Identification of m6A modification patterns and development of m6A–hypoxia prognostic signature to characterize tumor microenvironment in triple-negative breast cancer - Shen, Xi; Zhong, Jianxin; He, Jinlan; Han, Jiaqi; Chen, Nianyong (Frontiers in Immunology, 2022)
N6-methylation (m6A), the most common epigenetic modification of RNA, has been found to have essential effects on aspects of the tumor microenvironment including hypoxia status and mobilization of immune cells. However, the mechanism underlying the roles of m6A modification in hypoxia and immune infiltration in triple-negative breast cancer remains unclear. We characterized 46 m6A-related genes by Spearman analysis and used non-negative matrix factorization algorithms to cluster TNBC samples from The Cancer Genome Atlas (TCGA-TNBC, N=139); the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC-TNBC, N=297); the GSE103091, GSE21653, and GSE135565 series from the Gene Expression Omnibus (GEO-TNBC, N=247); and GSE118527 (FUSCCTNBC, N=245). Immune cell infiltration was analyzed and a TME signature was identified using the CIBERSORT algorithm and single-sample gene set enrichment analysis. An m6A–hypoxia signature and risk score were developed using LASSO-Cox analysis. Results: Based on the expression of m6A-related genes, we identified three distinct m6A clusters (denoted A, B, and C) in TNBC samples. Comparing the TME characteristics among the three clusters, we observed that cluster C had more stromal and metabolism activity, whereas clusters A and B displayed more immune cell infiltration. Based on the strong relationship between m6A modification clusters and the hypoxia signature, the genes related to m6A modification and hypoxia were used to cluster TNBC samples into two gene clusters and to characterize infiltrating immune cells. To further explore the clinical features of hypoxia and m6A, we constructed an m6A–hypoxia signature to divide samples into high- and low-risk groups; we found significant differences in prognosis between the two groups, and established a nomogram to predict patients' overall survival. In addition, we identified different TME features between the two groups, and a better immunotherapy response was observed in the low-risk group, which had higher frequencies of somatic mutations and higher tumor mutational burden. We identified distinct clusters based on differences in expression of m6A genes in TNBC samples and found a relationship between m6A modification and TME characteristics. Then, we constructed a specific m6A–hypoxia signature for TNBC to evaluate risk and predict immunotherapy response of patients, to enable more accurate treatment in the future.