Role of Microornas in Tumroigenesis and their Modulation by Versican 3' Untranslated Region
Bibliographic record
Abstract
MicroRNA is a single-stranded RNA molecule of about 22 nucleotides in length and is expressed endogenously. It functions as a gene regulator by pairing imperfectly with 3’ untranslated region (3’UTR) of target mRNAs, leading to translational inhibition. MicroRNA is implicated in many regulatory pathways and hence affects various cellular activities. In the development of cancer, genetic alterations occurred at miRNA locus and its expression level is dysregulated in various cancers versus normal tissue counterparts. It is thus important to find the targets of dysregulated microRNAs contributing to progression of cancer. To facilitate long term functional studies, a microRNA expression construct with unique futures was generated. Stable expression of miR-378 enhanced cell survival, reduced caspase-3 activity, and promoted tumor growth and angiogenesis. By algorithmic predictions and proteomic analysis, two tumor suppressors, SuFu and Fus-1, were found to be translationally regulated by miR-378. Target validation was confirmed by co-transfection experiments and luciferase activity assays, reassuring its oncogenic role by regulating two tumor suppressor genes simultaneously. Conversely, microRNA can also function as a tumor suppressor by modulating expression of Versican, an extracellular matrix protein known to facilitate tumorigenesis and angiogenesis. By a novel PCR method, more than one microRNA were found to bind to Versican 3’UTR. iii Among these microRNAs, targeting of Versican and Fibronectin by miR199a-3p was validated. Expression of a fragment of Versican 3’UTR was expected to antagonize the function of miR-199a-3p. Stable expression of Versican 3’UTR resulted change in cell morphology and increased cell-cell adhesion. Analysis of primary tissues from transgenic mice expressing versican 3’UTR showed an increase expression of Versican and Fibronectin, and organ adhesion was found between liver and its surrounding tissues. In addition, 3’UTR also modulated the level of miR-199a-3p and miR-136, alleviating translation of negative cell cycle regulators, PTEN and Rb1. This resulted in reduced cell proliferation and hence diminished tumor growth. These findings suggest a role of microRNA in tumor growth, providing a valuable target for therapeutic intervention.
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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) | 0.001 | 0.000 |
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".