Abstract 3776: Identification of hypoxia regulated long noncoding RNA, MARVEL in kidney cancer
Bibliographic record
Abstract
Abstract Background: The kidney is a highly vascular organ, receiving about 1/4th of the total cardiac output. Not surprisingly, kidney cancer is a highly vascular malignancy. According to WHO, worldwide, there are over 400,000 new cases and over 170,000 deaths yearly due to kidney cancer. The aggressive subtype of kidney cancer, clear cell renal carcinoma (ccRCC), represents 80% of all cases. The Von Hippel Lindau (VHL) gene, a tumor suppressor, is functionally inactive in 90% of ccRCC, which activates the oxygen-sensing machinery responsible for inducing angiogenesis. Long noncoding RNAs (lncRNAs) are RNA molecules of 200 nucleotides and longer in size. LncRNAs can be highly tissue-specific, defining tumor growth and metastasis, hence potential biomarkers for clinical staging, disease progression, and unique therapeutic targets. We hypothesize that the functional inactivation of VHL induces robust changes in the lncRNA transcription during ccRCC tumorigenesis and vascularization. Methodology: To understand the pro-angiogenic RNA signals in ccRCC induced by functional VHL inactivation, human 786-O and RCC4 cell lines were used. Microarray analysis comparing the reconstituted functional and non-functional VHL in these tumor cells then defined mRNA and lncRNAs that are VHL- and HIF-dependent. These lncRNAs were further validated in ccRCC patients. 5’ RACE (Rapid Amplification of cDNA ends), RT-qPCR, and cellular compartmentalization were used to characterize the most abundant lncRNA in ccRCC. Results: Microarray-based transcriptomic ccRCC from VHL-deficient cell lines and patients divulged a novel lncRNA- “MARVEL” (Mortality Associated in RCC, VHL-deficient Expressed Long Noncoding RNA). Analysis in The Atlas of Noncoding RNAs in Cancer (TANRIC) database for ccRCC patients with 447 tumor cases and 67 normal kidney tissues revealed MARVEL predicts overall patient survival by log-rank regression analysis with Cox p < 0.005 and correlated with a higher stage of the disease by the decision tree analysis. MARVEL is VHL-, hypoxia-, and HIF- dependent. ReMap, ChIP-seq data on HIF1 and HIF2 confirmed binding within 1 Kb upstream of the start site of MARVEL. 5’ RACE characterized three transcriptional start sites and three spliced variants. The most abundant transcript of MARVEL has seven exons with six introns that follow the GT-AG rule of splicing. Further, CRISPRi and CRISPRa led loss- and gain-of-function studies on MARVEL are ongoing both in vitro and in vivo to characterize its role in tumorigenicity and angiogenesis. Significance: This study defines tumor property of hypoxia and a functional outcome of survival in patients to define the lncRNAs. We identified a novel lncRNA- MARVEL that can be a potential biomarker to predict advanced tumor stage and overall survival in renal cell carcinoma. The functional characterization of MARVEL can reveal its therapeutic potential in ccRCC. Citation Format: Ranju Nair, Joseph Samuel, Philip Marsden. Identification of hypoxia regulated long noncoding RNA, MARVEL in kidney cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3776.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".