Integrating machine learning of bulk and single cell RNA data to characterize immune mechanism of m6A related mitophagy genes in malignant gastric cancer.

BACKGROUND AND

OBJECTIVE

Gastric cancer is a heterogeneous and complicated epithelial cancers. Chronic H. pylori and EBV infection, as well as intestinal microbiota exposure make gastric cancer encountered a complex tumor immune microenvironment. Mitophagy and m6A are deeply involved in immune microenvironment in the development of tumors.

METHODS

We used integrating machine learning of bulk and single cell RNA sequencing to explore the immune mechanisms of m6A related mitophagy genes (MRMGs) in gastric cancer. RT-qPCR and immunochemistry were used to verify gene expression.

RESULTS

Prognostic model that involves a total of 20 DE-MRMGs exhibited a performance property in prognosis, immunotherapy prediction and tumor mutation burden in patients with gastric cancer. And significant difference between high-risk group and low-risk group focus on T cells which clarified in both bulk RNA and single cell RNA data. In terms of mechanism, vimentin may participates in T cell differentiation of malignant gastric cancer. Meanwhile, vimentin expression in patients display a significant increasing in low differentiated gastric cancer than high differentiated gastric cancer.

CONCLUSIONS

Vimentin may be a diagnostic marker to draw the distinction between low and high differentiated gastric cancer in the mechanism of probably affecting T cell differentiation.