ArticleBMC cancer2023
Analysis of m7G-Related signatures in the tumour immune microenvironment and identification of clinical prognostic regulators in breast cancer.
Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Expression Profile and Prognostic Significance of Pivotal Regulators for N7-Methylguanosine Methylation in Diffuse Large B-Cell Lymphoma.Molecular biotechnology · 2025Article
- N7-methylguanosine (m7G) modification in breast cancer: clinical significances and molecular mechanisms.Cancer cell international · 2025Review
- Analyses of the roles and potential targets of m7G-related genes in colorectal cancer using single-cell and bulk RNA sequencing data.PloS one · 2025Article
- Combined signature of N7-methylguanosine regulators with their related genes and the tumor microenvironment: a prognostic and therapeutic biomarker for breast cancer.Frontiers in immunology · 2023Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundBreast cancer is a malignant tumour that seriously threatens women's life and health and exhibits high inter-individual heterogeneity, emphasising the need for more in-depth research on its pathogenesis. While internal 7-methylguanosine (m7G) modifications affect RNA processing and function and are believed to be involved in human diseases, little is currently known about the role of m7G modification in breast cancer. METHODS AND
resultsWe elucidated the expression, copy number variation incidence and prognostic value of 24 m7G-related genes (m7GRGs) in breast cancer. Subsequently, based on the expression of these 24 m7GRGs, consensus clustering was used to divide tumour samples from the TCGA-BRCA dataset into four subtypes based on significant differences in their immune cell infiltration and stromal scores. Differentially expressed genes between subtypes were mainly enriched in immune-related pathways such as 'Ribosome', 'TNF signalling pathway' and 'Salmonella infection'. Support vector machines and multivariate Cox regression analysis were applied based on these 24 m7GRGs, and four m7GRGs-AGO2, EIF4E3, DPCS and EIF4E-were identified for constructing the prediction model. An ROC curve indicated that a nomogram model based on the risk model and clinical factors had strong ability to predict the prognosis of breast cancer. The prognoses of patients in the high- and low-TMB groups were significantly different (p = 0.03). Moreover, the four-gene signature was able to predict the response to chemotherapy.
conclusionsIn conclusion, we identified four different subtypes of breast cancer with significant differences in the immune microenvironment and pathways. We elucidated prognostic biomarkers associated with breast cancer and constructed a prognostic model involving four m7GRGs. In addition, we predicted the candidate drugs related to breast cancer based on the prognosis model.
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