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 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) | 0.000 | 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".