Regulation of HSulf-1 Expression by Variant Hepatic Nuclear Factor 1 in Ovarian Cancer
Bibliographic record
Abstract
We recently identified HSulf-1 as a down-regulated gene in ovarian carcinomas. Our previous analysis indicated that HSulf-1 inactivation in ovarian cancers is partly mediated by loss of heterozygosity and epigenetic silencing. Here, we show that variant hepatic nuclear factor 1 (vHNF1), encoded by transcription factor 2 gene (TCF2, HNF1beta), negatively regulates HSulf-1 expression in ovarian cancer. Immunoblot assay revealed that vHNF1 is highly expressed in HSulf-1-deficient OV207, SKOV3, and TOV-21G cell lines but not in HSulf-1-expressing OSE, OV167, and OV202 cells. By short hairpin RNA-mediated down-regulation of vHNF1 in TOV-21G cells and transient enhanced vHNF1 expression in OV202 cells, we showed that vHNF1 suppresses HSulf-1 expression in ovarian cancer cell lines. Reporter assay and chromatin immunoprecipitation experiments showed that vHNF1 is specifically recruited to HSulf-1 promoter at two different vHNF1-responsive elements in OV207 and TOV-21G cells. Additionally, down-regulation of vHNF1 expression in OV207 and TOV-21G cells increased cisplatin- or paclitaxel-mediated cytotoxicity as determined by both 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide and clonogenic assays and this effect was reversed by down-regulation of HSulf-1. Moreover, nude mice bearing TOV-21G cell xenografts with stably down-regulated vHNF1 were more sensitive to cisplatin- or paclitaxel-induced cytotoxicity compared with xenografts of TOV-21G clonal lines with nontargeted control short hairpin RNA. Finally, immunohistochemical analysis of 501 ovarian tumors including 140 clear-cell tumors on tissue microarrays showed that vHNF1 inversely correlates to HSulf-1 expression. Collectively, these results indicate that vHNF1 acts as a repressor of HSulf-1 expression and might be a molecular target for ovarian cancer therapy.
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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".