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Analysis of distribution of DNA methylation in kidney-renal-clear-cell-carcinoma specific genes using entropy.

ABSTRACT: DNA Methylation is an epigenetic phenomenon in which methyl groups are added to the cytosines, thereby altering the physio-chemical properties of the DNA region and influencing gene expression. Aberrant DNA methylation in a set of genes or across the genome results in many epigenetic diseases including cancer. In this paper, we use entropy to analyze the extent and distribution of DNA methylation in Tumor Suppressor Genes (TSG's) and Oncogenes related to a specific type of cancer (viz.) KIRC (Kidney-renal-clear-cell-carcinoma). We apply various mathematical transformations to enhance the different regions in DNA methylation distribution and compare the resultant entropies for healthy and tumor samples. We also obtain the sensitivity and specificity of classification for the different mathematical transformations. Our findings show that it is not just the measure of methylation, but the distribution of the methylation levels in the genes that are significant in cancer.

SUBMITTER: Ramakrishnan N

PROVIDER: S-EPMC5080556 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Analysis of distribution of DNA methylation in kidney-renal-clear-cell-carcinoma specific genes using entropy.

Ramakrishnan Nithya N Bose R R

Genomics data 20161018

DNA Methylation is an epigenetic phenomenon in which methyl groups are added to the cytosines, thereby altering the physio-chemical properties of the DNA region and influencing gene expression. Aberrant DNA methylation in a set of genes or across the genome results in many epigenetic diseases including cancer. In this paper, we use entropy to analyze the extent and distribution of DNA methylation in Tumor Suppressor Genes (TSG's) and Oncogenes related to a specific type of cancer (viz.) KIRC (Ki ...[more]

PMID: 27818943

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Project description:Kidney Renal Clear Cell Carcinoma (KIRC) is one of fatal genitourinary diseases and accounts for most malignant kidney tumours. KIRC has been shown resistance to radiotherapy and chemotherapy. Like many types of cancers, there is no curative treatment for metastatic KIRC. Using advanced sequencing technologies, The Cancer Genome Atlas (TCGA) project of NIH/NCI-NHGRI has produced large-scale sequencing data, which provide unprecedented opportunities to reveal new molecular mechanisms of cancer. We combined differentially expressed genes, pathways and network analyses to gain new insights into the underlying molecular mechanisms of the disease development.Followed by the experimental design for obtaining significant genes and pathways, comprehensive analysis of 537 KIRC patients' sequencing data provided by TCGA was performed. Differentially expressed genes were obtained from the RNA-Seq data. Pathway and network analyses were performed. We identified 186 differentially expressed genes with significant p-value and large fold changes (P < 0.01, |log(FC)| > 5). The study not only confirmed a number of identified differentially expressed genes in literature reports, but also provided new findings. We performed hierarchical clustering analysis utilizing the whole genome-wide gene expressions and differentially expressed genes that were identified in this study. We revealed distinct groups of differentially expressed genes that can aid to the identification of subtypes of the cancer. The hierarchical clustering analysis based on gene expression profile and differentially expressed genes suggested four subtypes of the cancer. We found enriched distinct Gene Ontology (GO) terms associated with these groups of genes. Based on these findings, we built a support vector machine based supervised-learning classifier to predict unknown samples, and the classifier achieved high accuracy and robust classification results. In addition, we identified a number of pathways (P < 0.04) that were significantly influenced by the disease. We found that some of the identified pathways have been implicated in cancers from literatures, while others have not been reported in the cancer before. The network analysis leads to the identification of significantly disrupted pathways and associated genes involved in the disease development. Furthermore, this study can provide a viable alternative in identifying effective drug targets.Our study identified a set of differentially expressed genes and pathways in kidney renal clear cell carcinoma, and represents a comprehensive computational approach to analysis large-scale next-generation sequencing data. The pathway and network analyses suggested that information from distinctly expressed genes can be utilized in the identification of aberrant upstream regulators. Identification of distinctly expressed genes and altered pathways are important in effective biomarker identification for early cancer diagnosis and treatment planning. Combining differentially expressed genes with pathway and network analyses using intelligent computational approaches provide an unprecedented opportunity to identify upstream disease causal genes and effective drug targets.

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.

Dataset Information

Analysis of distribution of DNA methylation in kidney-renal-clear-cell-carcinoma specific genes using entropy.

Publications

Analysis of distribution of DNA methylation in kidney-renal-clear-cell-carcinoma specific genes using entropy.

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