Three dysregulated miRNAs control kallikrein 10 expression and cell proliferation in ovarian cancer
Bibliographic record
Abstract
BACKGROUND: Kallikrein-related peptidases (KLKs) are a family of serine proteases that have been shown to be dysregulated in several malignancies including ovarian cancer. The control of kallikrein genes and their physiological function in cancer is not well understood. We hypothesized that microRNAs (miRNAs) represent a novel mechanism for post-transcriptional control of KLK expression in cancer. METHODS: We first analysed miRNA expression in ovarian cancer in silico. A total of 98 miRNAs were reported to have altered expression in ovarian cancer. Three of these miRNAs were predicted to target KLK10. We experimentally verified the predicted miR-KLK10 interaction using two independent techniques, a luciferase assay with a construct containing the KLK10 3' untranslated region (UTR), pMIR-KLK10, and measuring KLK10 protein levels after transfection with miRNA. RESULTS: When we co-transfected cells with pMIR-KLK10 and either let-7f, miR-224, or mR-516a, we saw decreased luciferase signal, suggesting that these miRNAs can target KLK10. We then examined the effect of these three miRNAs on KLK10 protein expression and cell growth. Transfection of all miRNAs, let-7f, miR-224, and miR-516a led to a decrease in protein expression and cellular growth. This effect was shown to be dose dependent. The KLK10 protein levels were partially restored by co-transfecting let-7f and its inhibitor. In addition, there was a slight decrease in KLK10 mRNA expression after transfection with let-7f. CONCLUSION: Our results confirm that KLKs can be targeted by more than one miRNA. Increased expression of certain miRNAs in ovarian cancer can lead to decreased KLK protein expression and subsequently have a negative effect on cell proliferation. This dose-dependent effect suggests that a 'tweaking' or 'fine-tuning' mechanism exists in which the expression of one KLK can be controlled by multiple miRNAs. These data together suggest that miRNA may be used as potential therapeutic options and further studies are required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".