Pro-tumorigenic Effects of miR-31 Loss in Mesothelioma
Bibliographic record
Abstract
The human genome encodes several hundred microRNA (miRNA) genes that produce small (21-23n) single strand regulatory RNA molecules. Although abnormal expression of miRNAs has been linked to cancer progression, the mechanisms of this dysregulation are poorly understood. Malignant mesothelioma (MM) of pleura is an aggressive and highly lethal cancer resistant to conventional therapies. We and others previously linked loss of the 9p21.3 chromosome in MM with short time to tumor recurrence. In this study, we report that MM cell lines derived from patients with more aggressive disease fail to express miR-31, a microRNA recently linked with suppression of breast cancer metastases. We further demonstrate that this loss is due to homozygous deletion of the miR-31-encoding gene that resides in 9p21.3. Functional assessment of miR-31 activity revealed its ability to inhibit proliferation, migration, invasion, and clonogenicity of MM cells. Re-introduction of miR-31 suppressed the cell cycle and inhibited expression of multiple factors involved in cooperative maintenance of DNA replication and cell cycle progression, including pro-survival phosphatase PPP6C, which was previously associated with chemotherapy and radiation therapy resistance, and maintenance of chromosomal stability. PPP6C, whose mRNA is distinguished with three miR-31-binding sites in its 3'-untranslated region, was consistently down-regulated by miR-31 introduction and up-regulated in clinical MM specimens as compared with matched normal tissues. Taken together, our data suggest that tumor-suppressive propensity of miR-31 can be used for development of new therapies against mesothelioma and other cancers that show loss of the 9p21.3 chromosome.
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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) | 0.001 | 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".