Abstract 1815: Transcriptomic profiling of VHL-dependent long noncoding RNAs in clear cell renal cell carcinoma
Notice bibliographique
Résumé
INTRODUCTION: Clear cell renal cell carcinoma (ccRCC), the most common subtype of kidney cancer, carries a poor prognosis, with an estimated median overall survival time of only two years. This is often a clinically silent disease and about one third of patients present with metastases. Although inactivation of the tumor suppressor gene von Hippel-Lindau (VHL) is a well-characterized driver event in ccRCC, the exact molecular underpinnings of this disease remain unclear. To address this gap, we performed a transcriptomic analysis, focusing on VHL-dependent long noncoding RNAs (lncRNAs). LncRNAs are known to be involved in cancer progression, but their role in ccRCC oncogenesis has not been extensively studied. Accordingly, the aim of the present study is to characterize lncRNA transcripts that are differentially associated with VHL inactivation status. We anticipate that characterization of the lncRNA expression landscape in ccRCC will enable the identification of biomarkers and novel therapeutic targets for this disease.METHODS: Transcriptome-wide array-based analyses were performed on total RNA derived from the 786-O (VHL-/-) ccRCC cell line, stably reconstituted with either wild-type VHL (786-O-VHL) or mutant VHL (786-O-C162F). Differential lncRNA analysis was conducted using the Arraystar Human LncRNA V4.0 array. Statistical analyses were performed using Agilent GeneSpring GX v12.1 software, with a false discovery rate (FDR) adjusted p-value < 0.05 used as a threshold for significance of differential lncRNA expression. LncRNAs were cross-referenced to data generated from The Cancer Genome Atlas (TCGA), a publicly available pan-cancer database, to identify those predictive of overall survival. Finally, qRT-PCR was used to validate the most highly differentially dysregulated lncRNAs in another primary ccRCC cell type (RCC4) and in paired kidney tissue.RESULTS: A total of 360 lncRNA transcripts were differentially expressed four-fold or greater in 786-O-C162F cells relative to 786-O-VHL cells. Of these lncRNAs, 269 were upregulated and 91 were downregulated. Cross-referencing to TCGA, 52 of the upregulated lncRNAs and 23 of the downregulated lncRNAs were predictive of overall survival in ccRCC patients, as evidenced by statistically significant Cox regression and Log-rank p-values (p < 0.05). The top five up- and downregulated lncRNAs were validated with qRT-PCR.CONCLUSION: To our knowledge, this is one of the only analyses to systematically assess lncRNA expression in ccRCC, and the first to assess lncRNA expression in a VHL-dependent manner. We anticipate that these molecular and clinical findings will provide a framework for utilizing VHL inactivation status as a biomarker to stratify patients with ccRCC and associated molecular targets for further study. Future inter-institutional collaborations are needed to validate these findings in a large cohort of primary ccRCC samples.Citation Format: Joseph N. Samuel, Philip A. Marsden. Transcriptomic profiling of VHL-dependent long noncoding RNAs in clear cell renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1815.
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 | 0,001 |
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 ».