The N6-methyladenosine:mechanisms, diagnostic value, immunotherapy prospec-ts and challenges in gastric cancer.

The N6-methyladenosine (m6A) RNA modification is important in post-transcriptional regulation of RNA and are regulated reversibly by methyltransferases (writers), demethylases (erasers) and m6A recognition proteins (readers). Changes in the structure and function of key RNAs contribute to the development of diseases, particularly tumors. Many abnormal expressions of molecules related to m6A RNA methylation modification are discovered in gastric cancer (GC), which changes the methylation level and stability of target genes after transcription, and then regulates related metabolic pathways, affecting the occurrence and progression of GC. Therefore, an in-depth study of m6A RNA modification in GC is conducive to the development of new tumor therapies and the achieve of individualized treatment. At present, both basic and clinical studies indicate that m6A plays a complex and contentious role in GC. In this paper, we not only review the roles and mechanisms of m6A modified related proteins, but also discuss the value of m6A modulators in the clinical applications and current challenges of GC, aiming to provide research clues for the early diagnosis and explore the feasibility of m6A related proteins as specific targets for GC immunotherapy.

Construction and Validation of a Novel Prognostic Signature for Intestinal Type of Gastric Cancer.

BACKGROUND: Intestinal type of gastric cancer (IGC) is the largest subtype of gastric cancer (GC) by Lauren classification. The purpose of this present study was to construct a prognostic signature for IGC patients, based on the high-grade dysplasia (HGD) and IGC tissues, to improve and enhance the prognostic accuracy. METHODS: The microarray datasets and associated clinical characteristics of HGD and IGC were obtained from the Gene Expression Omnibus (GEO) database. Based on the differential expression analysis between HGD and IGC, the prognostic-related differential expression genes (DEGs) were identified in a training set by univariate COX regression analysis. The least absolute shrinkage and selection operator (LASSO) regression was used to construct an optimal prognostic signature. The enrichment analysis was performed by using Gene Set Enrichment Analysis (GSEA). The performance of the nomogram was assessed by the calibration curve and concordance index (C-index). The results were validated by using a testing set. RESULTS: We identified 35 prognostic-related DGEs in the training set. The nine-gene signature was established by LASSO analysis. The nine-gene signature was an independent risk factor in both the training and testing sets. The areas under the curve (AUC) values of receiver operating characteristic (ROC) analysis were 0.733 and 0.700 for the training and testing sets, respectively. In GSEA analysis, the gene expression in high-risk group was enriched in hedgehog signaling, epithelial mesenchymal transition, and angiogenesis. The nomogram for IGC showed good performance with C-index of 0.81 (95% CI: 0.76-0.86) and 0.70 (95% CI: 0.63-0.77) in the training and testing sets, respectively. CONCLUSION: We identified and verified a nine-gene signature for the prognostic prediction of IGC patients, which might identify subgroups of IGC patients and select more suitable therapeutic options.