DNA Methylation Modification Patterns Identify Distinct Prognosis and Responses to Immunotherapy and Targeted Therapy in Renal Cell Carcinoma

² Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Northwestern Polytechnical University, 518057 Shenzhen, China

³ Department of Gastroenterology, National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, Second Affiliated Hospital of Xi’an Jiaotong University, 710004 Xi’an, Shaanxi, China

⁴ Department of Gastroenterology, Fudan University Pudong Medical Center, 200031 Shanghai, China

⁵ Department of Chemistry, Graduate School of Science, Kyoto University, 606-8502 Kyoto, Japan

⁶ Institute for Integrated Cell-Material Science (WPI-iCeMS), Kyoto University, 606-8501 Kyoto, Japan

^*Correspondence: danbai@xjtu.edu.cn (Dan Bai)

Front. Biosci. (Landmark Ed) 2023, 28(9), 224; https://doi.org/10.31083/j.fbl2809224

Submitted: 24 March 2023 | Revised: 3 July 2023 | Accepted: 21 August 2023 | Published: 26 September 2023

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

Abstract

Background: Considering the remarkable heterogeneity of biological features of renal cell carcinoma (RCC), the current clinical classification that only relies on classic clinicopathological features is in urgent need of improvement. Herein, we aimed to conduct DNA methylation modification patterns in RCC. Methods: We retrospectively curated multiple RCC cohorts, comprising TCGA-KIRC, TCGA-KICH, TCGA-KIRP, and E-MTAB-1980. DNA methylation modification patterns were proposed with an unsupervised clustering algorithm based on 20 DNA methylation regulators. Immunological features were characterized using tumor-infiltrating immune cells and immunomodulators. Sensitivity to immuno- or targeted therapy was estimated with submap and Genomics of Drug Sensitivity in Cancer (GDSC). DNA methylation score (DMS) was developed with principal component analysis. Results: Three DNA methylation modification patterns were conducted across RCC patients, namely C1, C2 and C3. Among them, C3 displayed the most remarkable survival advantage. The three patterns presented in agreement with immune phenotypes: immune-desert, immune-excluded, and immune-inflamed, respectively. These patterns displayed distinct responses to anti-PD-1 and targeted drugs. DMS enabled the quantification of DNA methylation status individually as an alternative tool for prognostic estimation. Conclusions: The DNA methylation molecular patterns we proposed are an innovative complement to the traditional classification of RCC, which might contribute to precision medicine.

Keywords

renal cell carcinoma

DNA methylation

molecular pattern

prognosis

therapeutic response

Funding

JCYJ20190806153018791/Science Technology and Innovation Commission of Shenzhen Municipality

LGF19H200005/Natural Science Foundation of Zhejiang Province

81601553/Natural Science Foundation of Shaanxi

XN2021119/The Innovation and Entrepreneurship Project for Undergraduate Students

S202110699680/The Innovation and Entrepreneurship Project for Undergraduate Students

2018920/Japan China Medical Association

Figures

Fig. 1.

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Fig. 1.

Multi-omics landscape, prognosis, and immune features of DNA methylation modifiers in renal cell carcinoma (RCC). (A) The location of 20 DNA methylation modifiers in the human chromosomes. (B) Boxplot displays the expression of these DNA methylation regulators in tumors and non-tumor tissues in the TCGA-KIRC cohort. Ns, no significance; *p-value $<$ 0.05; ***p-value $<$ 0.001. (C) The distribution of copy number variation (CNV) frequencies of amplification (red dot) and deletion (green dot) of the regulators in the TCGA-KIRC cohort. (D) The protein-protein interaction (PPI) network of DNA methylation modifiers via the STRING database (https://string-db.org/). The size of the circle indicates the number of regulators that interact with others. (E) The interaction and prognostic implication across the above regulators in the TCGA-KIRC cohort. The size of the circle shows p-values derived from log-rank tests. The orange line indicates a positive association with p-value $<$ 0.05 while the blue line indicates a negative association with p-value $<$ 0.05. The red point represents a risk factor of overall survival (OS) while the black point suggests a favorable factor of OS. (F) Heatmap displays the associations between DNA methylation regulator expression and sensitivity to small molecular compounds. The red circle represents a positive association while the blue circle indicates a negative association. The black border suggests a p-value $\leq$ 0.05 while the grey border suggests a p-value $>$ 0.05. (G) Heatmap displays the interactions of DNA methylation modifiers with immune cell infiltration across TCGA-KIRC cases. Red indicates a positive association while blue indicates a negative association. *p-value $<$ 0.05; **p-value $<$ 0.01.

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