N6-Methyladenosine Regulator-Mediated Methylation Modification Patterns with Distinct Prognosis, Oxidative Stress, and Tumor Microenvironment in Renal Cell Carcinoma

N⁶-Methyladenosine Regulator-Mediated Methylation Modification Patterns with Distinct Prognosis, Oxidative Stress, and Tumor Microenvironment in Renal Cell Carcinoma

Chunyang Li^1,†, Maoshu Zhu^2,†, Chuane Gao^3,†, Fuhua Lu⁴, Huoshu Chen⁵, Jiancheng Liu^6,*, Weimin Zhong^2,*

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¹ Department of General Surgery at The Fifth Hospital of Xiamen, 361101 Xiamen, Fujian, China

² Central Laboratory at The Fifth Hospital of Xiamen, 361101 Xiamen, Fujian, China

³ Department of Respiratory at The Fifth Hospital of Xiamen, 361101 Xiamen, Fujian, China

⁴ Department of Nephrology at The Fifth Hospital of Xiamen, 361101 Xiamen, Fujian, China

⁵ Department of Pharmacy at The Fifth Hospital of Xiamen, 361101 Xiamen, Fujian, China

⁶ Department of Radiology at The Fifth Hospital of Xiamen, 361101 Xiamen, Fujian, China

^*Correspondence: liu84610@163.com (Jiancheng Liu); zhongweimin63@gmail.com (Weimin Zhong)
^†These authors contributed equally.

Front. Biosci. (Landmark Ed) 2024, 29(1), 33; https://doi.org/10.31083/j.fbl2901033

Submitted: 30 March 2023 | Revised: 8 October 2023 | Accepted: 19 November 2023 | Published: 19 January 2024

(This article belongs to the Special Issue Targeting of Multiple Predictive Biomarkers for Checkpoint Immunotherapy)

This is an open access article under the CC BY 4.0 license.

Abstract

Objective: Emerging evidence suggests the biological implications of N ${}^{6}$ -methyladenosine (m ${}^{6}$ A) in carcinogenesis. Herein, we systematically analyzed the role of m ${}^{6}$ A modification in renal cell carcinoma (RCC) progression. Methods: Based on 23 m ${}^{6}$ A regulators, unsupervised clustering analyses were conducted to determine m ${}^{6}$ A modification subtypes across 893 RCC specimens in the Cancer Genome Atlas (TCGA) cohort. By performing principal component analysis (PCA) analysis, m ${}^{6}$ A scoring system was developed for evaluating m ${}^{6}$ A modification patterns of individual RCC patients. The activity of signaling pathways was assessed by gene-set variation analysis (GSVA) algorithm. The single-sample gene set enrichment analysis (ssGSEA) algorithm was applied for quantifying the infiltration levels of immune cells and the activity of cancer immunity cycle. Drug responses were estimated by genomics of drug sensitivity in cancer (GDSC), the Cancer Therapeutics Response Portal (CTRP) and Preservice Research Institute for Science and Mathematics (PRISM) database. databases. Results: Five m ${}^{6}$ A modification subtypes were characterized by different survival outcomes, oxidative stress, cancer stemness, infiltrations of immune cells, activity of cancer immunity cycle, programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) expression and microsatellite instability (MSI) levels. According to m ${}^{6}$ A score, RCC patients were categorized into high and low m ${}^{6}$ A score groups. Patients with high m ${}^{6}$ A score displayed a prominent survival advantage, and the prognostic value of m ${}^{6}$ A score was confirmed in two anti-PD-1/PD-L1 immunotherapy cohorts. m ${}^{6}$ A score was significantly linked to oxidative stress-related genes, and high m ${}^{6}$ A score indicated the higher sensitivity to axitinib, pazopanib and sorafenib and the lower sensitivity to sunitinib. Conclusion: This study analyzed the extensive regulatory mechanisms of m ${}^{6}$ A modification on oxidative stress, the tumor microenvironment, and immunity. Quantifying m ${}^{6}$ A scores may enhance immunotherapeutic effects and assist in developing more effective agents.

Keywords

renal cell carcinoma

N⁶-methyladenosine

prognosis

oxidative stress

tumor microenvironment

immunity

Funding

2019-ZQNB-34/Fujian Province Health Care Young and Middle-aged Backbone Talents Training Project

2021D010/Fujian Provincial Science and Technology Plan Project

Figures

Fig. 1.

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Fig. 1.

Systematic analyses of genetic variation, expression, prognostic implication and interactions of N6-methyladenosine (m6A) regulators in renal cell carcinoma (RCC). (A,B) Before and after removing batch effects by integrating three types of RCC: clear cell RCC (ccRCC), papillary RCC (pRCC) and chromophobe RCC (chRCC) from the Cancer Genome Atlas (TCGA) datasets. PCA, Principal component analysis. (C) The frequency of copy number variation (CNV) (red: gain and blue: loss) of m ${}^{6}$ A regulators across RCC samples. (D) An overview of the mRNA expression of m ${}^{6}$ A regulators in RCC and normal specimens. p values were calculated by unpaired student’s t test, *p $<$ 0.05 and ****p $<$ 0.0001. Red: up-regulation and green: down-regulation. (E) The mRNA expression of m ${}^{6}$ A regulators in stage I&II and stage III&IV RCC cases. *p $<$ 0.05, ***p $<$ 0.001 and ****p $<$ 0.0001. (F–I) Univariate-cox regression analyses of the correlation between m ${}^{6}$ A regulators and RCC patients’ overall survival (OS), disease free survival (DFS), disease-specific survival (DSS), and progression free survival (PFS). (J) The protein-protein interaction (PPI) network of writers (green), erasers (blue) and readers (red). (K) The Pearson correlations between m ${}^{6}$ A regulators at the mRNA levels across RCC samples. Red: positive correlation and green: negative correlation, *p $<$ 0.05, **p $<$ 0.01 and ***p $<$ 0.001.

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