintains genetic integrity and the stable replication and transmission of genetic material. m6A modifies DDR-related gene expression and affects the balance of DNA damage response in tumor cells. In this study, a risk model based on m6A-modified DDR-related gene was established to evaluate its role in patients with gastric cancer. Methods. We downloaded 639 DNA damage response genes from the Gene Set Enrichment Analysis (GSEA) database and constructed risk score models using typed differential genes. We used Kaplan-Meier curves and risk curves to verify the clinical relevance of the model, which was then validated with the univariate and multifactorial Cox analysis, ROC, C -index, and nomogram, and finally this model was used to evaluate the correlation of the risk score model with immune microenvironment, microsatellite instability (MSI), tumor mutational burden (TMB), and immune checkpoints. Results. In this study, 337 samples in The Cancer Genome Atlas (TCGA) database were used as training set to construct a DDR-related gene model, and GSE84437 was used as external data set for verification. We found that the prognosis and immunotherapy effect of gastric cancer patients in the low-risk group were significantly better than those in the high-risk group. Conclusion. We screened eight DDR-related genes (ZBTB7A, POLQ, CHEK1, NPDC1, RAMP1, AXIN2, SFRP2, and APOD) to establish a risk model, which can predict the prognosis of gastric cancer patients and guide the clinical implementation of immunotherapy.

Objective: We aimed to construct a multi-immune gene model for the prognosis of colorectal cancer. This study would not only provide important clinical data for the evaluation of survival and prognosis of colorectal cancer, but provide insights into the tumor immune mechanisms.Methods: Colorectal cancer gene expression and clinicopathological data were downloaded from the TCGA database, and then we performed gene expression analysis to obtain differentially expressed genes. In addition, we downloaded immune genes from the ImmPort immune gene database, and obtained differentially expressed immune genes after intersection with the differentially expressed colorectal cancer genes. We further performed survival analysis of the differential immune genes to obtain prognosis-related genes, which were used to construct a multi-immune gene prognostic model. We then analyzed the impact of the prognostic model risk score on the survival of colorectal cancer patients through survival analysis, using ROC analysis. In addition, we performed risk curve analysis to validate the accuracy of the prognostic model risk score in assessing the prognosis of colorectal cancer, and also conducted independent prognostic analysis. Finally, we analyzed the correlation between the immune genes, and transcription factors as well as immune cells.Results: Our analysis showed that prognosis of the high-risk group as evaluated by the immune gene prognosis model risk score was poor (P<0.001). The prognostic model risk score could accurately classify the colorectal patients and has high accuracy in the analysis of prognosis of colorectal cancer (AUC=0.861). Our data demonstrated a certain correlation between the immune genes, transcription factors and immune cells.Conclusions: The constructed prognostic model could accurately assess the prognosis and survival of patients with colorectal cancer. Immune genes might regulate malignant progression of tumors by modulating the production of transcription factors and immune cells. This study demonstrated the influence of immune factors on the prognosis of colorectal cancer and provided a reference for further studies evaluating the role of immunity in the development of colorectal cancer.