MiR-520d-5p directly targets TWIST1 and downregulates the metastamiR miR-10b
Bibliographic record
Abstract
// Pinchas Tsukerman 1 , Rachel Yamin 1 , Einat Seidel 1 , Saleh Khawaled 1 , Dominik Schmiedel 1 , Tomer Bar-Mag 1 , Ofer Mandelboim 1 1 The Lautenberg Center for General and Tumor Immunology, The Hebrew University, The BioMedical Research Institute, Israel Canada, Hadassah Medical School, Jerusalem, Israel Correspondence to: Ofer Mandelboim, e-mail: oferm@ekmd.huji.ac.il Keywords: TWIST1, miR-10b, mir-520d Received: August 14, 2014 Accepted: October 01, 2014 Published: November 07, 2014 ABSTRACT MicroRNAs are key players in most biological processes. Some microRNAs are involved in the genesis of tumors and are therefore termed oncomiRs, while others, termed metastamiRs, play a significant role in the formation of cancer metastases. Previously, we identified ten different cellular microRNAs that downregulate the expression of MICB, a ligand of the activating NK receptor NKG2D. Interestingly, several of the ten MICB-targeting microRNAs, such as miR-10b, are involved in tumor formation and metastasis. In this work, we identify a complex interplay between these different microRNAs. Specifically, we demonstrate that three of the MICB-targeting microRNAs: miR-20a, miR-17-5p and miR-93, also target the same site in the 3′UTR of TWIST1, a transcription factor implicated in cancer metastasis. Additionally, we show that miR-520d-5p targets a different site in the 3′UTR of TWIST1. We next show that the miR-520d-5p-mediated decrease of TWIST1 expression results in reduced expression of one of its targets, miR-10b, and in the restoration of E-Cadherin expression, which in turn results in reduced cellular motility and invasiveness. Finally, we show that miR-520d-5p leads to reduced proliferation of tumor cells, and that high levels of miR-520d-5p correlate with higher survival rates of cancer patients.
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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 | 0.001 |
| 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".