Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics Data

Activation of hypermethylated P2RY1 mitigates gastric cancer by promoting apoptosis and inhibiting proliferation

The P2RY1 receptor is known to cause cancer by activating the ERK signal pathway, and its DNA methylation status and corresponding regulatory mechanism remain unknown. This study used the DNA methylation chip to profile the genome-wide DNA methylation level in gastric cancer tissues. The proliferation and apoptosis of the SGC7901 gastric cancer cell line were determined after treatment with a selective P2RY1 receptor agonist, MRS2365. The promoter region of P2RY1 was found to be highly methylated with four hypermethylated sites (|Δβ value| > 0.2) in diffuse gastric cancer and was validated by bioinformatics analysis in the TCGA database. Also, immunohistochemical staining data obtained from the HPA database demonstrated the downregulated expression of proteins encoded by P2RY1 in stomach cancer tissue. The analysis of MRS2365-treated cells by annexin V/propidium iodide staining and caspase-3 activity assays indicated the induction of apoptosis in SGC7901 cells. The P2RY1 receptor activation in human SGC7901 gastric cancer cells via the MRS2365 agonist induced apoptosis and reduced cell growth. High DNA methylation in the promoter region of P2RY1 might have contributed to the reduced expression of P2RY1's mRNA, which was likely responsible for the "aggressive" nature of the diffuse gastric cancer.

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA.