Comprehensive Evaluation of the m6A Regulator Prognostic Risk Score in the Prediction of Immunotherapy Response in Clear Cell Renal Cell Carcinoma

Mingke Yu, Xuefei Liu, Han Xu, Sangyu Shen, Fajiu Wang, Dajin Chen, Guorong Li, Zongping Wang, Zhixiang Zuo, An Zhao, Mingke Yu, Xuefei Liu, Han Xu, Sangyu Shen, Fajiu Wang, Dajin Chen, Guorong Li, Zongping Wang, Zhixiang Zuo, An Zhao

Abstract

Background: Clear cell renal cell carcinoma (ccRCC) is known for its high drug resistance. The tumor-immune crosstalk mediated by the epigenetic regulation of N6-methyladenosine (m6A) modification has been demonstrated in recent studies. Therefore, m6A modification-mediated immune cell infiltration characteristics may be helpful to guide immunotherapy for ccRCC.

Methods: This study comprehensively analyzed m6A modifications using the clinical parameters, single-cell RNA sequencing data, and bulk RNA sequencing data from the TCGA-ccRC cohort and 13 external validation cohorts. A series of bioinformatic approaches were applied to construct an m6A regulator prognostic risk score (MRPRS) to predict survival and immunotherapy response in ccRCC patients. Immunological characteristics, enriched pathways, and mutation were evaluated in high- and low-MRPRS groups.

Results: The expressional alteration landscape of m6A regulators was profiled in ccRCC cell clusters and tissue. The 8 regulator genes with minimal lambda were integrated to build an MRPRS, and it was positively correlated with immunotherapeutic response in extent validation cohorts. The clinicopathological features and immune infiltration characteristics could be distinguished by the high- and low-MRPRS. Moreover, the MRPRS-mediated mutation pattern has an enhanced response to immune checkpoint blockade in the ccRCC and pan-cancer cohorts.

Conclusions: The proposed MRPRS is a promising biomarker to predict clinical outcomes and therapeutic responses in ccRCC patients.

Keywords: N6-methyladenosine; clear cell renal cell carcinoma; immune infiltration characteristic; immunotherapy; mutation; prognosis.

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.

Copyright © 2022 Yu, Liu, Xu, Shen, Wang, Chen, Li, Wang, Zuo and Zhao.

Figures

Figure 1
Figure 1
(A) Expression of 23 m6A RNA methylation regulators between renal cancer and normal tissues in the TCGA-ccRCC cohort. (B, C) The UMAP plot and overview of epithelial cells by the origin and cell type of the cells. (D) Composition of various epithelial cells in different immunotherapeutic responses. (E) The heatmap of marker gene expression in 7 identified epithelial cell subsets. (F) Dot plot analysis of KEGG pathway enrichment of 7 epithelial cell subsets. (G) Violin plots showing the partial expression of m6A regulators for each epithelial cell type ("*p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns, not significant).
Figure 2
Figure 2
(A) LASSO coefficient profiles of the 23 m6A RNA methylation regulators in the TCGA-ccRCC cohort. (B) The prognostic analyses for 23 m6A RNA methylation regulators in the TCGA-ccRCC cohort using the univariate Cox regression model. (C) Kaplan–Meier analysis of patients between high- and low-MRPRS groups in the TCGA-ccRCC cohort. (D) Validation cohort of MRPRS from GSE29609. (E) Box plot of different MRPRSs in 3 epithelial cell subsets. (F) The MRPRS between response and non-response groups in PMID32472114. (G–I) Kaplan–Meier analysis of three validation cohorts of immunotherapy in ccRCC (PMID32472114, PMID29301960, and PMID32895571). (J) Heatmap of chemotherapy and targeted drug-related genes between high- and low-MRPRS groups. (K) The correlation of MRPRS and genes associated with angiogenesis. (L) The MRPRS between response and non-response groups in ccRCC with sunitinib (E-MTAB-3267). (M) Kaplan–Meier analysis between high- and low-MRPRS groups in ccRCC with sunitinib (E-MTAB-3267) (*p < 0.05; **p < 0.01; ***p < 0.001).
Figure 3
Figure 3
(A) Box plot of the relationship between stage T, N, M, grade, and MRPRS. (B) The heatmap of markers on multiple immune infiltrates. (C) The MCP_counter algorithm was used to estimate the abundance of various types of immune cells between high- and low-MRPRS groups. (D) The abundance and diversity of TCR clone in high- and low-MRPRS groups. (E) Crosstalk between immune cells and epithelial cells (*p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001).
Figure 4
Figure 4
(A) Waterfall plot of the distribution of mutations found in the high- and low-MRPRS groups of the TCGA-ccRCC cohort. (B) The top 5 genes of high- vs. low-MRPRS group mutation status. (C) Lollipop plot of somatic mutations in SETD2, TRIOBP, RYR2, ZFPM2, and ABCC6. (D, E) MCP_counter and ssGSEA algorithm were used to estimate the abundance of various types of immune cells in high- and low-MRPRS groups (*p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns, not significant).
Figure 5
Figure 5
(A) Different distribution ratio of response and non-response in the immunotherapeutic cohort of ccRCC. (B) Kaplan–Meier analysis of patients in the mutated and non-mutated groups in the immunotherapeutic cohort of ccRCC (Van_2018, Morris_2019, PMID29337640 and PMID29301960). (C) The composition of major cancer types in the immunotherapeutic cohort contains mutations of pan-cancer. (D, E) Kaplan–Meier analysis (OS and PFS) of patients in the mutation and non-mutation groups in the immunotherapeutic cohort of pan-cancer. (F) Kaplan–Meier analysis of patients in the low TMB of mutated, low TMB of non-mutated, and high TMB groups.

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