Purpose: Glioblastoma is the most aggressive form of brain tumors. A better understanding of the molecular mechanisms leading to its evolution is essential for the development of treatments more effective than the available modalities. Here, we aim to identify molecular drivers of glioblastoma development and recurrence by analyzing DNA CpG methylation patterns in sequential samples.
Methods: DNA was isolated from 22 pairs of primary and recurrent formalin-fixed, paraffin-embedded glioblastoma specimens, and subjected to reduced representation bisulfite sequencing. Bioinformatic analyses were conducted to identify differentially methylated sites and pathways, and biostatistics was used to test correlations among clinical and pathological parameters.
Results: Differentially methylated pathways likely involved in primary tumor development included those of neuronal differentiation, myelination, metabolic processes, synapse organization and endothelial cell proliferation, while pathways differentially active during glioblastoma recurrence involved those associated with cell processes and differentiation, immune response, Wnt regulation and catecholamine secretion and transport.
Conclusion: DNA CpG methylation analyses in sequential clinical specimens revealed hypomethylation in certain pathways such as neuronal tissue development and angiogenesis likely involved in early tumor development and growth, while suggested altered regulation in catecholamine secretion and transport, Wnt expression and immune response contributing to glioblastoma recurrence. These pathways merit further investigations and may represent novel therapeutic targets.
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http://dx.doi.org/10.1007/s00432-020-03349-w | DOI Listing |
Epigenetics
December 2025
Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA.
Perceived discrimination, recognized as a chronic psychosocial stressor, has adverse consequences on health. DNA methylation (DNAm) may be a potential mechanism by which stressors get embedded into the human body at the molecular level and subsequently affect health outcomes. However, relatively little is known about the effects of perceived discrimination on DNAm.
View Article and Find Full Text PDFEpigenomics
January 2025
NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, Southampton, UK.
Aim: We aim to assess association of DNA methylation (DNAm) at birth with total immunoglobulin E (IgE) trajectories from birth to late adolescence and whether such association is ethnicity-specific.
Methods: We examined the association of total IgE trajectories from birth to late adolescence with DNAm at birth in two independent birth cohorts, the Isle of wight birth cohort (IOWBC) in UK ( = 796; White) and the maternal and infant cohort study (MICS) in Taiwan ( = 60; Asian). Biological pathways and methylation quantitative trait loci (methQTL) for associated Cytosine-phosphate-Guanine sites were studied.
Hum Mol Genet
January 2025
Biomedical Research Centre, School of Biological Sciences, University of East Anglia, Norwich Research Park, Earlham Road, Norwich NR4 6PN, United Kingdom.
Genomic imprinting is the parent-of-origin dependent monoallelic expression of genes often associated with regions of germline-derived DNA methylation that are maintained as differentially methylated regions (gDMRs) in somatic tissues. This form of epigenetic regulation is highly conserved in mammals and is thought to have co-evolved with placentation. Tissue-specific gDMRs have been identified in human placenta, suggesting that species-specific imprinting dependent on unorthodox epigenetic establishment or maintenance may be more widespread than previously anticipated.
View Article and Find Full Text PDFBreast Cancer Res
January 2025
Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 246 Clayton Road, Clayton, VIC, 3168, Australia.
Background: Tumour DNA methylation has been investigated as a potential marker for breast cancer survival, but findings often lack replication across studies.
Methods: This study sought to replicate previously reported associations for individual CpG sites and multi-CpG signatures using an Australian sample of 425 women with breast cancer from the Melbourne Collaborative Cohort Study (MCCS). Candidate methylation sites (N = 22) and signatures (N = 3) potentially associated with breast cancer survival were identified from five prior studies that used The Cancer Genome Atlas (TCGA) methylation dataset, which shares key characteristics with the MCCS: comparable sample size, tissue type (formalin-fixed paraffin-embedded; FFPE), technology (Illumina HumanMethylation450 array), and participant characteristics (age, ancestry, and disease subtype and severity).
Geroscience
January 2025
Department of Bioengineering and QB3, University of California, Berkeley, Berkeley, CA, 94720, USA.
Biological age estimation from DNA methylation and determination of relevant biomarkers is an active research problem which has predominantly been tackled with black-box penalized regression. Machine learning is used to select a small subset of features from hundreds of thousands of CpG probes and to increase generalizability typically lacking with ordinary least-squares regression. Here, we show that such feature selection lacks biological interpretability and relevance in the clocks of the first and next generations and clarify the logic by which these clocks systematically exclude biomarkers of aging and age-related disease.
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