Classification and immune invasion analysis of breast cancer based on m6A genes

Abstract

Background: Breast cancer (BRCA) shows genetic, epigenetic, and phenotypic diversity. Methylation of N6-methyladenosine (m6A) affects the occurrence, development, and therapeutic efficacy of BRCA.However, the characteristics and prognostic value of m6A in BRCA remain unclear. We aimed to classify and construct a scoring system for the m6A regulatory gene in BRCA, and to explore its potential mechanisms.Methods: In this study, we selected 23 m6A regulatory genes and analyzed their genetic variation in BRCA, including copy number variation (CNV) data, expression differences, mutations, gene types, and correlations between genes. Survival curves were drawn by the Kaplan-Meier method, and a log-rank P<0.05 was considered statistically significant. The partitioning around medoids (PAM) algorithm was used for molecular subtype analysis of m6A, single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to quantify the relative infiltration levels of various immune cell subgroups, and a scoring system was built based on principal component analysis (PCA).Results: In BRCA, m6A regulatory gene mutation frequency is not high, while that of CNV mutation is high, which is related to gene expression and closely related to prognosis. In this study, we identified 3 different m6A subtypes, which are closely related to the level of immune cell infiltration. We further constructed an m6A score system, in which lower scores were correlated with low tumor mutation burden (TMB), later clinical staging, programmed cell death 1 ligand 1 (PD-L1) expression, and triple-negative breast cancer (TNBC). Conclusions: This study highlights the diversity and complexity of the role of m6A in BRCA. The classification of BRCA based on the m6A regulatory gene can help us understand the characteristics of BRCA and help develop individualized immunotherapy regimens.

Identification of hub genes and potential ceRNA networks of diabetic cardiomyopathy

“…We used the WGCNA package (version 1.60) in R to find and combine highly correlated mRNAs into mRNA modules, which permitted an examination for correlation between each module and diabetic complication-prone 25 . Before using the WGCNA, we used surrogate variable analysis (sva) package and object-oriented microarray and proteomics analysis (oompaBase) package to remove the batch effect between different samples 26 . The power of β = 5 (scale-free R 2 = 0.8) was selected as the soft threshold to ensure a scale-free network (Figure S1 ).…”

Establishment of a N1-methyladenosine-related risk signature for breast carcinoma by bioinformatics analysis and experimental validation

“…Considering the important role of various CNV regions in lymph-node metastasis in triple-negative breast cancer patients [ 23 ], the genetic variation of N6-methyladenosine (m6A)-related regulators, that is, CNV data, expression differences, and mutations, has been investigated in BRCA [ 24 ]. In the current study, similarly, the R package was employed for the 10 m 1 A-related regulators to initially seek their CNV types and frequencies and chromosomal assignments.…”

One Stone, Two Birds: N6-Methyladenosine RNA Modification in Leukemia Stem Cells and the Tumor Immune Microenvironment in Acute Myeloid Leukemia

“…m 6 A modification is also pivotal for the self-renewal of LSCs ( 21 ). In addition, m 6 A modification also involves the modulation of the TIME in various cancers, as well as in AML ( 29 – 32 ).…”