Analysis of m7G-Related Signatures in the Tumor Immune Microenvironment and Identification of Clinical Prognostic Regulators in breast cancer

Breast cancer is a malignant tumor that seriously threatens the life and health of women and exhibits high inter-individual heterogeneity, emphasizing the need for more in-depth research on its pathogenesis. It is well-established that internal 7-Methylguanosine (m7G) modifications affect RNA processing and function and are thought to be involved in human diseases. However, little is currently known about the role of m7G modification in breast cancer. In this study, we elucidated the expression, copy number variation incidence, and prognostic value of the 24 m7G-related genes (m7GRGs) in breast cancer. Subsequently, based on the expression of 24 m7GRGs, consensus clustering divided tumor samples from the TCGA-BRCA dataset into 4 subtypes with significantly different immune cell infiltration and stromal scores. Differentially expressed genes between subtypes were mainly enriched in immune-related pathways such as Ribosome, TNF signaling pathway, and Salmonella infection. Support vector machines and multivariate cox regression analysis were performed based on these 24 m7GRGs, and 4 m7GRGs (AGO2, EIF4E3, DPCS, and EIF4E) were identified for constructing the prediction model. Next, we constructed a nomogram model based on the risk model and clinical factors. The ROC curve indicated that the nomogram model had a strong ability to predict the prognosis of breast cancer. The prognosis of patients in the high- and low-TMB groups was significantly different (p = 0.03). Moreover, the 4-gene signature could predict response to chemotherapy. Therefore, this study found that the m7GRGs signature in the immune microenvironment of breast cancer affects the prognosis of breast cancer patients. Our study provides a reference for applying m7GRGs signature to the personalized treatment of breast cancer patients and exploring the mechanism of breast cancer progression.