Abstract 3787: miR 2355-5p regulates tumor growth and angiogenesis in VHL-inactivated clear cell renal cell carcinoma
Bibliographic record
Abstract
Abstract Background: Clear Cell Renal Cell Carcinoma (ccRCC) constitutes more than 75% of kidney cancer diagnostics. Inactivation of the tumor suppressor gene termed von Hippel-Lindau (VHL) is considered the major truncal event and occurs in more than 85% of patients. The most notable role of VHL is its ability to negatively regulate the Hypoxia-Induced Factors (HIFα), which in turn, stimulate the transcription of multiple oncogenes, including microRNAs (miRNAs). MiRNAs are small non-coding RNAs that negatively regulate gene expression through complementary base pairing. Using ccRCC cell models, we observed an overexpression of the miR 2355-5p following the loss of VHL in a HIF2α dependent manner. Hypothesis: We hypothesize that the miR 2355-5p overexpression is involved in the tumorigenesis of VHL-inactivated ccRCC. Our specific aims are to i) quantify the expression of miR 2355-5p in ccRCC clinical samples, ii) characterize the functional impact of miR 2355-5p in ccRCC development, and iii) identify miR 2355-5p targeted genes in ccRCC tumors. Methods: Clinical samples (tissues and plasma) from consented patients with ccRCC were recruited through the Université de Sherbrooke. Normal adjacent tissue and plasma from healthy individuals were used as controls. miR 2355-5p expression was quantified through RT-qPCR with Taqman probes. Then, 786-0 and A498 VHL-deficient ccRCC cell lines were used. miR 2355-5p knockout models (KO) were generated using CRISPR/cas9 technology or stably overexpressed using miRNA precursor plasmid (786-0++2355). The impact on cell survival and proliferation was assessed with clonogenic assay and daily cell counts, respectively. Furthermore, cell models were injected into immunodeficient mice. Angiogenesis was evaluated by tube formation assay and angiogenesis proteome profiler kit. Finally, miRNA pulldown was performed to identify miR 2355-5p potential targets. Results: Our findings showed a significant increase in miR 2355-5p expression in tumor tissue compared to normal adjacent tissue as well as in plasma samples compared to healthy individuals. Inhibition of miR 2355-5p significantly reduced cell survival and cell proliferation in vitro and in vivo. Altered expression of the miRNA also disturbed the expression of multiple angiogenic factors and reduced the ability of cells to stimulate tube formation. MiRNA pulldown identified over 267 potential targets, where 161 were mRNA coding for functioning proteins. Close to 80% of identified proteins were direct targets of miR 2355-5p according to prediction software. Further analysis showed an inverse correlation between the tumor suppressor gene SPRY4 and miR 2355-5p, uncovering a potential link between the two. Discussion: Our results demonstrate an important role of miR 2355-5p on angiogenesis and ccRCC tumor growth. Further research on the interaction between miR-2355-5p, BTG2, SLIT2, and CMTM4 could unlock new potential avenues for targeted therapies. Citation Format: Patric Page, Sandra Turcotte, Mykella Martin, Patrick Richard, Tamiko Nishimura, Yasser Riazalhosseini. miR 2355-5p regulates tumor growth and angiogenesis in VHL-inactivated clear cell renal cell carcinoma. [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 3787.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".