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Enregistrement W1698712530 · doi:10.18632/aging.100556

Metformin, aging and cancer

2013· editorial· en· W1698712530 sur OpenAlexaff
Olga Moiseeva, Xavier Deschênes‐Simard, Michaël Pollak, Gerardo Ferbeyre

Notice bibliographique

RevueAging · 2013
Typeeditorial
Langueen
DomaineMedicine
ThématiqueTelomeres, Telomerase, and Senescence
Établissements canadiensUniversité de Montréal
Organismes subventionnairesnon disponible
Mots-clésMetforminCancerInflammationSenescenceCancer cellChemokineMedicineBiologyCancer researchImmunologyInternal medicineEndocrinologyDiabetes mellitus

Résumé

récupéré en direct d'OpenAlex

Many cancers are associated with aging [1]. Metformin, a widely used antidiabetic drug, has been linked to a reduced cancer incidence in some retrospective, hypothesis-generating studies [2]. Since cancer and aging may share certain molecular processes, it is plausible that metformin may prevent cancer by acting on the aging process. Consistent with this idea, several studies report a life span extension in animal models after treatment with metformin [3]. What is the mechanism by which aging may increase cancer incidence? Although many molecular changes correlate with aging, the presence of senescent cells capable of secreting inflammatory cytokines may be involved. This senescence associated secretory phenotype (SASP) consists of multiple cytokines, chemokines, growth factors and extracellular matrix degrading enzymes that can potentially affect normal tissue structure [4]. The SASP probably evolved as a gene expression program to assist the senescent tumor suppression response and tissue repair after damage and should be viewed as an initial adaptive response [5]. However, like acute inflammation, the SASP should be turned off to avoid maladaptive consequences. In some contexts, senescent cells are cleared by professional phagocytic cells [6] and this mechanism avoids any further complications. On the other hand, if senescent cells escape clearance, mechanisms that prevent the SASP should operate to avoid chronic inflammation and tissue disruption. Such endogenous mechanisms for clearing senescent cells or suppressing the SASP may fail with age. As a consequence, chronic SASP may cause a microenvironment in old tissues that facilitates tumor initiation and then stimulates cancer cell growth, motility and angiogenic activity. This unfortunate interaction between senescent cells and cancer cells has been reproduced in experimental mouse models where senescent fibroblasts stimulated tumor progression [4]). The mechanisms of senescent cell clearance and SASP control are not yet known. However, during experiments to study the potential cancer prevention activity of metformin, we found serendipitously that the drug prevented the expression of many proteases, cytokines and chemokines in senescent cells [7]. At the molecular level, we found that metformin interfered with the activation of protein kinases IKK a and b, which are responsible for activating NF-kB, an essential transcription factor for SASP activation. Intriguingly, metformin did not reduce the expression of anticancer cytokines such as interferon and interferon target genes in senescent cells, suggesting that it modulates SASP to reduce its inflammatory potential but retaining its antitumor activity. In addition, metformin did not affect the senescent cell cycle arrest caused by oncogenic ras in primary human cells, suggesting again that it can modulate the SASP without allowing proliferation of potentially malignant cells. The primary site of action of metformin is considered to be the complex I of the electron transport chain [2]. However, molecular details of the interaction between metformin and complex I remain to be identified. Complex I is one of the main cellular sources for reactive oxygen species (ROS) and we have shown that metformin can prevent ROS production by senescent cells [8]. It is thus plausible that ROS links senescence to NF-kB activation and that metformin interferes with this mechanism by acting on complex I (Fig ​(Fig1).1). Metformin is not immunosuppressive so its ability to inhibit NF-kB is likely confined to certain pro-inflammatory contexts such as senescence. We thus propose that metformin prevents cancer by modulating the SASP in tissues where senescent cells were not naturally cleared. Figure 1 Metformin inhibits the activation of IKK kinases in senescent cells Many questions remain to be addressed in order to fully characterize metformin actions. Our results were obtained using cultured senescent fibroblasts and macrophages; other cell types should be studied as well. In addition, it remains to be determined if metformin can achieve this anti-SASP activity in vivo or whether it can influence the clearance of senescent cells by modulating the SASP. Anisimov and colleagues reported that metformin extends life span in female mice but not males [3] and it would be interesting to study whether NF-kB and SASP inhibition by metformin is gender dependent. Additional epidemiological data and laboratory experiments may justify well-designed clinical studies to evaluate metformin as a cancer preventive agent in specific contexts where its recently described actions would be hypothesized to be useful.

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)0,000
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: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,018

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

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.

Tête enseignante Opus0,013
Tête enseignante GPT0,299
Écart entre enseignants0,287 · 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'étudeSans objet
Domainenon disponible
GenreÉditorial

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

En bref

Citations44
Publié2013
Routes d'admission1
Résumé présentoui

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