Abstract 6760: Anticancer mechanisms of quercetin revealed via a systems biology approach
Notice bibliographique
Résumé
Abstract Introduction: Quercetin is a dietary flavonoid found in apples, onions, and berries. Previous in vitro and in vivo studies have identified its pro-apoptotic, anti-angiogenic, antioxidant, and anti-inflammatory activities, in addition to its ability to sensitize cancer cells to traditional chemotherapies. However, there has not yet been a comprehensive evaluation of its anticancer properties on a large panel of cancer cell lines. We hypothesized that quercetin exhibits varied anticancer effects depending on the particular cell line and its tissue of origin. Methods: For cell survival assessments, 77 cancer cell lines from the NCI60 dataset were seeded into 96-well plates and incubated for 24 hours. DMSO control or quercetin was then added at 5 ten-fold concentrations to a maximum of 100 µM. 48 hours later, the cells were fixed and stained, and cell viability measured with CellTiter-Glo. Next, for transcriptomic profiling, the L1000 assay from The Broad Institute was used. For these experiments, 10 µM of quercetin was added to 9 core cell lines seeded in 384-well plates. Then, the Affymetrix GeneChip HG-U133 Plus 2.0 Array was used to generate a gene expression signature for quercetin, which was then compared with signatures from other compound and genetic perturbations. Connectivity scores were used to classify the L1000 signatures according to similarity to the gene expression changes produced by quercetin. The data for cell survival and transcriptomic profiling were extracted from PharmacoDB and Clue.io, respectively. Results: The most sensitive cell line to quercetin was the glioblastoma cell line U-87/H.Fine (IC50=5.03 µM), and the least sensitive was the renal cell carcinoma cell line TK-10 (IC50 = 5780.61 µM). Overall, quercetin was most potent against myeloid and prostate cancers and least potent against ovary and fallopian tubes cancers. As indicated by high median tau scores, quercetin administration induced gene expression changes similar to NFKB2, PTK2 and EPCAM knock-downs, which are associated with reductions in inflammation, cell proliferation, and cancer growth and progression. The cellular effects of quercetin were analogous to the administration of other anticancer compounds such as CP466722, rhamnetin, and piceatannol. CP46672 can sensitize tumors to ionizing radiation, rhamnetin acts as an antioxidant, and piceatannol is a naturally occurring anticancer agent. Conclusions: This study provides a comprehensive in vitro examination of the impact of quercetin on the viability and gene expression profiles of cancer cells of various origins. Further research on the molecular changes induced by quercetin in animal models and human subjects would help inform the applicability of quercetin in human cancers and guide its use in cancer prevention and treatment. Citation Format: Adriana Goraieb, Adin Aggarwal, Kenneth W. Yip. Anticancer mechanisms of quercetin revealed via a systems biology approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6760.
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) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».