Identification of m6A modification patterns and development of m6A–hypoxia prognostic signature to characterize tumor microenvironment in triple-negative breast cancer

Abstract

BackgroundN6-methylation (m6A) modification of RNA has been found to have essential effects on aspects of the tumor microenvironment (TME) including hypoxia status and mobilization of immune cells. However, there are no studies to explore the combined effect of m6A modification and hypoxia on molecular heterogeneity and TME of triple-negative breast cancer (TNBC).MethodsWe collected 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 FUSCCTNBC (N=245) for our study. The non-negative matrix factorization algorithm was used to cluster TNBC samples. Immune cell infiltration was analyzed by the CIBERSORT algorithm. The enrichment scores were calculated by single-sample gene set enrichment analysis(ssGSEA) to characterize TME in TNBC samples. Immunohistochemistry (IHC) and qRT-PCR were performed to detect the gene expression.ResultsBased 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 was strongly related to hypoxia status and immune suppression, whereas clusters A and B displayed more immune cell infiltration. Therefore, we combine m6A and hypoxia related genes to classify two m6A-hypoxia clusters of TNBC and screened six prognostic genes by LASSO-Cox regression to construct a m6A-hypoxia signature(MHPS), which divided TNBC samples into high- and low-risk groups. We identified different TME features, immune cell infiltration between the two groups, and a better immunotherapy response was observed in the low-risk group. A nomogram was constructed with tumor size, lymph node, and risk score to improve clinical application of MHPS.ConclusionWe identified distinct TME characteristics of TNBC based on three different m6A modification patterns. 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.

METTL14 enhances the m6A modification level of lncRNA MSTRG.292666.16 to promote the progression of non-small cell lung cancer

“…There is growing evidence that m6A modifications play an integral role in inflammation, innate immunity, and antitumor effects through interactions with a variety of m6A regulators. Shen et al [ 22 ] identified three distinct m6A clusters in triple-negative breast cancer (TNBC) through bioinformatics analysis, and then constructed a specific m6A-hypoxia signature to assess risk and predict the immunotherapy response of patients, thereby enabling more accurate treatment of TNBC in the future. Another study demonstrated that ECE2 , a prognostic biomarker of lung adenocarcinoma (LUAD), was found to have negative correlation with m6A modification-associated genes ( HNRNPC , IGF2BP1 , IGF2BP3 and RBM1 ), which suggested that ECE2 may affect the tumor progression of LUAD by influencing the methylation level of m6A [ 23 ].…”

Emerging insights into keratin 7 roles in tumor progression and metastasis of cancers

“…The modification of N6-methyladenosine (m6A) is a prevalent epigenetic regulation in mammals and has been shown to have a substantial impact on the metastasis of various cancers, specifically BC ( 32 ). The effects of m6A are extensive, influencing numerous RNA metabolic processes, such as mRNA degradation, stability, and translation.…”

Overview of triple negative breast cancer prognostic signatures in the context of data science-driven clinico-genomics research

“…Two DMS were related to high risk (cg21234506 and cg21580376; HR >1), and three DMS were protective (cg15724876, cg17887364, and cg19419246; HR <1). Interestingly, a recent study (25) on TME of TNBC associated m 6 A modification and hypoxia status, identifying 26 genes related to both regulation types and characterizing two clusters, one being with significantly worse prognosis. A 6-gene prognostic signature was identified (PIM2, PET117, SMARCA5, TAF9, ABCB10, MKP1) among the m 6 A modification-hypoxia genes to evaluate risk and predict ImT response of patients.…”

“…In study of Liu et al (29), bioinformatics and network analyses explored the role of alternative splicing events (ASE) events and their correlation with TNBC prognostic DEGs, delivering ASE profiles, prognostic interaction networks, and splice factor-AS interaction networks. Finally, the use of ML was central to (30) where first a Recursive Feature Elimination (RFE) algorithm identified signatures (20,25,30,35,40,45, and 50 genes) differentiating TNBC from other subtypes, then XGBoost was found the best performer for 45 genes (mixed signatures), of which 34 genes differentially regulated, 4 specifically relevant for distant metastasis free survival, and 2 potentially prognostic (POU2AF1 and S100B) associated with MAPK, PI3-AkT, Wnt, TGF-β, and other signal transduction pathways involved in metastasis. In study of Gong et al (31), 151 TNBC patients obtained from the TCGA SpliceSeq database showed relevance for the Exon Skip (ES) type of AS events, more robust in predicting performance in TNBC prognosis.…”