Overexpression of miR-200s inhibits proliferation and invasion while increasing apoptosis in murine ovarian cancer cells
Bibliographic record
Abstract
Women diagnosed with ovarian cancer frequently have a poor prognosis as their cancer is often diagnosed at more advanced stages when the cancer has metastasized. At this point surgery cannot remove all the tumor cells and while ovarian cancer cells often initially respond to chemotherapeutic agents like carboplatin and paclitaxel, resistance to these agents frequently occurs. Thus, novel therapies are required for the treatment of advanced stage ovarian cancer. One therapeutic option being explored is the regulation of non-coding RNAs such as microRNAs. An advantage of microRNAs is that they can regulate tens, hundreds and sometimes thousands of mRNAs in cells and thus may be more effective than chemotherapeutic agents or targeted therapies. To investigate the therapeutic potential of miR-200s in ovarian cancer, lentiviral vectors were used to overexpress both miR-200 clusters in two murine ovarian cancer cell lines, ID8 and 28-2. Overexpression of miR-200s reduced the expression of several mesenchymal genes and proteins, significantly inhibited proliferation as assessed by BrdU flow cytometry and significantly reduced invasion through Matrigel coated transwell inserts in both cell lines. Overexpression of miR-200s also increased basal apoptosis approximately 3-fold in both cell lines as determined by annexin V flow cytometry. Pathway analysis of RNA sequencing of control and miR-200 overexpressing ovarian cancer cells revealed that genes regulated by miR-200s were involved in processes like epithelial mesenchymal transition (EMT) and cell migration. Therefore, miR-200s can inhibit proliferation and increase apoptosis while suppressing tumor cell invasion and thus simultaneously target three key cancer pathways.
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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".