Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach.

ABSTRACT: Tissue-specific gene methylation events are key to the pathogenesis of several diseases and can be utilized for diagnosis and monitoring. Here, we established an in silico pipeline to analyze high-throughput methylome datasets to identify specific methylation fingerprints in three pathological entities of major burden, i.e., breast cancer (BrCa), osteoarthritis (OA) and diabetes mellitus (DM). Differential methylation analysis was conducted to compare tissues/cells related to the pathology and different types of healthy tissues, revealing Differentially Methylated Genes (DMGs). Highly performing and low feature number biosignatures were built with automated machine learning, including: (1) a five-gene biosignature discriminating BrCa tissue from healthy tissues (AUC 0.987 and precision 0.9

SUBMITTER: Karaglani M 

PROVIDER: S-EPMC8952417 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature