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. 2022 Feb 10:10:807129.
doi: 10.3389/fcell.2022.807129. eCollection 2022.

Links Between N 6-Methyladenosine and Tumor Microenvironments in Colorectal Cancer

Yundi Zhang  1 , Ke Zhang  2 , Haoming Gong  3 , Qin Li  4 , Lajie Man  3 , Qingchang Jin  3 , Lin Zhang  5 , Song Li  6
Affiliations

Links Between N 6-Methyladenosine and Tumor Microenvironments in Colorectal Cancer

Yundi Zhang et al. Front Cell Dev Biol. .

Abstract

N 6-methyladenosine (m6A) is a critical epigenetic modification for tumor malignancies, but its role in regulating the tumor microenvironments (TMEs) has not been fully studied. By integrating multiple data sets and multi-omics data, we comprehensively evaluated the m6A "writers," "erasers," and "readers" in colorectal cancer and their association with TME characteristics. The m6A regulator genes showed specific patterns in co-mutation, copy number variation, and expression. Based on the transcriptomic data of the m6A regulators and their correlated genes, two types of subtyping systems, m6AregCluster and m6AsigCluster, were developed. The clusters were distinct in pathways (metabolism/inflammation/extracellular matrix and interaction), immune phenotypes (immune-excluded/immune-inflamed/immune-suppressive), TME cell composition (lack immune and stromal cells/activated immune cells/stromal and immune-suppressive cells), stroma activities, and survival outcomes. We also established an m6Ascore associated with molecular subgroups, microsatellite instability, DNA repair status, mutation burdens, and survival and predicted immunotherapy outcomes. In conclusion, our work revealed a close association between m6A modification and TME formation. Evaluating m6A in cancer has helped us comprehend the TME status, and targeting m6A in tumor cells might help modulate the TME and improve tumor therapy and immunotherapy.

Keywords: N6-methyladenosine; colorectal cancer; immunotherapy; molecular classification; tumor microenvironments.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Workflow and landscapes of m6A regulators. (A) Workflow chart of this study with the main process. Cohorts used in this study are underlined. (B) Mutation rates of m6A regulators in The Cancer Genome Atlas (TCGA) data set. (C) Mutation co-occurrence analysis of m6A regulators in the TCGA data set. Co-occurrences with statistical significance (p < 0.05 and <0.001) are shown. (D) Copy number variants in the TCGA data set. (E) Expression levels of m6A regulators in normal and tumor tissues. (F) Principal component analysis for RNA level of 24 m6A regulators in the TCGA data set. *p < 0.05; **p < 0.01; ***p < 0.001.
FIGURE 2
FIGURE 2
Clustering of m6A regulator–based subtypes in meta-data of six Gene Expression Omnibus cohorts. (A) Hazard ratio of m6A regulators in predicting survivals in CRC patients. (B) Interaction among m6A regulators in colorectal cancer. Line colors represent positive or negative correlation, and thickness represents correlation strength. Colored circles indicate the types of m6A regulators, and circle sizes indicate prognostic ability. (C) Unsupervised clustering based on 24 m6A regulators. Three clusters, termed m6AregC1–3, were defined. (D–E) Differential biological pathways between m6A regulator–based clusters. The pathways were quantified by gene set variation analysis enrichment and compared between C1 and C2 (D) and C2 and C3 (E). (F) Abundance of tumor-infiltrating cells in three subtypes. (G) Enrichment of stroma-activated pathways in three subtypes. One-way ANOVA tests compared the three groups in (F,G). *p < 0.05; **p < 0.01; ***p < 0.001.
FIGURE 3
FIGURE 3
Association between m6A regulator–based subtypes and tumor microenvironment composition. (A) Unsupervised clustering based on m6A regulators with n = 2 to 5 in the GSE39582 data set. (B) Clustering m6A regulators into three subtypes. Distribution of molecular subtypes (chromosomal instability, CpG island methylator phenotype, and microsatellite instability) and driver mutations (KRAS, RBAF, and TP53) were provided. (C) Distribution of genetic change types in three m6A regulator–based subtypes. (D) Kaplan–Meier curves of the three m6A regulator–based subtypes.
FIGURE 4
FIGURE 4
Construction of m6A signature–based clusters. (A) Overlaps of differential expression genes among the three m6A regulator–based subtypes. (B) Gene Ontology enrichment of the m6A signature genes. (C) Clustering patients based on m6A signature genes into three subtypes termed m6AsigC1–3. (D) Alluvial diagram connecting m6AregClusters, m6AsigClusters, gene mutation subtypes, and m6Ascores. (E) Kaplan–Meier curves of the three m6A signature–based subtypes. (F) Expression levels of m6A regulators in three m6A signature–based subtypes. (G–I) Signatures of stromal activation (G), immune activation (H), and immune checkpoints (I) in three m6A signature–based subtypes. *p < 0.05; **p < 0.01; ***p < 0.001.
FIGURE 5
FIGURE 5
Characteristics of m6Ascore in colorectal cancer. (A,B) m6Ascores in m6A regulator–based (A) and signature–based (B) clusters. (C) Correlations between m6Ascores and gene signatures in colorectal cancer. (D) Levels of stromal activity in patients with high and low m6Ascores. (E,F) Distribution of m6Ascores in patients with different genomic change subtypes and clinical features. (G) Kaplan–Meier curves of patients with high and low m6Ascores. (H) Forest plot showing multivariable COX results of m6Ascore and clinical features in predicting death. *p < 0.05; **p < 0.01; ***p < 0.001.
FIGURE 6
FIGURE 6
Validation of m6Ascores in The Cancer Genome Atlas (TCGA) cohorts. (A,B) m6Ascores in patients with different molecular subtypes (A) and clinical features (B). (C) Genomic mutation rates of the top 20 genes in patients with high and low m6Ascores. (D) Correlation between m6Ascores and tumor mutation burdens. (E) Tumor mutation burdens in patients with high and low m6Ascores. (F) Kaplan–Meier curves of m6Ascores.
FIGURE 7
FIGURE 7
Prediction values of m6Ascores in six Gene Expression Omnibus data sets. (A,B) Recurrence-free survival (A) and overall survival (B) of patients with high and low m6Ascores in six data sets. (C–G) Kaplan–Meier curves of patients with high and low m6Ascores in GSE17536 (C), GSE29621 (D), GSE33113 (E), GSE37892 (F), and GSE38832 (G). (H,I) ROC curves of recurrence-free survival (H) and overall survival (I) in six data sets.
FIGURE 8
FIGURE 8
The ability of m6Ascore to predict responses to ICI. (A) PD-L1 expression in patients with high and low m6Ascores in the IMvigor210 cohort. (B) Stromal activation signatures in patients with high and low m6Ascores. (C) m6Ascores in the ignored, excluded, and inflamed types of tumors. (D,E) Kaplan–Meier curves (D) and response rates (E) in patients with high and low m6Ascores after treatment of ICI. (F) m6Ascores in patients with different responses to ICI. Data of (A–F) were from the IMvigor210 cohort. (G,H) Kaplan–Meier curves (G) and response rates (H) to ICI in patients with high and low m6Ascores after treatment of ICI in the GSE78220 cohort. (I) m6Ascores in patients with different responses to ICI in the GSE78220 cohort. (J) Graphic abstract of this study (top) and characteristics of the subtypes (bottom). *p < 0.05; **p < 0.01; ***p < 0.001.
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