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Enregistrement W2042036664 · doi:10.4161/cc.22935

Micromanaging a large tumor suppressor

2012· letter· en· W2042036664 sur OpenAlexaff
Yanmei Li, David Spaner

Notice bibliographique

RevueCell Cycle · 2012
Typeletter
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueMicroRNA in disease regulation
Établissements canadiensUniversity of TorontoSunnybrook HospitalSunnybrook Health Science Centre
Organismes subventionnairesnon disponible
Mots-clésBiologymicroRNAGene silencingOncogeneUntranslated regionGeneDroshaGeneticsRNACancer researchCell cycleRNA interference

Résumé

récupéré en direct d'OpenAlex

Studies of microRNA (miRNA) in cancer are relatively recent but have exciting possibilities to improve our understanding and treatment of this disease.1 Over 1,000 miRNAs (1–4% of the human genome) exist in non-coding genes or gene clusters or in introns of coding genes. Primary miRNA transcripts are cleaved into stem-loop structures in the nucleus and trimmed into 19–24 nucleotide duplexes in the cytoplasm. One strand of a duplex is incorporated into an RNA-induced silencing complex (RISC), where it binds complementary sequences in 3′-untranslated regions (UTRs) and decreases protein expression by inhibiting translation or causing mRNA degradation. Each miRNA can potentially bind 200 mRNAs and regulate multiple genetic pathways. Aberrant miRNA expression has been documented in a number of cancers. For example, the miR-17-92a cluster consisting of miR-17, -18a, -19a, -19b-1, -20a and -92a-1 is overexpressed in lymphomas and lung cancers and may act like an oncogene.2 In contrast, let-7 family members, miR-15 and miR-16 are downregulated in a number of cancers and may act as tumor suppressors by decreasing the expression of oncogenes or anti-apoptotic proteins such as Ras or Bcl-2.1,3 While members of an miRNA cluster are often expressed coordinately, they may not have identical functions. For example, miR-17 acts like a brake on miR-92a, which appears to be the most potent oncogene of the miR-17-92 cluster.2 In this issue of Cell Cycle, Burton Yang’s laboratory identifies a new role for miR-93 in breast cancer.4 MiR-93 is part of the miR-106b-25 cluster on chromosome 7 and was found to be overexpressed in human primary breast cancer cells compared with normal breast tissue. Overexpression of miR-93 in MT-1 human breast cancer cells caused them to survive better in serum-free conditions and increased their invasive properties and interactions with endothelial cell lines in vitro. These properties were reflected in increased metastatic capability and angiogenesis in immunodeficient mice. To explain these observations, in silico studies implicated the tumor suppressor large tumor suppressor 2 (LATS2) as a miR-93 target. LATS2 is a serine-threonine protein kinase that is activated by mitotic stress, DNA damage and oncogenes and regulates a number of cell processes, including apoptosis and motility.5 LATS2 is downregulated in a number of cancers, including breast cancer, through epigenetic mechanisms involving promoter methylation. However, LATS2 has also been shown to be downregulated by miR-372, miR-373, miR-31 and miR-195 in a number of cancer cell lines.5,6 Fang, et al. found a reciprocal relationship between LATS2 protein expression and miR-93 in breast cancer lines and primary cells. Expression of a luciferase construct harboring the target site in the LATS2 3′-UTR was decreased by miR-93, but not when this region was mutated to prevent binding of miR-93. Treatment of MT-1 cells with siRNA to LATS2 conferred the same phenotype as miR-93, while ectopic expression of a LATS2 gene lacking the 3′-UTR reversed the effects of miR-93. Taken together, these results establish miR-93 as an oncogene that downregulates LATS2 and promotes malignant behavior of breast cancer cells. This study is an important contribution to our understanding of breast cancer. However, as with any good research, the findings raise additional questions. Why is miR-93 upregulated? Is there a cytogenetic basis for this finding? Interestingly, previous reports documented loss of heterozygosity at this locus rather than amplification.7 Alternatively, is there a constitutively activated signaling pathway that increases miR-93 expression? Is miR-93 associated with specific breast cancer types?8 It is interesting that so many different miRNAs appear to regulate LATS2. This observation probably attests to the biological importance of this molecule, but is LATS2 the only pathogenic protein that is regulated by miR-93 in breast cancer? Certainly, increased interactions with endothelial cells suggest that cytokines and adhesion molecules are likely also increased by miR-93 and may be independent of LATS2. What is the role of the other members of the miR-106b-25 cluster? Are miR-106b and miR-25 also oncogenes, or do they function to balance the oncogenic properties of miR-93, as described for the paralogous miR-17–92 cluster?2 Intriguing therapeutic possibilities arise from the finding that miR-93 is overexpressed in breast cancer. In the future, the results of Fang and coworkers may lead to more effective breast cancer treatments that use agents such as anti-sense oligonucleotides1 to block miR-93.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,330
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,006
Tête enseignante GPT0,215
Écart entre enseignants0,209 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
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

Citations0
Publié2012
Routes d'admission1
Résumé présentoui

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