Dataset Information

Development of a prognostic risk model for clear cell renal cell carcinoma by systematic evaluation of DNA methylation markers.

ABSTRACT:

Background

Current risk models for renal cell carcinoma (RCC) based on clinicopathological factors are sub-optimal in accurately identifying high-risk patients. Here, we perform a head-to-head comparison of previously published DNA methylation markers and propose a potential prognostic model for clear cell RCC (ccRCC).

Patients and methods

Promoter methylation of PCDH8, BNC1, SCUBE3, GREM1, LAD1, NEFH, RASSF1A, GATA5, SFRP1, CDO1, and NEURL was determined by nested methylation-specific PCR. To identify clinically relevant methylated regions, The Cancer Genome Atlas (TCGA) was used to guide primer design. Formalin-fixed paraffin-embedded (FFPE) tissue samples from 336 non-metastatic ccRCC patients from the prospective Netherlands Cohort Study (NLCS) were used to develop a Cox proportional hazards model using stepwise backward elimination and bootstrapping to correct for optimism. For validation purposes, FFPE ccRCC tissue of 64 patients from the University Hospitals Leuven and a series of 232 cases from The Cancer Genome Atlas (TCGA) were used.

Results

Methylation of GREM1, GATA5, LAD1, NEFH, NEURL, and SFRP1 was associated with poor ccRCC-specific survival, independent of age, sex, tumor size, TNM stage or tumor grade. Moreover, the association between GREM1, NEFH, and NEURL methylation and outcome was shown to be dependent on the genomic region. A prognostic biomarker model containing GREM1, GATA5, LAD1, NEFH and NEURL methylation in combination with clinicopathological characteristics, performed better compared to the model with clinicopathological characteristics only (clinical model), in both the NLCS and the validation population with a c-statistic of 0.71 versus 0.65 and a c-statistic of 0.95 versus 0.86 consecutively. However, the biomarker model had limited added prognostic value in the TCGA series with a c-statistic of 0.76 versus 0.75 for the clinical model.

Conclusion

In this study we performed a head-to-head comparison of potential prognostic methylation markers for ccRCC using a novel approach to guide primers design which utilizes the optimal location for measuring DNA methylation. Using this approach, we identified five methylation markers that potentially show prognostic value in addition to currently known clinicopathological factors.

SUBMITTER: Joosten SC

PROVIDER: S-EPMC8094610 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCandidate biomarkers have been identified for clear cell renal cell carcinoma (ccRCC) patients, but most have not been validated.ObjectiveTo validate published ccRCC prognostic biomarkers in an independent patient cohort and to assess intratumour heterogeneity (ITH) of the most promising markers to guide biomarker optimisation.Design, setting, and participantsCancer-specific survival (CSS) for each of 28 identified genetic or transcriptomic biomarkers was assessed in 350 ccRCC patients. ITH was interrogated in a multiregion biopsy data set of 10 ccRCCs.Outcome measurements and statistical analysisBiomarker association with CSS was analysed by univariate and multivariate analyses.Results and limitationsA total of 17 of 28 biomarkers (TP53 mutations; amplifications of chromosomes 8q, 12, 20q11.21q13.32, and 20 and deletions of 4p, 9p, 9p21.3p24.1, and 22q; low EDNRB and TSPAN7 expression and six gene expression signatures) were validated as predictors of poor CSS in univariate analysis. Tumour stage and the ccB expression signature were the only independent predictors in multivariate analysis. ITH of the ccB signature was identified in 8 of 10 tumours. Several genetic alterations that were significant in univariate analysis were enriched, and chromosomal instability indices were increased in samples expressing the ccB signature. The study may be underpowered to validate low-prevalence biomarkers.ConclusionsThe ccB signature was the only independent prognostic biomarker. Enrichment of multiple poor prognosis genetic alterations in ccB samples indicated that several events may be required to establish this aggressive phenotype, catalysed in some tumours by chromosomal instability. Multiregion assessment may improve the precision of this biomarker.Patient summaryWe evaluated the ability of published biomarkers to predict the survival of patients with clear cell kidney cancer in an independent patient cohort. Only one molecular test adds prognostic information to routine clinical assessments. This marker showed good and poor prognosis results within most individual cancers. Future biomarkers need to consider variation within tumours to improve accuracy.

