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. 2022 Apr 21:13:864549.
doi: 10.3389/fgene.2022.864549. eCollection 2022.

m6A Methylation Patterns and Tumor Microenvironment Infiltration Characterization in Clear-Cell Renal Cell Carcinoma

Tianming Ma  1   2 , Jiawen Wang  1   2 , Xiaodong Liu  1   2 , Wei Zhang  1 , Lingfeng Meng  1   2 , Yaoguang Zhang  1   2
Affiliations

m6A Methylation Patterns and Tumor Microenvironment Infiltration Characterization in Clear-Cell Renal Cell Carcinoma

Tianming Ma et al. Front Genet. .

Abstract

Increasing evidence suggests the essential regulation of RNA N6-methyladenosine (m6A) modification in carcinogenesis and immune response. Nevertheless, the potential impacts of these modifications on the tumor microenvironment (TME) immune cell infiltration characteristics in clear-cell renal cell carcinoma (ccRCC) remain unclear. Utilizing a consensus clustering algorithm, we determined three m6A modification patterns and identified three m6A-related gene clusters among 569 ccRCC samples, which were associated with different biological functions and clinical outcomes. Thereafter, the m6A score was constructed using m6A-associated signature genes to accurately exploit the m6A modification patterns within individual tumors. The m6A score was further demonstrated to be noticeably related to ccRCC prognosis. In addition, the m6A score was found to be strongly correlated with tumor mutational burden (TMB), microsatellite instability, immune infiltration, immune checkpoint expression, and immunotherapy response, which was also validated in the pan-cancer analyses. Our findings thoroughly elucidated that m6A modification contributes to tumor microenvironment immune-infiltrating characteristics and prognosis in ccRCC. Assessing the m6A modification patterns of individual patients with ccRCC will offer novel insights into TME infiltration and help develop more effective treatment strategies.

Keywords: N6-methyladenosine; clear-cell renal cell carcinoma; immune checkpoint inhibitors; pan-cancer; tumor microenvironment.

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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
Landscape of RNA N6-methyladenosine (m6A) regulators in clear-cell renal cell carcinoma (ccRCC). (A) The copy number variation (CNV) frequency of 23 m6A regulators in the ccRCC cohort. (B) The positions of CNV changes of m6A regulators. (C) The mutation frequency of m6A regulators in 336 ccRCC samples. (D) Differential expression levels of m6A genes. *p < 0.05; **p < 0.01; ***p < 0.001; ns, not significant.
FIGURE 2
FIGURE 2
m6A clusters and relevant tumor microenvironment (TME) characteristics. (A) Principal component analysis of three determined m6A clusters. (B) Unsupervised clustering of m6A regulators. (C) The infiltration of TME immune cells in distinct m6A clusters.
FIGURE 3
FIGURE 3
Construction of m6A-related gene signatures. (A) Venn diagram of 2,776 m6A-related differentially expressed genes. Consensus clustering cumulative distribution function (B) and delta area curves (C) with k = 2 to 9. (D) Consensus matrix. (E) Differential overall survival of three gene clusters. (F) Unsupervised clustering of m6A signature-related genes.
FIGURE 4
FIGURE 4
Establishment of the m6A score. Comparison of the m6A score between gene clusters (A) and m6A clusters (B). (C) The alluvial diagram displaying the changes in the m6A cluster, gene cluster, m6A score and survival outcome. (D) The percentage weight of survival status in low or high m6A score groups. (E) Distribution of m6A score in dead and surviving patients. (F) Overall survival analysis of m6A score groups. (G) The application of m6A score in the GSE22541 cohort.
FIGURE 5
FIGURE 5
Relationship between m6A score and tumor mutational burden (TMB). Microsatellite instability (MSI) (A) and TMB (B) status between m6A score groups. (C) Kaplan-Meier curves revealing the survival of the low and high TMB groups. (D) Kaplan-Meier curves classified by both m6A score and TMB. (E,F) OncoPrints indicating distinct mutation conditions.
FIGURE 6
FIGURE 6
Immunological characteristics in distinct m6A score groups. (A,B) Differential TICs fractions between m6A score groups using sGSEA or CIBERSORT algorithm. (C,D) Correlations of the m6A score with TICs analyzed using ssGSEA or CIBERSORT algorithm. (E) Differences of TME scores between the m6A score groups. *p < 0.05; **p < 0.01; ***p < 0.001; ns, not significant.
FIGURE 7
FIGURE 7
Asscociation of the m6A score with TME immune reaction. (A) Differential expression of six immune checkpoint genes between m6A score groups. (B) Correlation of m6A score with immune checkpoint-related gene expression. (C) Differential immunophenoscore between low and high m6A score groups. (D) Differential survival was compared in the GSE78220 cohort. (E) Differential drug sensitivity between the m6A score groups.
FIGURE 8
FIGURE 8
Performance of m6A score in 33 tumors. Univariable Cox regression analyses for overall survival (A) and disease-specific survival (B). The radar graphs of correlation of m6A score with TMB (C), MSI (D), PD-1 and PD-L1 expression (E,F). *p < 0.05; **p < 0.01; ***p < 0.001.
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