Abstract 158: The landscape of DNA hypomethylation in liver cancer, its correlation with other cancers, and its potential role in cancer
Notice bibliographique
Résumé
Abstract DNA demethylation, although a common hallmark of cancer, has been relatively ignored. We used hepatic cellular carcinoma, which is one of the most common human cancers, as a model to delineate the landscape of hypomethylation in cancer, unravel the driving forces of hypomethylation and determine its potential role in cancer progression, metastasis and invasion. Using liver cancer cell lines as a model system, we tested whether the demethylation observed in liver cancer is driven by MBD2, a protein previously implicated in demethylation, which we found to be overexpressed in liver cancer. We used genome-wide promoter arrays and methylated DNA immunoprecipitation to map the sites in the genome that lost methyl marks in liver cancer patients. Affymetrix arrays were used for gene expression studies. The array data were validated by pyrosequencing and QPCR. For functional analyses, we used siRNA transfection, soft agar and invasion assays. We discovered that about 3700 transcript-encoding genes were demethylated in liver cancer samples compared to adjacent normal tissue. At least 350 of these 3700 genes were significantly induced in tumors. In the group of demethylated and induced genes, we identified a distinct subgroup of over 100 genes with CpG dense promoters that were highly methylated in normal liver, but demethylated in the tumors. Gene ontology analyses revealed that these genes are involved in cell growth, cell adhesion, signal transduction and invasion. Examination of the Ensemble methylome data suggests that these promoters are differentially methylated in a tissue specific manner but are always methylated in liver. All of the demethylated genes in liver cancer were found to be demethylated in other cancers suggesting that the demethylation of these genes is a basic requirement in cancer. The experiments in HepG2 and SkHep1 cells showed that knock-down of MBD2 with siRNA results in suppression of the majority of the hypomethylated genes and in a relevant decrease in cancer cell growth and invasion. Furthermore, the down-regulation of these genes was accompanied by an increase in promoter methylation and a decrease in MBD2 binding. It suggests that MBD2 is required for demethylation and up-regulation of these genes, which appear to be putative targets for MBD2. Our results demonstrated that DNA hypomethylation drives the coordinated expression of several gene circuitries involved in cancer growth, survival and metastasis. We showed that MBD2 is involved in driving this processes by affecting methylation and expression of important players in liver cancer growth and metastasis. Our studies established for the first time the rules governing hypomethylation in liver cancer and defined the potential functional role of hypomethylation. This study was supported by a grant from the MDEEI program of the government of Quebec and the National Cancer Institute of Canada to MS and MOST to ZGH Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 158.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».