Dataset Information

DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups.

ABSTRACT: Background:Renal 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. Methods:In this study, we identified the prognostic methylation and constructed specific prognosis-subgroups based on the DNA methylation spectrum of RCC from the TCGA database. Results:Significant 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. Conclusions:This 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.

SUBMITTER: Chen W

PROVIDER: S-EPMC6636124 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups.

Chen Wenbiao W Zhuang Jia J Wang Peizhong Peter PP Jiang Jingjing J Lin Chenhong C Zeng Ping P Liang Yan Y Zhang Xujun X Dai Yong Y Diao Hongyan H

Cancer cell international 20190716

<h4>Background</h4>Renal 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.<h4>Methods</h4>In this study, we identified the prognostic methylation and constructed specific prognosis-subgroups based on the DNA ...[more]

PMID: 31346320

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Project description:BackgroundPancreatic cancer (PC) is one of the most lethal and aggressive cancer malignancies. The lethality of PC is associated with delayed diagnosis, presence of distant metastasis, and its easy relapse. It is known that clinical treatment decisions are still mainly based on the clinical stage and pathological grade, which are insufficient to determine an appropriate treatment. Considering the significant heterogeneity of PC biological characteristics, the current clinical classificatory pattern relying solely on classical clinicopathological features identification needs to be urgently improved. In this study, we conducted in-depth analyses to establish prognosis-related molecular subgroups based on DNA methylation signature.ResultsDNA methylation, RNA sequencing, somatic mutation, copy number variation, and clinicopathological data of PC patients were obtained from The Cancer Genome Atlas (TCGA) dataset. A total of 178 PC samples were used to develop distinct molecular subgroups based on the 4227 prognosis-related CpG sites. By using consensus clustering analysis, four prognosis-related molecular subgroups were identified based on DNA methylation. The molecular characteristics and clinical features analyses based on the subgroups offered novel insights into the development of PC. Furthermore, we built a risk score model based on the expression data of five CpG sites to predict the prognosis of PC patients by using Lasso regression. Finally, the risk score model and other independent prognostic clinicopathological information were integrative utilised to construct a nomogram model.ConclusionNovel prognosis-related molecular subgroups based on the DNA methylation signature were established. The specific five CpG sites model for PC prognostic prediction and the derived nomogram model are effective and intuitive tools. Moreover, the construction of molecular subgroups based on the DNA methylation data is an innovative complement to the traditional classification of PC and may contribute to precision medicine development, therapeutic efficacy prediction, and clinical decision guidance.

Project description:Sinonasal undifferentiated carcinoma (SNUC) is an aggressive malignancy harboring IDH2 R172 mutations in >80% cases. We explored the potential of genome-wide DNA methylation profiling to elucidate tumor biology and improve the diagnosis of sinonasal undifferentiated carcinoma and its histologic mimics. Forty-two cases, including sinonasal undifferentiated, large cell neuroendocrine, small cell neuroendocrine, and SMARCB1-deficient carcinomas and olfactory neuroblastoma, were profiled by Illumina Infinium Methylation EPIC array interrogating >850,000 CpG sites. The data were analyzed using a custom bioinformatics pipeline. IDH2 mutation status was determined by the targeted exome sequencing (MSK-IMPACTTM) in most cases. H3K27 methylation level was assessed by the immunohistochemistry-based H-score. DNA methylation-based semi-supervised hierarchical clustering analysis segregated IDH2 mutants, mostly sinonasal undifferentiated (n = 10) and large cell neuroendocrine carcinomas (n = 4), from other sinonasal tumors, and formed a single cluster irrespective of the histologic type. t-distributed stochastic neighbor embedding dimensionality reduction analysis showed no overlap between IDH2 mutants, SMARCB1-deficient carcinoma and olfactory neuroblastoma. IDH2 mutants demonstrated a global methylation phenotype and an increase in repressive trimethylation of H3K27 in comparison to IDH2 wild-type tumors (p < 0.001). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed no difference in pathway activation between IDH2-mutated sinonasal undifferentiated and large cell neuroendocrine carcinomas. In comparison to SMARCB1-deficient, IDH2-mutated carcinomas were associated with better disease-free survival (p = 0.034) and lower propensity for lung metastasis (p = 0.002). ARID1A mutations were common in small cell neuroendocrine carcinoma but not among IDH2 mutants (3/3 versus 0/18 and p < 0.001). IDH2 mutations in sinonasal carcinomas induce a hypermethylator phenotype and define a molecular subgroup of tumors arising in this location. IDH2-mutated sinonasal undifferentiated carcinoma and large cell neuroendocrine carcinoma likely represent a phenotypic spectrum of the same entity, which is distinct from small cell neuroendocrine and SMARCB1-deficient sinonasal carcinomas. DNA methylation-based analysis of the sinonasal tumors has potential to improve the diagnostic accuracy and classification of tumors arising in this location.

Dataset Information

DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups.

Publications

DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups.

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