Methylomics and cancer: the current state of methylation profiling and marker development for clinical care

Liu, Chengyin; Tang, Han; Hu, Nana; Li, Tianbao

doi:10.1186/s12935-023-03074-7

Cited by 8 publications

(2 citation statements)

References 142 publications

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“…The selection of these two omics was motivated by the close relationship existing between them. Indeed, whereas RNA sequencing drives all cell processes, methylation of small regions of DNA might affect transcript reading [17], significantly impacting cancer regulation [18]. In particular, in the context of glioma, methylation might explain the pathogenesis and clinical behavior of the different cancer types [19, 20], therefore being promising information to include in a multi-omics study aiming at understanding glioma heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

A Novel Tool for Multi-Omics Network Integration and Visualization: A Study of Glioma Heterogeneity

Coletti,

Carrilho,

Martins

et al. 2024

Preprint

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Gliomas are highly heterogeneous tumors with generally poor prognoses. Leveraging multi-omics data and network analysis holds great promise in uncovering crucial signatures and molecular relationships that elucidate glioma heterogeneity. However, the complexity of the problem and the high dimensionality of the data increase the challenges of integrating information across various biological levels. In this study, we developed a framework comprising two steps for variable selection based on sparse network estimation from various omics. Subsequently, we introduced MINGLE (Multi-omics Integrated Network for GraphicaL Exploration), a novel methodology designed to merge distinct multi-omics information into a single network, enabling the identification of underlying relations through an innovative integrated visualization. Applying this method to glioma data, with patients grouped according to the newest glioma classification guidelines, led to the selection of variables as potential candidates for novel glioma-type-specific biomarkers.

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Section: Introductionmentioning

confidence: 99%

A Novel Tool for Multi-Omics Network Integration and Visualization: A Study of Glioma Heterogeneity

Coletti,

Carrilho,

Martins

et al. 2024

Preprint

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“…Research suggests that alterations in genomic methylation patterns, including the increased methylation of tumor suppressor gene expression and the decreased methylation of oncogenes, manifest early in the carcinogenic process. Alterations in genomic methylation patterns promotes the expression of oncogenes, leading to genomic instability ( 13 , 14 ).…”

Section: Introductionmentioning

confidence: 99%

Combining non-invasive liquid biopsy and a methylation analysis to assess surgical risk for early esophageal cancer

Chen,

Li,

et al. 2024

Transl Cancer Res

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Background While the widespread use of endoscopic submucosal dissection (ESD) has significantly reduced the incidence of early esophageal cancer (ESCA), the limited ability of ESD to strip deep infiltrating esophageal lesions results in a considerable risk of intraoperative perforation. Circulating-free DNA (cfDNA) is widely used in modern tumor screening due to its non-invasive detection capabilities. A methylation analysis offers vital insights into the condition and advancement of malignancies due to its unique positioning, such as a marker of cancer. This study investigated the potential of combining a non-invasive liquid biopsy technique, along with a methylation analysis, to assess the surgical perforation risk of ESCA patients. Methods In this study, we conducted an analysis of gene expression differences between stage I esophageal squamous carcinoma samples and healthy tissue samples using data from The Cancer Genome Atlas (TCGA) database. We also identified the genes associated with progression-free survival (PFS) in esophageal squamous carcinoma. Integrating the framework of the methylation analysis, we explored the methylated sites of these distinct genes. To refine this process, we used the Shiny Methylation Analysis Resource Tool (SMART) to conduct a comprehensive analysis of these sites. We then confirmed the stability of the methylation sites in different lesion conditions using methylation-specific quantitative polymerase chain reaction (MS-qPCR) with paraffin tissue samples collected after ESD. Results We analyzed RNA-sequencing data from 42 early stage ESCA patients and 17 controls, identifying 1,263 up-regulated and 460 down-regulated genes. Functional analyses revealed involvement in key pathways such as cell cycle regulation and immune responses. Furthermore, we identified 38 differentially expressed genes associated with PFS. Using SMART analysis, we found 217 hyper-methylated regions in 38 genes, suggesting potential early markers for ESCA. Validation experiments confirmed the reliability of 29 hyper-methylated regions in FFPE tissue samples and 6 regions in cfDNA. A LunaCAM model showed high accuracy [area under the curve (AUC) =0.89] in discriminating early ESCA. Integrated assessment of six highly methylated regions significantly improved predictive performance, with 90.56% sensitivity, highlighting the importance of combinatorial biomarker evaluation for early cancer detection. Conclusions This study established a novel approach that integrates non-invasive testing with a methylation analysis to assess the surgical risk of early ESCA patients. The significance of changes in methylation sites in relation to lesion status should not be underestimated, as they have the potential to offer vital insights for proactive risk assessments before surgery.

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Genome-wide methylation profiling differentiates benign from aggressive and metastatic pituitary neuroendocrine tumors

Jotanovic,

Boldt,

Burton

et al. 2024

Acta Neuropathol

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Aggressive pituitary neuroendocrine tumors (PitNETs)/adenomas are characterized by progressive growth despite surgery and all standard medical therapies and radiotherapy. A subset will metastasize to the brain and/or distant locations and are termed metastatic PitNETs (pituitary carcinomas). Studies of potential prognostic markers have been limited due to the rarity of these tumors. A few recurrent somatic mutations have been identified, and epigenetic alterations and chromosomal rearrangements have not been explored in larger cohorts of aggressive and metastatic PitNETs. In this study, we performed genome-wide methylation analysis, including copy-number variation (CNV) calculations, on tumor tissue specimens from a large international cohort of 64 patients with aggressive (48) and metastatic (16) pituitary tumors. Twelve patients with non-invasive pituitary tumors (Knosp 0–2) exhibiting an indolent course over a 5 year follow-up served as controls. In an unsupervised hierarchical cluster analysis, aggressive/metastatic PitNETs clustered separately from benign pituitary tumors, and, when only specimens from the first surgery were analyzed, three separate clusters were identified: aggressive, metastatic, and benign PitNETs. Numerous CNV events affecting chromosomal arms and whole chromosomes were frequent in aggressive and metastatic, whereas benign tumors had normal chromosomal copy numbers with only few alterations. Genome-wide methylation analysis revealed different CNV profiles and a clear separation between aggressive/metastatic and benign pituitary tumors, potentially providing biomarkers for identification of these tumors with a worse prognosis at the time of first surgery. The data may refine follow-up routines and contribute to the timely introduction of adjuvant therapy in patients harboring, or at risk of developing, aggressive or metastatic pituitary tumors.

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Methylomics and cancer: the current state of methylation profiling and marker development for clinical care

Cited by 8 publications

References 142 publications

A Novel Tool for Multi-Omics Network Integration and Visualization: A Study of Glioma Heterogeneity

A Novel Tool for Multi-Omics Network Integration and Visualization: A Study of Glioma Heterogeneity

Combining non-invasive liquid biopsy and a methylation analysis to assess surgical risk for early esophageal cancer

Genome-wide methylation profiling differentiates benign from aggressive and metastatic pituitary neuroendocrine tumors

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