This study aims to investigate the functional consequences of specific methylation changes and to examine the clinical implications of these alterations. Methylation analysis is a powerful tool for understanding the molecular mechanisms of brain tumors and other solid tumors and can help identify new diagnostic and therapeutic targets. By analyzing DNA methylation patterns in tumor tissue, it is possible to identify genes that are silenced or activated by methylation. This information can be used to develop new biomarkers for early diagnosis and to identify new therapeutic targets that can be used to reverse methylation-mediated gene silencing.
Confirmed diagnosis of one of the following types of cancer: gliomas, malignant thyroid neoplasms, or endometrial carcinomas (according to the WHO Nomenclature)
Availability of adequate material for analysis at the laboratory, specifically availability of at least 3 sections of 10 µm of FFPE
no
The study population will include patients aged 18 years or older who have been diagnosed with adult gliomas, malignant thyroid neoplasms, or endometrial carcinomas. The patient selection period will run from January 2021 to December 2023. The operational unit responsible for patient selection will be the Intercompany Laboratory of Molecular Pathology of Solid Tumors - IRCCS - University Hospital of Bologna - Policlinico S. Orsola. The study will be retrospective and will be based on material already collected from normal clinical practice.
Cohort
Retrospective
Not listed
Identify methylation profiles
The primary objective of this project is to perform a comprehensive analysis of methylation in glial tumors, endometrial carcinomas, and endocrine neoplasms, with the overall goal of improving their classification and translating the data obtained into clinical practice. Specifically, this project aims to: 1. Characterize DNA methylation patterns in the solid tumors under investigation: this research aims to clarify the specific DNA methylation profiles associated with different types of solid tumors, allowing the identification of unique epigenetic signatures that may contribute to tumor classification; 2. Develop robust predictive models for the classification of the tumors studied; 3. Validate classification models using clinical data; 4. Assess the relevance of methylation-based tumor classification to determine the clinical utility of classification models; 5. Developing an easy-to-use clinical implementation framework.
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