Computer methods and programs in biomedicine · 2026
Integrating machine learning of bulk and single cell RNA data to characterize immune mechanism of m6A related mitophagy genes in malignant gastric cancer.
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Yan Liting, Sun Jingying, Feng Yangmeng, Yang Xiaoli, Xu Cuixiang, Wang Jianhua
Аннотация
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.
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.