GRO-α and IL-8 enhance ovarian cancer metastatic potential via the CXCR2-mediated TAK1/NFκB signaling cascade
Bibliographic record
Abstract
Intraperitoneal metastasis is a common occurrence and is usually involved in the poor prognosis of ovarian cancer. Its specific metastatic pattern implies that certain indispensable microenvironmental factors secreted in the peritoneal cavity can direct metastatic ovarian cancer cells to permissive niches for secondary lesion formation. However, the underlying molecular mechanisms are ill defined. Herein, we report that GRO- and IL-8 are predominately upregulated in culture media derived from either normal or cancerous omenta and are associated with increased ovarian cancer aggressiveness. Methods: OCM was established from culture medium of fresh human omental tissues. Primary and metastatic ovarian cancer cell lines were generated from human tumor tissues and verified by specific antibodies. The functional roles of GRO-, IL-8, and their specific receptor CXCR2 were examined by neutralizing antibodies, shRNA gene knockdown, CRISPR/Cas9 gene knockout and pharmaceutical CXCR2 inhibitor SB225002. The oncogenic properties of ovarian cancer cells were examined by in vitro and in vivo mouse models. Results: Both GRO- and IL-8 can activate TAK1/NFB signaling via the CXCR2 receptor. Intriguingly, TAK1/NFB signaling activity was higher in metastatic ovarian cancer cells; this higher activity makes them more susceptible to OCM-induced tumor aggressiveness. Treatment of ovarian cancer cells with GRO- and IL-8 neutralizing antibodies or ablation of CXCR2 by shRNA gene knockdown, CRISPR/Cas9 gene knockout, or CXCR2 inhibitor SB225002 treatment significantly attenuated TAK1/NFB signaling and decreased in vitro and in vivo oncogenic and metastatic potential, suggesting CXCR2 plays a key role in the GRO- and IL-8-governed metastatic spreading of ovarian cancer cells in the intraperitoneal cavity.
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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) | 0.002 | 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".