ArticleCancer cell international2019
DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups.
Article in Cancer cell international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 42 citations in OpenAlex.
- DNA methyltransferase inhibition is a therapeutic vulnerability in VHL-deficient renal cell carcinoma cells.Experimental & molecular medicine · 2026Article
- Prognostic Role of Global DNA Methylation in Renal Cancer Reveals Decitabine Treatment Benefit.Current medicinal chemistry · 2026Article
- DNA methylation expression patterns predict outcome of clear cell renal cell carcinoma.Discover oncology · 2025Article
- Volatile Organic Metabolites as Potential Biomarkers for Genitourinary Cancers: Review of the Applications and Detection Methods.Metabolites · 2025Review
- Optimization of response surface methodology for the extraction of isoliquiritigenin fromFrontiers in pharmacology · 2025Article
- Methylation profiling in neuropathological tumors diagnosis: a comprehensive review.Frontiers in oncology · 2025Review
- Microfluidics and molecular diagnostics in renal cell carcinoma: advances, challenges, and future directions.Frontiers in oncology · 2025Review
- Circulating Tumor DNA in Genitourinary Cancers: Detection, Prognostics, and Therapeutic Implications.Cancers · 2024Review
- Article
- meth-SemiCancer: a cancer subtype classification framework via semi-supervised learning utilizing DNA methylation profiles.BMC bioinformatics · 2023Article
- DNA methylation regulator-based signature for predicting clear cell renal cell carcinoma prognosis.American journal of translational research · 2023Article
- Urine Molecular Biomarkers for Detection and Follow-Up of Small Renal Masses.International journal of molecular sciences · 2022Article
- Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker-driven learning framework.Science advances · 2022Article
- Epidemiology and Prevention of Renal Cell Carcinoma.Cancers · 2022Review
- CancerNet: a unified deep learning network for pan-cancer diagnostics.BMC bioinformatics · 2022Article
- Molecular Subgroup Classification in Alzheimer's Disease by Transcriptomic Profiles.Journal of molecular neuroscience : MN · 2022Article
- Epigenetic Biomarkers of Renal Cell Carcinoma for Liquid Biopsy Tests.International journal of molecular sciences · 2021Review
- The Role of Epigenetics in the Progression of Clear Cell Renal Cell Carcinoma and the Basis for Future Epigenetic Treatments.Cancers · 2021Review
- Reclassification of Kidney Clear Cell Carcinoma Based on Immune Cell Gene-Related DNA CpG Pairs.Biomedicines · 2021Article
- Aberrant methylation of the 16q23.1 tumor suppressor gene ADAMTS18 promotes tumorigenesis and progression of clear cell renal cell carcinoma.Genes & genomics · 2021Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRenal cell carcinoma (RCC) is the most common kidney cancer and includes several molecular and histological subtypes with different clinical characteristics. The combination of DNA methylation and gene expression data can improve the classification of tumor heterogeneity, by incorporating differences at the epigenetic level and clinical features.
methodsIn this study, we identified the prognostic methylation and constructed specific prognosis-subgroups based on the DNA methylation spectrum of RCC from the TCGA database.
resultsSignificant differences in DNA methylation profiles among the seven subgroups were revealed by consistent clustering using 3389 CpGs that indicated that were significant differences in prognosis. The specific DNA methylation patterns reflected differentially in the clinical index, including TNM classification, pathological grade, clinical stage, and age. In addition, 437 CpGs corresponding to 477 genes of 151 samples were identified as specific hyper/hypomethylation sites for each specific subgroup. A total of 277 and 212 genes corresponding to DNA methylation at promoter sites were enriched in transcription factor of GKLF and RREB-1, respectively. Finally, Bayesian network classifier with specific methylation sites was constructed and was used to verify the test set of prognoses into DNA methylation subgroups, which was found to be consistent with the classification results of the train set. DNA methylation-based classification can be used to identify the distinct subtypes of renal cell carcinoma.
conclusionsThis study shows that DNA methylation-based classification is highly relevant for future diagnosis and treatment of renal cell carcinoma as it identifies the prognostic value of each epigenetic subtype.
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