Loss of LKB1-NUAK1 signalling enhances NF-κB activity in a spheroid model of high-grade serous ovarian cancer
Bibliographic record
Abstract
High-grade serous ovarian cancer (HGSOC) is an aggressive malignancy often diagnosed at an advanced stage. Although most HGSOC patients respond initially to debulking surgery combined with cytotoxic chemotherapy, many ultimately relapse with platinum-resistant disease. Thus, improving outcomes requires new ways of limiting metastasis and eradicating residual disease. We identified previously that Liver kinase B1 (LKB1) and its substrate NUAK1 are implicated in EOC spheroid cell viability and are required for efficient metastasis in orthotopic mouse models. Here, we sought to identify additional signalling pathways altered in EOC cells due to LKB1 or NUAK1 loss-of-function. Transcriptome analysis revealed that inflammatory signalling mediated by NF-κB transcription factors is hyperactive due to LKB1-NUAK1 loss in HGSOC cells and spheroids. Upregulated NF-κB signalling due to NUAK1 loss suppresses reactive oxygen species (ROS) production and sustains cell survival in spheroids. NF-κB signalling is also activated in HGSOC precursor fallopian tube secretory epithelial cell spheroids, and is further enhanced by NUAK1 loss. Finally, immunohistochemical analysis of OVCAR8 xenograft tumors lacking NUAK1 displayed increased RelB expression and nuclear staining. Our results support the idea that NUAK1 and NF-κB signalling pathways together regulate ROS and inflammatory signalling, supporting cell survival during each step of HGSOC pathogenesis. We propose that their combined inhibition may be efficacious as a novel therapeutic strategy for advanced HGSOC.
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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 | 0.001 |
| 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".