The regulation of plasma gelsolin by DNA methylation in ovarian cancer chemo-resistance
Bibliographic record
Abstract
BACKGROUND: Ovarian cancer (OVCA) is the most lethal gynecologic cancer and chemoresistance remains a major hurdle to successful therapy and survival of OVCA patients. Plasma gelsolin (pGSN) is highly expressed in chemoresistant OVCA compared with their chemosensitive counterparts, although the mechanism underlying the differential expression is not known. Also, its overexpression significantly correlates with shortened survival of OVCA patients. In this study, we investigated the methylation role of Ten eleven translocation isoform-1 (TET1) in the regulation of differential pGSN expression and chemosensitivity in OVCA cells. METHODS: Chemosensitive and resistant OVCA cell lines of different histological subtypes were used in this study to measure pGSN and TET1 mRNA abundance (qPCR) as well as protein contents (Western blotting). To investigate the role of DNA methylation specifically in pGSN regulation and pGSN-induced chemoresistance, DNMTs and TETs were pharmacologically inhibited in sensitive and resistant OVCA cells using specific inhibitors. DNA methylation was quantified using EpiTYPER MassARRAY system. Gain-and-loss-of-function assays were used to investigate the relationship between TET1 and pGSN in OVCA chemoresponsiveness. RESULTS: We observed differential protein and mRNA expressions of pGSN and TET1 between sensitive and resistant OVCA cells and cisplatin reduced their expression in sensitive but not in resistant cells. We observed hypomethylation at pGSN promoter upstream region in resistant cells compared to sensitive cells. Pharmacological inhibition of DNMTs increased pGSN protein levels in sensitive OVCA cells and decreased their responsiveness to cisplatin, however we did not observe any difference in methylation level at pGSN promoter region. TETs inhibition resulted in hypermethylation at multiple CpG sites and decreased pGSN protein level in resistant OVCA cells which was also associated with enhanced response to cisplatin, findings that suggested the methylation role of TETs in the regulation of pGSN expression in OVCA cells. Further, we found that TET1 is inversely related to pGSN but positively related to chemoresponsiveness of OVCA cells. CONCLUSION: Our findings broaden our knowledge about the epigenetic regulation of pGSN in OVCA chemoresistance and reveal a novel potential target to re-sensitize resistant OVCA cells. This may provide a future therapeutic strategy to improve the overall OVCA patient survival.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".