ArticleBMC genomics2023
Identification and validation of a DNA methylation-driven gene-based prognostic model for clear cell renal cell carcinoma.
Article in BMC genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Role of DNA methylation transferase in urinary system diseases: From basic to clinical perspectives (Review).International journal of molecular medicine · 2025Review
- Article
- ZNF471 Interacts with BANP to Reduce Tumour Malignancy by Inactivating PI3K/AKT/mTOR Signalling but is Frequently Silenced by Aberrant Promoter Methylation in Renal Cell Carcinoma.International journal of biological sciences · 2024Article
- Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction.BMC genomics · 2023Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClear cell renal cell carcinoma (ccRCC) is a malignant tumor with heterogeneous morphology and poor prognosis. This study aimed to establish a DNA methylation (DNAm)-driven gene-based prognostic model for ccRCC.
methodsReduced representation bisulfite sequencing (RRBS) was performed on the DNA extracts from ccRCC patients. We analyzed the RRBS data from 10 pairs of patient samples to screen the candidate CpG sites, then trained and validated an 18-CpG site model, and integrated the clinical characters to establish a Nomogram model for the prognosis or risk evaluation of ccRCC.
resultsWe identified 2261 DMRs in the promoter region. After DMR selection, 578 candidates were screened, and was correspondence with 408 CpG dinucleotides in the 450 K array. We collected the DNAm profiles of 478 ccRCC samples from TCGA dataset. Using the training set with 319 samples, a prognostic panel of 18 CpGs was determined by univariate Cox regression, LASSO regression, and multivariate Cox proportional hazards regression analyses. We constructed a prognostic model by combining the clinical signatures. In the test set (159 samples) and whole set (478 samples), the Kaplan-Meier plot showed significant differences; and the ROC curve and survival analyses showed AUC greater than 0.7. The Nomogram integrated with clinicopathological characters and methylation risk score had better performance, and the decision curve analyses also showed a beneficial effect.
conclusionsThis work provides insight into the role of hypermethylation in ccRCC. The targets identified might serve as biomarkers for early ccRCC diagnosis and prognosis biomarkers for ccRCC. We believe our findings have implications for better risk stratification and personalized management of this disease.
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