Notice bibliographique
Résumé
Prostate cancer (PCa), the third leading cause of cancer-related mortality in Canada, is a heterogeneous disease, making it clinically challenging to distinguish indolent from aggressive cases. Additionally, treatment options in advanced disease are very limited and often have dismal outcomes. Thus, there is an immense need to identify novel prognostic markers and therapy targets. Genomic gain at chromosome 16p13.3 was recently shown to be associated with distant metastases and recurrence of PCa. The Δ3, Δ2-Enoyl-CoA Delta Isomerase 1 (ECI1), a metabolic enzyme encoded from this genomic region, is essential for fatty acids β-oxidation which is the primary source of energy of PCa cells. Unpublished data showed that high ECI1 expression was associated with early relapse after radical prostatectomy and its ectopic overexpression in cancer cells increased tumorigenic behavior. It is unknown how ECI1 contributes to PCa tumorigenicity and whether it could impact benign cells or be clinically relevant in more advanced cases. Hereby, we hypothesized that ECI1 could promote tumor initiation, result in aggressive cancer phenotype through multiple effectors and ultimately lead to worse survival.Stable ectopic ECI1 overexpression in benign prostate RWPE-1 cells enhanced cell growth (P < 0.01), clonogenicity (single cell survival) (P < 0.001), and motility (P < 0.001). The latter effect was reversed with siRNA mediated ECI1 knockdown affirming the specificity of the observed phenotype.Gene expression microarray conducted on LNCaP cells following transient ECI1 knockdown and PC-3 cells with ectopic ECI1 overexpression revealed hundreds of differentially expressed genes (False Discovery Rate < 10%). Gene Ontology and Gene Set Enrichment Analysis reported biological processes related to regulation of cell cycle, migration, and apoptosis. Data from both cell lines was cross-referenced and highlighted 124 and 32 common genes positively and negatively regulated with ECI1, respectively. Out of those, 22%, 5%, and 13% were linked to critical tumorigenic functions such as proliferation, motility, and apoptosis, respectively. With few exceptions, genes with oncogenic effects were found among the positively regulated genes. Out of the 156 genes, 56.4% were previously linked to cancer in literature, and consistent with our phenotype, 83.3% of tumor promoting genes were associated with EC1 overexpression.Immunohistochemistry performed on clinical samples obtained from TURP (transurethral resection of the prostate) procedure showed that, although no statistical significance was found between ECI1 expression and PCa specific survival (P = 0.1), ECI1 overexpression status emerged as a predictor of 10-year overall survival (P =0.039). Multivariate analysis showed that ECI1 maintained its prognostic significance after adjusting for age and Gleason grade.These findings further support the role of ECI1 in PCa pathobiology and demonstrate that ECI1 can impact benign prostate cells and might be involved in tumor initiation. The gene expression data provide multiple candidates that could explain the observed phenotype and provide basis for future research in PCa biology. Our results suggest PCa cases can be usefully classified according to their ECI1 expression, which can ultimately improve prognostication and management
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 | 0,000 | 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) | 0,002 | 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 ».