MétaCan
Menu
← Retour à la cohorte
Enregistrement W4389243236 · doi:10.1182/blood-2023-173489

DNA Methylation Instability: A Novel Biomarker for Aging, Clonality, and Cancer

2023· article· en· W4389243236 sur OpenAlexaff
Salman Basrai, Robert Kridel, Mehran Bakhtiari, Dennis Dong Hwan Kim, Fernando Luís Scolari, Filio Billia, Andrea Arruda, Mark D. Minden, Sagi Abelson

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueEpigenetics and DNA Methylation
Établissements canadiensToronto General HospitalPrincess Margaret Cancer CentreUniversity Health NetworkOntario Institute for Cancer Research
Organismes subventionnairesnon disponible
Mots-clésDNA methylationEpigeneticsBiologyCpG siteMethylationGenome instabilityGeneticsDifferentially methylated regionsGeneCancer researchMolecular biologyDNADNA damageGene expression

Résumé

récupéré en direct d'OpenAlex

Background: Clonal hematopoiesis (CH) is an age-related condition defined by the over-representation of blood cells derived from a single clone. CH has been associated with an increased risk of leukemia development, adverse cardiovascular events, and all-cause mortality. There has been great emphasis on investigating CH at the genomic level, yet the epigenetic factors that regulate the development and progression of pre-malignant clones haven't been extensively studied. Methods: Here, we develop a novel framework that employs DNA methylation (DNA-M) to detect abnormal clonal expansion of hematopoietic cells. DNA-M profiles of >1500 blood samples from healthy, young persons (age = 18) were interrogated to identify CpG sites with highly consistent methylation levels across individuals. DNA methylation instability (DMI), which we define as deviation in methylation levels at the identified stable methylation sites (SMSs), was evaluated in diverse healthy and cancer cohorts to investigate how epigenetic mechanisms may affect cellular functions resulting in clonality. Results: Characterization of SMSs. SMSs were found to be predominantly unmethylated and significantly enriched within CpG islands (OR: 13.8, p < 2 x 10 -16) and gene promoter regions (OR: 4.8, p < 2 x 10 -16). A gene ontology enrichment analysis implicated the corresponding genes in vital cellular processes such as DNA repair, damage response, and cell cycle checkpoints (p < 0.002). Consistently low methylation levels at SMSs were observed across major blood cell types. Moreover, analysis of a methylome cell atlas derived from whole genome bisulphite sequencing (WGBS) of 40 healthy human tissues revealed that SMSs remain stably unmethylated in the vast majority of tissues, suggesting broad and strict regulation at those sites. DMI in overt blood cancers. Interrogation of SMS methylation levels across diverse control and cancer datasets revealed significantly higher destabilization in cancer samples (Fig. 1, p < 2 x 10 -16). Moreover, longitudinal profiling of bone marrow samples from AML patients (n = 4, four time points each) indicated that DMI levels follow the clonal burden patterns expected during treatment. Analysis of serial dilutions of AML cell lines in cord blood further corroborated the link between DMI levels and malignant cell fractions (p < 2 x 10 -16). DMI in non-cancer cohorts. Given the link between DMI and clonality, we measured DMI in blood samples of three independent aging cohorts of individuals without a malignancy (n = 1751, age 14-101) and observed a positive correlation with age (r = 0.25, 0.38, 0.4; p < 7 x 10 -11). These results are in accordance with the increasing incidence of CH with age and suggest that DMI, much like genomic mutations, may causally precede the development and progression of various age-related diseases. CH has previously been associated with adverse cardiovascular outcomes. In a competing risk analysis of 64 cardiogenic shock patients (CH+ = 31, CH- = 33), DMI was better able to stratify patients into risk groups than genomics-based CH detection (Kaplan-Meier analysis, CH: p = 0.46, DMI: p = 0.01). These results indicate that DMI is significantly associated with mortality in high-risk patients. DMI and gene regulation. WGBS of AML and healthy control samples revealed that significantly destabilized SMSs in AML (p < 7 x 10 -6) near transcriptional start sites reflect broader hypermethylation of promoter regions. Transcriptome profiling of age-stratified controls (n = 755) and AML samples (n = 151) confirmed a gradual decrease in expression for many of the implicated genes. Notably, genes known to suppress cell proliferation and promote apoptosis such as BASP1 were implicated (Fig. 2, r = -0.20, p < 2 x 10 -16), as well as novel genes that have not previously been associated with age-related gene silencing and leukemia development. These findings further emphasize that DMI reflects a gradual process that precedes disease. Conclusion: Our results suggest that the development of clonality is not merely a genomic phenomenon and that the gradual silencing of specific genes via DMI in their promoter regions may result in positive selection and clonal dominance. This work has significant implications for elucidating the molecular mechanisms underlying age-related diseases, early detection of cancer, and the development of novel biomarkers and therapeutic strategies against hematologic malignancies.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,040
Tête enseignante GPT0,324
Écart entre enseignants0,283 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueBlood→Même sujetEpigenetics and DNA Methylation→Travaux en français237 207→