Identifying subtypes and developing prognostic models based on N6-methyladenosine and immune microenvironment related genes in breast cancer

Abstract

Breast cancer (BC) is the most prevalent cancer in women globally. The tumor microenvironment (TME), comprising epithelial tumor cells and stromal elements, is vital for breast tumor development. N6-methyladenosine (m6A) modification plays a key role in RNA metabolism, influencing its various aspects such as stability and translation. There is a notable link between m6A methylation and immune cells in the TME, although this relationship is complex and not fully deciphered. In this research, BC expression and clinicopathological data from TCGA were scrutinized to assess expression profiles, mutations, and CNVs of 31 m6A genes and immune microenvironment-related genes, examining their correlations, functions, and prognostic impacts. Lasso and Cox regression identified prognostic genes for constructing a nomogram. Single-cell analyses mapped the distribution and patterns of these genes in BC cell development. We investigated associations between gene-derived risk scores and factors like immune infiltration, TME, checkpoints, TMB, CSC indices, and drug response. As a complement to computational analyses, in vitro experiments were conducted to confirm these expression patterns. We included 31 m6A regulatory genes and discovered a correlation between these genes and the extent of immune cell infiltration. Subsequently, a 7-gene risk score was generated, encompassing HSPA2, TAP1, ULBP2, CXCL1, RBP1, STC2, and FLT3. It was observed that the low-risk group exhibited better overall survival (OS) in BC, with higher immune scores but lower tumor mutational burden (TMB) and cancer stem cell (CSC) indices, as well as lower IC50 values for commonly used drugs. To enhance clinical applicability, age and stage were incorporated into the risk score, and a more comprehensive nomogram was constructed to predict OS. This nomogram was validated and demonstrated good predictive performance, with area under the curve (AUC) values for 1-year, 3-year, and 5-year OS being 0.848, 0.807, and 0.759, respectively. Our findings highlight the profound impact of prognostic-related genes on BC immune response and prognostic outcomes, suggesting that modulation of the m6A-immune pathway could offer new avenues for personalized BC treatment and potentially improve clinical outcomes.

Keywords: Breast cancer; Gene signature; Immune microenvironment; N6-methyladenosine; Prognosis.

Figure 1

Flow chart of the study design.

Figure 2

The m6A Regulator Landscape in Breast Cancer (A) Comparison of M6A regulatory gene between tumor and normal group. (B) Correlation analysis of M6A regulatory genes. (C) Frequencies of CNV gain and loss among M6A regulatory genes. (D) Expression differences of M6A regulatory genes in tumor tissues and normal tissues. (E) Association between the abundance of immune cells and M6A regulatory factors (t-test, ****P < 0.0001; ***P < 0.001; **P < 0.01; *P < 0.05).

Figure 3

Identification of m6A Subgroup in Breast Cancer. (A, B) A consensus matrix heat map defining three M6a related subtypes (k = 3). (C) Kaplan–Meier analysis of three subtypes of OS. (D) PCA analysis of three M6A subtypes. (E) The gene expression level of three M6A subtypes.

Figure 4

Characteristics of the Biological Behavior in m6A Subgroups. (A) GSVA analysis of three M6A subtypes. (B) Wayne diagram of three subtypes of GSVA analysis. (C) Immune cell infiltration of three M6A subtypes. (D, E) GO (D) and KEGG (E) enrichment analysis of DEGs among three M6A subtypes.

Figure 5

Construction of m6A-Immune-Related Prognostic Risk Score. (A, B) Lasso regression analysis on the prognosis-related genes. (C) multivariate Cox regression analysis. (D) OS analysis of two risk groups using Kaplan–Meier. (E) ROC curves to predict 1, 3, and 5-year OS according to the risk score in the training cohort. (F) The difference of risk score among three m6A gene subtypes. (G) The sankey diagram of the sample distribution of three gene subtypes and two risk score groups.

Figure 6

Development and Validation of a Prognostic Nomogram for Breast Cancer. (A) A nomogram used to predict BC OS. (B) ROC curves to predict 1-, 3-, and 5year OS according to the nomogram in the training cohort. (C) ROC curves when clinical indicators are used alone. (D) The C-index of the nomogram. (E) ROC curves to predict 1-, 3-, and 5year OS according to the nomogram in the testing cohort.

Figure 7

TME and immune checkpoint characteristics in both risk score groups. (A) Association of risk score with immune cell infiltration. (B) Association between risk score and TME score. (C) Association between immune cell infiltration and seven genes in the risk_score model. (D–G) Immunotherapy effect in the low- and low-risk groups.

Figure 8

TMB, CSC index and drug susceptibility analysis among two risk_score groups. (A) Correlation between risk score and TMB. (B) Correlation between risk score and CSC index. (C–I) Correlation between risk score and drug susceptibility. CSC, cancer stem cells; TMB, tumor mutational burden.

Figure 9

Single Cell Verification of the Distribution of m6A-Immune Prognostic Genes in Breast Cancer. (A) tSNE and UMAP projections of breast cancer cells in GSE176078. Different cell types are indicated by unique colors. (B) Delineating the distribution of key genes in cell subsets.

Figure 10

Pseudotime analysis of T cells in triple-negative breast cancer samples from GSE176078. (A) All cells analyzed were T cells. (B) Differences in the timing of T cell differentiation. The darker blue represents the earlier differentiation stage, while the lighter blue represents the later differentiation stage. (C) Five stages of T cell differentiation. State 1 is the earliest stage of differentiation. (D) Expression levels of key genes at different stages of T cell development.

Figure 11

Validation of prognosis-related genes expression. (A) RT-PCR was used to detect the mRNA expression of seven prognosis-related M6a-immune genes in breast cancer cells and normal breast epithelial cells. (B) mRNA expression of 7 prognostic-related M6a-immune genes from BC patients and corresponding normal tissues (t-test, ***P < 0.001; **P < 0.01; *P < 0.05).