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

Chen, Wenbiao; Jia, Zhuang; Jiang, Jingjing; Lin, Changpo; Zeng, Ping; Yan, Liang; Zhang, Xujun; Dai, Yong; Diao, Hongyan

doi:10.1186/s12935-019-0900-4

Cited by 29 publications

(30 citation statements)

References 53 publications

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“…Still, such analyses may well overlook more subtle or focal changes of the methylome and at least for adult glioblastoma some degree of temporal evolution has been reported . Nonetheless there seems to be a relative persistence of the tumoral methylation pattern which provides the foundation for the utility of DNA methylation‐based analyses for cell lineage tracing and by extension, the identification of the origin of metastases of unknown primaries and the rapidly developing field of DNA methylation‐based tumour classification . A graphical summary of the evolution of the field within the context of brain tumours is displayed in Figure .…”

Section: Dna Methylation In Cancermentioning

confidence: 99%

Invited Review: DNA methylation‐based classification of paediatric brain tumours

Perez

Capper

2020

Neuropathology Appl Neurobio

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2020) Neuropathology and Applied Neurobiology 46, 28-47 DNA methylation-based classification of paediatric brain tumours DNA methylation-based machine learning algorithms represent powerful diagnostic tools that are currently emerging for several fields of tumour classification. For various reasons, paediatric brain tumours have been the main driving forces behind this rapid development and brain tumour classification tools are likely further advanced than in any other field of cancer diagnostics. In this review, we will discuss the main characteristics that were important for this rapid advance, namely the high clinical need for improvement of paediatric brain tumour diagnostics, the robustness of methylated DNA and the consequential possibility to generate high-quality molecular data from archival formalin-fixed paraffin-embedded pathology specimens, the implementation of a single array platform by most laboratories allowing data exchange and data pooling to an unprecedented extent, as well as the high suitability of the data format for machine learning. We will further discuss the four most central output qualities of DNA methylation profiling in a diagnostic setting (tumour classification, tumour sub-classification, copy number analysis and guidance for additional molecular testing) individually for the most frequent types of paediatric brain tumours. Lastly, we will discuss DNA methylation profiling as a tool for the detection of new paediatric brain tumour classes and will give an overview of the rapidly growing family of new tumours identified with the aid of this technique.

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Section: Dna Methylation In Cancermentioning

confidence: 99%

Invited Review: DNA methylation‐based classification of paediatric brain tumours

Perez

Capper

2020

Neuropathology Appl Neurobio

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“…For example, genotyping methods based on genomics have been widely used to classify tumors, thus conducting the clinical trials. Recent studies have confirmed that DNA methylation provide insights into various tumor early diagnosis, molecular classification, and precise treatment [34,14,30].…”

Section: Discussionmentioning

confidence: 96%

“…DNA methylation is a posttranslational modification process, which are selectively occurred on the cytosines of 5′-CpG-3′ to generates 5-methyldeoxycytidine. The aberrant of DNA methylation, especially in CpG-rich regions (CpG islands), has been found closely related with physio-pathologic mechanisms underlying an array of human diseases [14]. Moreover, CpG islands was frequently detected in the promoter regions of the structural transcription gene [15], and abnormal CpG island hypermethylation of various tumor suppressor genes and hypomethylation of oncogenes play vital role in carcinogenesis [16,17].…”

Section: Introductionmentioning

confidence: 99%

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DNA methylation patterns–based subtype distinction and identification of soft tissue sarcoma prognosis

Fan

et al. 2020

Preprint

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Background: Soft tissue sarcomas (STSs) are heterogeneous at the clinical with a variable tendency of aggressive behavior. Methods: In this study, we constructed a specific DNA methylation-based classification to identify the distinct prognosis-subtypes of STSs based on the DNA methylation spectrum from the TCGA database.Results: Eventually, samples were clustered into four subgroups, and their survival curves were distinct from each other. Meanwhile, the samples in each subgroup reflected differentially in several clinical features. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was also conducted on the genes of the corresponding promoter regions of the above‐described specific methylation sites, revealing that these genes were mainly concentrated in certain cancer‑associated biological functions and pathways. In addition, we calculated the differences among clustered methylation sites and performed the specific methylation sites with LASSO algorithm. The selection operator algorithm was employed to derive a risk signature model, and a prognostic signature based on these methylation sites performed well for risk stratification in STSs patients. At last, a nomogram consisted of clinical features and risk score was developed for the survival prediction. Conclusion: In conclusion, this study declares that DNA methylation-based STSs subtype classification is highly relevant for future development of personalized therapy as it identifies the prediction value of patient prognosis.

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“…The detailed molecular mechanisms that underlie RCC development remain poorly understood. However, an increasing number of studies have determined that the inactivation of TSG is frequently involved in the tumorigenesis of RCC as a result of epigenetic abnormalities in DNA methylation [1][2][3][4][5][6] .…”

Section: Introductionmentioning

confidence: 99%

Aberrant Methylation of the 16q22.1 Tumor Suppressor Gene CDH11 Promotes Tumorigenesis and Progression of Renal Cell Carcinoma through NF-kB Pathway

Xu¹,

Song²,

Luo³

et al. 2020

Preprint

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Background: To discover new epigenetic biomarkers for early cancer detection. The CDH11 gene has been reported as a critical tumor suppressor gene (TSG) for multiple tumors, although it has not yet been reported in renal cell carcinoma (RCC). We explored its epigenetic alteration in RCC and analyzed the possible biological function and mechanism in tumorigenesis and progression of RCC.Materials and Methods: We examined CDH11 gene expression and methylation using semiquantitative reverse transcriptase PCR (RT-PCR) and methylation-specific polymerase chain reaction (MSP) in RCC cell lines before and after treatment with 5-aza-2’-deoxycytidine (5-Aza). MSP was further applied to 93 RCC primary tumors, and the relationship between CDH11 gene methylation and clinicopathological features was discussed. A selection of the cell lines and specimens was subsequently examined using bisulfite genomic sequencing (BGS) and real-time PCR. Meanwhile, assays of cell viability, colony formation, migration and invasion, wound healing, and western blot were performed to confirm the tumor-suppressive function and mechanism of CDH11 gene.Results: CDH11 gene methylation was detected in 4 of 5 RCC cell lines with silenced expressions. Treatment with 5-Aza reversed methylation and restored CDH11 gene expression. Aberrant methylation was further detected in 41 of the 93 (44.1%) primary tumors. Furthermore, CDH11 gene methylation was significantly associated with tumor stage and nuclear grade in patients with RCC (p<0.05). Due to the phenomenon of aberrant methylation, ectopic low-level expression of CDH11 gene could result in promotion of tumorigenesis and progression in RCC cell lines, which might be mediated through NF-kB pathway. Conclusions: The CDH11 gene is often down-regulated by aberrant promoter methylation in RCC cell lines and primary tumors, indicating its critical role as a TSG in RCC. It may be involved in the tumorigenesis and progression in RCC through NF-kB pathway, thus which could potentially serve as a novel biomarker and therapeutic target for RCC.

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

Cited by 29 publications

References 53 publications

Invited Review: DNA methylation‐based classification of paediatric brain tumours

Invited Review: DNA methylation‐based classification of paediatric brain tumours

DNA methylation patterns–based subtype distinction and identification of soft tissue sarcoma prognosis

Aberrant Methylation of the 16q22.1 Tumor Suppressor Gene CDH11 Promotes Tumorigenesis and Progression of Renal Cell Carcinoma through NF-kB Pathway

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