Loss of HSulf-1: The Missing Link between Autophagy and Lipid Droplets in Ovarian Cancer
Bibliographic record
Abstract
Abstract Defective autophagy and deranged metabolic pathways are common in cancer; pharmacologic targeting of these two pathways could provide a viable therapeutic option. However, how these pathways are regulated by limited availability of growth factors is still unknown. Our study shows that HSulf-1 (endosulfatase), a known tumor suppressor which attenuates heparin sulfate binding growth factor signaling, also regulates interplay between autophagy and lipogenesis. Silencing of HSulf-1 in OV202 and TOV2223 cells (ovarian cancer cell lines) resulted in increased lipid droplets (LDs), reduced autophagic vacuoles (AVs) and less LC3B puncta. In contrast, HSulf-1 proficient cells exhibit more AVs and reduced LDs. Increased LDs in HSulf-1 depleted cells was associated with increased ERK mediated cPLA2S505 phosphorylation. Conversely, HSulf-1 expression in SKOV3 cells reduced the number of LDs and increased the number of AVs compared to vector controls. Furthermore, pharmacological (AACOCF3) and ShRNA mediated downregulation of cPLA2 resulted in reduced LDs, and increased autophagy. Finally, in vivo experiment using OV202 Sh1 derived xenograft show that AACOCF3 treatment effectively attenuated tumor growth and LD biogenesis. Collectively, these results show a reciprocal regulation of autophagy and lipid biogenesis by HSulf-1 in ovarian cancer.
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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".