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Identification of a DNA methylation signature for Renpenning syndrome (RENS1), a spliceopathy.

ABSTRACT: The challenges and ambiguities in providing an accurate diagnosis for patients with neurodevelopmental disorders have led researchers to apply epigenetics as a technique to validate the diagnosis provided based on the clinical examination and genetic testing results. Genome-wide DNA methylation analysis has recently been adapted for clinical testing of patients with genetic neurodevelopmental disorders. In this paper, preliminary data demonstrating a DNA methylation signature for Renpenning syndrome (RENS1 - OMIM 309500), which is an X-linked recessive neurodevelopmental disorder caused by variants in polyglutamine-binding protein 1 (PQBP1) is reported. The identified episignature was then utilized to construct a highly sensitive and specific binary classification model. Besides providing evidence for the existence of a DNA methylation episignature for Renpenning syndrome, this study increases the knowledge of the molecular mechanisms related to the disease. Moreover, the availability of more subjects in future may facilitate the establishment of an episignature that can be utilized for diagnosis in a clinical setting and for reclassification of variants of unknown clinical significance.

SUBMITTER: Haghshenas S

PROVIDER: S-EPMC10400603 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Identification of a DNA methylation signature for Renpenning syndrome (RENS1), a spliceopathy.

Haghshenas Sadegheh S Foroutan Aidin A Bhai Pratibha P Levy Michael A MA Relator Raissa R Kerkhof Jennifer J McConkey Haley H Skinner Cindy D CD Caylor Raymond C RC Tedder Matthew L ML Stevenson Roger E RE Sadikovic Bekim B Schwartz Charles E CE

European journal of human genetics : EJHG 20230216 8

The challenges and ambiguities in providing an accurate diagnosis for patients with neurodevelopmental disorders have led researchers to apply epigenetics as a technique to validate the diagnosis provided based on the clinical examination and genetic testing results. Genome-wide DNA methylation analysis has recently been adapted for clinical testing of patients with genetic neurodevelopmental disorders. In this paper, preliminary data demonstrating a DNA methylation signature for Renpenning synd ...[more]

PMID: 36797465

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Project description:BACKGROUND:The identification of prognostic biomarkers for cancer patients is essential for cancer research. These days, DNA methylation has been proved to be associated with cancer prognosis. However, there are few methods which identify the prognostic markers based on DNA methylation data systematically, especially considering the interaction among DNA methylation sites. METHODS:In this paper, we first evaluated the stabilities of microRNA, mRNA, and DNA methylation data in prognosis of cancer. After that, a rank-based method was applied to construct a DNA methylation interaction network. In this network, nodes with the largest degrees (10% of all the nodes) were selected as hubs. Cox regression was applied to select the hubs as prognostic signature. In this prognostic signature, DNA methylation levels of each DNA methylation site are correlated with the outcomes of cancer patients. After obtaining these prognostic genes, we performed the survival analysis in the training group and the test group to verify the reliability of these genes. RESULTS:We applied our method in three cancers (ovarian cancer, breast cancer and Glioblastoma Multiforme). In all the three cancers, there are more common ones of prognostic genes selected from different samples in DNA methylation data, compared with gene expression data and miRNA expression data, which indicates the DNA methylation data may be more stable in cancer prognosis. Power-law distribution fitting suggests that the DNA methylation interaction networks are scale-free. And the hubs selected from the three networks are all enriched by cancer related pathways. The gene signatures were obtained for the three cancers respectively, and survival analysis shows they can distinguish the outcomes of tumor patients in both the training data sets and test data sets, which outperformed the control signatures. CONCLUSIONS:A computational method was proposed to construct DNA methylation interaction network and this network could be used to select prognostic signatures in cancer.

Dataset Information

Identification of a DNA methylation signature for Renpenning syndrome (RENS1), a spliceopathy.

Publications

Identification of a DNA methylation signature for Renpenning syndrome (RENS1), a spliceopathy.

Similar Datasets

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