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Identification of clear cell renal cell carcinoma and oncocytoma using a three-gene promoter methylation panel.

ABSTRACT: BACKGROUND:Promoter methylation has emerged as a promising class of epigenetic biomarkers for diagnosis and prognosis of renal cell tumors (RCTs). Although differential gene promoter methylation patterns have been reported for the major subtypes (clear cell, papillary and chromophobe renal cell carcinoma, and oncocytoma), validation of diagnostic performance in independent series have been seldom performed. Herein, we aimed at assessing the diagnostic performance of genes previously shown to be hypermethylated in RCTs in different clinical settings. METHODS:Promoter methylation levels of HOXA9 and OXR1 were assessed by quantitative methylation specific PCR. ROC curves were generated for OXR1, OXR1 combined with MST1R and HOXA9. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy were computed, maximizing specificity. Methylation levels were also correlated with clinical and pathological relevant parameters. RESULTS:HOXA9 and OXR1 promoter methylation was disclosed in 73 and 87% of RCTs, respectively. A two-gene methylation panel comprising OXR1 and MST1R identified malignancy with 98% sensitivity and 100% specificity, and clear cell renal cell carcinoma with 90% sensitivity and 98% specificity. HOXA9 promoter methylation allowed for discrimination between oncocytoma and both papillary and chromophobe renal cell carcinoma but only with 77% sensitivity and 73% specificity. Significantly higher OXR1 promoter methylation levels (p = 0.005) were associated with high nuclear grade in ccRCC. CONCLUSIONS:A panel including OXR1 and MST1R promoter methylation allows specific and sensitive identification of renal cell tumors, and, especially, of clear cell renal cell carcinoma. Moreover, higher OXR1 promoter methylation levels associate with clear cell renal cell carcinoma nuclear grade, a surrogate for tumor aggressiveness. Thus, gene promoter methylation analysis might a useful ancillary tool in diagnostic management of renal masses.

SUBMITTER: Pires-Luis AS

PROVIDER: S-EPMC5492907 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Identification of clear cell renal cell carcinoma and oncocytoma using a three-gene promoter methylation panel.

Pires-Luís Ana Sílvia AS Costa-Pinheiro Pedro P Ferreira Maria João MJ Antunes Luís L Lobo Francisco F Oliveira Jorge J Henrique Rui R Jerónimo Carmen C

Journal of translational medicine 20170629 1

<h4>Background</h4>Promoter methylation has emerged as a promising class of epigenetic biomarkers for diagnosis and prognosis of renal cell tumors (RCTs). Although differential gene promoter methylation patterns have been reported for the major subtypes (clear cell, papillary and chromophobe renal cell carcinoma, and oncocytoma), validation of diagnostic performance in independent series have been seldom performed. Herein, we aimed at assessing the diagnostic performance of genes previously show ...[more]

PMID: 28662726

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Project description:The present techniques of clinical and histopathological diagnosis hardly distinguish chromophobe renal cell carcinoma (ChRCC) from renal oncocytoma (RO). To identify differentially expressed genes (DEGs) as effective biomarkers for diagnosis and prognosis of ChRCC and RO, three mRNA microarray datasets (GSE12090, GSE19982, and GSE8271) were downloaded from the GEO database. Functional enrichment analysis of DEGs was performed by DAVID. STRING and Cytoscape were applied to construct the protein-protein interaction (PPI) network and key modules of DEGs. Visualized plots were conducted by the R language. We downloaded clinical data from the TCGA database and the influence of key genes on the overall survival of ChRCC was performed by Kaplan-Meier and Cox analyses. Gene set enrichment analysis (GSEA) was utilized in exploring the function of key genes. A total of 79 DEGs were identified. Enrichment analyses revealed that the DEGs are closely related to tissue invasion and metastasis of cancer. Subsequently, 14 hub genes including ESRP1, AP1M2, CLDN4, and CLDN7 were detected. Kaplan-Meier analysis indicated that the low expression of CLDN7 and GNAS was related to the worse overall survival in patients with ChRCC. Univariate Cox analysis showed that CLDN7 might be a helpful biomarker for ChRCC prognosis. Subgroup analysis revealed that the expression of CLDN7 showed a downtrend with the development of the clinical stage, topography, and distant metastasis of ChRCC. GSEA analysis identified that cell adhesion molecules cams, B cell receptor signaling pathway, T cell receptor signaling pathway, RIG-I like receptor signaling pathway, Toll-like receptor signaling pathway, and apoptosis pathway were associated with the expression of CLDN7. In conclusion, ESRP1, AP1M2, CLDN4, PRSS8, and CLDN7 were found to distinguish ChRCC from RO. Besides, the low expression of CLDN7 was closely related to ChRCC progression and could serve as an independent risk factor for the overall survival in patients with ChRCC.

Project description:BackgroundTumorigenesis is highly heterogeneous, and using clinicopathological signatures only is not enough to effectively distinguish clear cell renal cell carcinoma (ccRCC) and improve risk stratification of patients. DNA methylation (DNAm) with the stability and reversibility often occurs in the early stage of tumorigenesis. Disorders of transcription and metabolism are also an important molecular mechanisms of tumorigenesis. Therefore, it is necessary to identify effective biomarkers involved in tumorigenesis through multi-omics analysis, and these biomarkers also provide new potential therapeutic targets.MethodThe discovery stage involved 160 pairs of ccRCC and matched normal tissues for investigation of DNAm and biomarkers as well as 318 cases of ccRCC including clinical signatures. Correlation analysis of epigenetic, transcriptomic and metabolomic data revealed the connection and discordance among multi-omics and the deregulated functional modules. Diagnostic or prognostic biomarkers were obtained by the correlation analysis, the Least Absolute Shrinkage and Selection Operator (LASSO) and the LASSO-Cox methods. Two classifiers were established based on random forest (RF) and LASSO-Cox algorithms in training datasets. Seven independent datasets were used to evaluate robustness and universality. The molecular biological function of biomarkers were investigated using DAVID and GeneMANIA.ResultsBased on multi-omics analysis, the epigenetic measurements uniquely identified DNAm dysregulation of cellular mechanisms resulting in transcriptomic alterations, including cell proliferation, immune response and inflammation. Combination of the gene co-expression network and metabolic network identified 134 CpG sites (CpGs) as potential biomarkers. Based on the LASSO and RF algorithms, five CpGs were obtained to build a diagnostic classifierwith better classification performance (AUC > 99%). A eight-CpG-based prognostic classifier was obtained to improve risk stratification (hazard ratio (HR) > 4; log-rank test, p-value < 0.01). Based on independent datasets and seven additional cancers, the diagnostic and prognostic classifiers also had better robustness and stability. The molecular biological function of genes with abnormal methylation were significantly associated with glycolysis/gluconeogenesis and signal transduction.ConclusionThe present study provides a comprehensive analysis of ccRCC using multi-omics data. These findings indicated that multi-omics analysis could identify some novel epigenetic factors, which were the most important causes of advanced cancer and poor clinical prognosis. Diagnostic and prognostic biomarkers were identified, which provided a promising avenue to develop effective therapies for ccRCC.

Dataset Information

Identification of clear cell renal cell carcinoma and oncocytoma using a three-gene promoter methylation panel.

Publications

Identification of clear cell renal cell carcinoma and oncocytoma using a three-gene promoter methylation panel.

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