Roles of KLF6 and KLF6-SV1 in Ovarian Cancer Progression and Intraperitoneal Dissemination
Bibliographic record
Abstract
PURPOSE: We investigated the role of the KLF6 tumor suppressor gene and its alternatively spliced isoform KLF6-SV1 in epithelial ovarian cancer (EOC). EXPERIMENTAL DESIGN: We first analyzed tumors from 68 females with EOC for KLF6 gene inactivation using fluorescent loss of heterozygosity (LOH) analysis and direct DNA sequencing and then defined changes in KLF6 and KLF6-SV1 expression levels by quantitative real-time PCR. We then directly tested the effect of KLF6 and KLF6-SV1 inhibition in SKOV-3 stable cell lines on cellular invasion and proliferation in culture and tumor growth, i.p. dissemination, ascites production, and angiogenesis in vivo using BALB/c nu/nu mice. All statistical tests were two sided. RESULTS: LOH was present in 59% of samples in a cell type-specific manner, highest in serous (72%) and endometrioid (75%) subtypes, but absent in clear cell tumors. LOH was significantly correlated with tumor stage and grade. In addition, KLF6 expression was decreased in tumors when compared with ovarian surface epithelial cells. In contrast, KLF6-SV1 expression was increased approximately 5-fold and was associated with increased tumor grade regardless of LOH status. Consistent with these findings, KLF6 silencing increased cellular and tumor growth, angiogenesis, and vascular endothelial growth factor expression, i.p. dissemination, and ascites production. Conversely, KLF6-SV1 down-regulation decreased cell proliferation and invasion and completely suppressed in vivo tumor formation. CONCLUSION: Our results show that KLF6 and KLF6-SV1 are associated with key clinical features of EOC and suggest that their therapeutic targeting may alter ovarian cancer growth, progression, and dissemination.
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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".