Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA381920 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Methylome analysis of different histological thyroid lesions and clinical features, aiming to better understand the DNA methylation deregulation of TC and to identify a prognostic epigenetic signature in well differentiated thyroid carcinomas.
2017-10-20 | GSE97466 | GEO
Project description:Meningiomas are common intracranial tumors with complex behavior that can be difficult to predict. Historically, morphology has been used to predict tumor aggressiveness and risk of recurrence but has limitations as a prognostic tool. Recent work has shown the value of DNA methylation, transcriptomic, and copy number data for identifying groups of tumors which have distinct biological signatures and predicting recurrence risk. Here, we describe development, clinical validation, and implementation of a methylation classifier based on k-means clustering for prognostic stratification of meningiomas. Previously published work has validated the concept of meningioma prognostic stratification through DNA methylation data, but our system is unique in that it is the first clinically validated classifier which identifies risk groups based exclusively on DNA methylation signatures. This work has the potential to improve diagnostic workup, recurrence risk prediction, and clinical management of meningiomas.
2026-01-12 | GSE292325 | GEO
Project description:Meningiomas are common intracranial tumors with complex behavior that can be difficult to predict. Historically, morphology has been used to predict tumor aggressiveness and risk of recurrence but has limitations as a prognostic tool. Recent work has shown the value of DNA methylation, transcriptomic, and copy number data for identifying groups of tumors which have distinct biological signatures and predicting recurrence risk. Here, we describe development, clinical validation, and implementation of a methylation classifier based on k-means clustering for prognostic stratification of meningiomas. Previously published work has validated the concept of meningioma prognostic stratification through DNA methylation data, but our system is unique in that it is the first clinically validated classifier which identifies risk groups based exclusively on DNA methylation signatures. This work has the potential to improve diagnostic workup, recurrence risk prediction, and clinical management of meningiomas.
2026-01-12 | GSE292326 | GEO
Project description:Around 20-30% of Thyroid cancers are difficult to accurately diagnose with fine needle aspiration (FNA) due to their shared features with benign nodules. Although there are molecular tools to assist the diagnosis, such as the Afirma Gene Expression Classifier and ThyroSeqv3, which are the most widely used in the United States, however, these tests all show low positive predictive value and would lead to unnecessary removal of the thyroid in non-cancer patients. In addition, the prediction model of these tests is trained mainly based on the American population. Therefore, it is required to develop a highly accurate test for thyroid nodule diagnostic which could not only increase the PPV, and reduce the unnecessary surgeries, but also is based on Chinese population datasets. To overcome the potential overfitting issues of the prediction model, here we provide a function-guided DNA methylation biomarker selection method through multi-omics datasets. To develop a novel DNA methylation based diagnostic test for thyroid cancer, we characterized the genome-wide DNA methylation pattern in 49 malignant and benign fresh thyroid tissues by using Reduced Representation Bisulfite Sequencing (RRBS) analysis on a single nucleotide level. And we found a group of differentiated DNA methylation sites that could be used to distinguish thyroid cancer and benign tissues.
2025-05-26 | GSE233560 | GEO
Project description:By characterizing at the genome-wide level the DNA methylation patterns of the largest series of well-differentiated thyroid tumors described to date, we provide novel insights into the biology underlying on the one hand the histological heterogeneity, and on the other differential patient outcomes of this disease. We describe distinct subtype- and mutational-specific methylation profiles as well as novel markers associated with recurrence-free survival, which could provide an improved classification of patients. Bisulphite converted DNA from the 83 primary thyroid tumor samples and 8 adjacent normal tissue samples were hybridized to the Illumina Infinium 27k Human Methylation Beadchip v1.2
2014-07-31 | E-GEOD-51090 | biostudies-arrayexpress
Project description:By characterizing at the genome-wide level the DNA methylation patterns of the largest series of well-differentiated thyroid tumors described to date, we provide novel insights into the biology underlying on the one hand the histological heterogeneity, and on the other differential patient outcomes of this disease. We describe distinct subtype- and mutational-specific methylation profiles as well as novel markers associated with recurrence-free survival, which could provide an improved classification of patients.