Project description:Background: Abnormal DNA methylation of is one of the important mechanisms leading to tumor pathogenesis. The purpose of this study was to explore differentially methylated genes that may drive the development of renal clear cell carcinoma through a comprehensive analysis of the TCGA database. Materials and methods: Methylation data and RNA-seq data for clear cell renal cell carcinoma were downloaded from The Cancer Genome Atlas (TCGA). Differentially methylated genes and the differential genes associated with survival were then screened by MethylMix R package and univariate Cox proportional-hazards model, respectively. Their common genes were then intersected and obtained for further analysis. Correlation of gene expression and methylation levels, gene set enrichment analysis (GSEA) enrichments, survival curve, and ROC curve plotting for DNA methylation-driven genes were finally performed. The methylation alterations of the three genes were validated via two GEO datasets (GSE70303 and GSE113501), and the genes expression level was verified through two GEO datasets (GSE6344 and GSE53757). Results: Three novel DNA methylation-driven genes LAT, HOXD3 and NFE2L3 were identified in clear cell renal cell carcinoma. Expression analysis further revealed that hypomethylation levels of LAT and NFE2L3 showed higher gene expression levels, while HOXD3 exhibited opposite methylation-expression pattern. The CpG sites of LAT (cg16462073), HOXD3 (cg24000528) and NFE2L3 (cg16882373) that may affect respective gene expressions were also identified. For the survival analysis, we found that hypomethylation and over-expression of LAT and NFE2L3 were correlated with poor survival, while hypermethylation and low-expression HOXD3 was correlated with poor survival of clear cell renal cell carcinoma patients. In addition, GSEA KEGG analysis and biological processes of these genes were also enriched for functional analysis. Kaplan-Meier survival and ROC analyses of these genes showed an average risk score of 0.9140593, AUC = 0.692, which suggested a good clinical application value. Finally, the opposite methylation-expression pattern of these three genes were verified in GEO datasets. Conclusions: In this study, we successfully exhibited the potential DNA methylation-driven genes LAT, HOXD3, and NFE2L3 involved in clear cell renal cell carcinoma. Moreover, gene functions and prognostic risk models were also elucidated, which facilitated the expansion of the current study on the role of methylation in the pathology process of clear cell renal cell carcinoma.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer and is associated with poor prognosis if metastasized. Up to one third of patients with local disease at diagnosis will develop metastasis after nephrectomy, and there is a need for new molecular markers to identify patients with high risk of tumor progression. In the present study, we performed genome-wide promoter DNA methylation analysis at diagnosis to identify DNA methylation profiles associated with risk for progress.MethodDiagnostic tissue samples from 115 ccRCC patients were analysed by Illumina HumanMethylation450K arrays and methylation status of 155,931 promoter associated CpGs were related to genetic aberrations, gene expression and clinicopathological parameters.ResultsThe ccRCC samples separated into two clusters (cluster A/B) based on genome-wide promoter methylation status. The samples in these clusters differed in tumor diameter (p < 0.001), TNM stage (p < 0.001), morphological grade (p < 0.001), and patients outcome (5 year cancer specific survival (pCSS5yr) p < 0.001 and cumulative incidence of progress (pCIP5yr) p < 0.001. An integrated genomic and epigenomic analysis in the ccRCCs, revealed significant correlations between the total number of genetic aberrations and total number of hypermethylated CpGs (R = 0.435, p < 0.001), and predicted mitotic age (R = 0.407, p < 0.001). We identified a promoter methylation classifier (PMC) panel consisting of 172 differently methylated CpGs accompanying progress of disease. Classifying non-metastatic patients using the PMC panel showed that PMC high tumors had a worse prognosis compared with the PMC low tumors (pCIP5yr 38% vs. 8%, p = 0.001), which was confirmed in non-metastatic ccRCCs in the publically available TCGA-KIRC dataset (pCIP5yr 39% vs. 16%, p < 0.001).ConclusionDNA methylation analysis at diagnosis in ccRCC has the potential to improve outcome-prediction in non-metastatic patients at diagnosis.