The N6-methyladenosine methylation landscape stratifies breast cancer into two subtypes with distinct immunological characteristics.
Yang ChenYijiang HouShuguang LiWenxing QinJian ZhangPublished in: Clinical and experimental pharmacology & physiology (2024)
N6-methyladenosine (m6A) methylation modification affects the tumorigenesis and metastasis of breast cancer (BC). This study investigated the association between m6A regulator-mediated methylation modification patterns and characterization of the tumour microenvironment in BC, as well as their prognostic importance. Public gene expression data and clinical annotations were collected from The Cancer Genome Atlas (TCGA) database, the Gene Expression Omnibus website and the METABRIC program. We analysed the genetic expression, gene-gene interactions, gene mutations and copy number variations using R software. The data were screened for risk genes using the Cox risk regression model, and we developed an algorithm for risk score and its predictive value. Compared to adjacent normal tissue, we identified 16 differentially expressed m6A regulators in BC, including six writers and 10 readers. Under unsupervised clustering, two distinguished modification patterns were identified, cluster C1 and C2. Compared to m6A cluster C2, cluster C1 was found to be more involved in immune-related pathways, with a relatively higher immune score and stromal score (P < 0.05). Patients were divided into two groups based on their risk scores for survival analysis. The patients in the high-risk score group had significantly worse overall survival than patients in the low-risk score group, (P < 0.0001). The TCGA database validation revealed the same prognostic tendency. In summary, our study showed distinct m6A regulator modification patterns contribute to the immunological heterogeneity and diversity of BC. The development of m6A gene signatures and the m6A score aid in the prognostic prediction of patients with BC.
Keyphrases
- genome wide
- copy number
- gene expression
- end stage renal disease
- dna methylation
- newly diagnosed
- ejection fraction
- chronic kidney disease
- mitochondrial dna
- peritoneal dialysis
- machine learning
- healthcare
- poor prognosis
- bone marrow
- transcription factor
- young adults
- emergency department
- deep learning
- patient reported outcomes
- squamous cell carcinoma
- artificial intelligence
- big data
- mental health
- free survival
- binding protein
- adverse drug