Abstract 1815: Transcriptomic profiling of VHL-dependent long noncoding RNAs in clear cell renal cell carcinoma
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".