2014-07-31 | GSE51090 | GEO
Project description:A novel approach of reference-free deconvolution of large-scale DNA methylation data enabled to develop a machine learning classifier based on CpG sites, specific for Latent Methylation Components (LMC), that allowed for patient allocation to prognostic clusters. DNA methylation data was processed using reference-free analyses (MeDeCom) and reference-based computational tumor deconvolution (MethylCIBERSORT, LUMP). These results demonstrate that LMC-based segregation of large-scale DNA methylation data is a promising tool for classifier development and treatment response estimation in cancer patients under targeted immunotherapy.
2021-07-22 | GSE175699 | GEO
Project description:Using a genome-wide DNA methylation profiling of 186 cervical samples from women with different CIN grades and well-characterized HPV genotyping, we identified novel methylation markers of epigenetic changes that discriminate accurately between clinically significant and transient cervical disease. In particular, a 2-gene DNA methylation classifier (ATP10A and HAS1) showed a promising ability to discriminate among pre-invasive cervical lesion grades. The identified markers are excellent candidates for future diagnostic or prognostic assays in cervical cancer screening.
2020-01-30 | GSE143752 | GEO
Project description:The 2021 WHO Classification of Tumors of the Central Nervous System includes several tumor types and subtypes for which the diagnosis is at least partially reliant on utilization of whole genome methylation profiling. The current approach to array DNA methylation profiling utilizes a reference library of tumor DNA methylation data, and a machine learning-based tumor classifier. This approach was pioneered and popularized by the German Cancer Research Network (DKFZ) and University Hospital Heidelberg. This research group has kindly made their classifier for central nervous system tumors freely available as a research tool via a web-based portal. However, this classifier is not maintained in a clinical testing environment. Therefore, we validated our own DNA methylation-based classifier of central nervous system tumors. We validated our classifier using the same training and validation datasets as the DKFZ group. In addition, we performed a validation of samples tested in our own laboratory and compared the performance of both classifiers. Using the validation data set, our classifier’s performance showed high concordance (92%) and comparable accuracy (specificity 94.0% v. 84.9% for DKFZ, sensitivity 88.6% v. 94.7% for DKFZ). Receiver operator curve showed areas under the curve of 0.964 v. 0.966 for NM and DKFZ classifiers, respectively. Our classifier performed comparably well with samples tested in our own laboratory and is currently offered for clinical testing.
2022-03-19 | GSE198855 | GEO
Project description:Background: Modern neuropathology is challenged by an increasing number of clinically-relevant CNS tumor subgroups that require assessment of a multitude of molecular markers for classification, as well a highly trained medical staff. Failure to meet this challenge leads to tumor misclassification, which can have severe consequences for affected patients. Methods: We compiled a cohort of genome-wide DNA methylation profiles of 2,682 tumors from 82 histologically and/or molecularly distinct CNS tumor classes across all ages and histologies that served as reference for a Random Forest-based diagnostic classifier. This classifier was used to prospectively investigate a further 1,104 CNS tumor samples in order to determine its clinical utility. Results: The classifier was able to reliably assign tumor samples to a given diagnostic category with a misclassification rate of less than 2%. The system functioned robustly across laboratories and using different DNA methylation profiling techniques. Prospective application to clinical samples resulted in a reclassification of 12% of tumors compared with standard practice alone. A further 12% could not be classified by methylation profiling – this subset was highly enriched for unusual syndrome-associated tumors and likely novel entities. Conclusion: This study represents a proof-of-concept for the application of machine learning approaches in molecular diagnostics using a single, easy-to-use assay. The reference cohort and Random Forest-based classifier are available online as a valuable community tool for improving precision in brain tumor diagnostics. We expect that approaches similar to the one presented herein will rapidly restructure diagnostic practice in neurooncology and across tumor pathology.
2018-01-18 | GSE109379 | GEO