Plasma Gelsolin Confers Chemoresistance in Ovarian Cancer by Resetting the Relative Abundance and Function of Macrophage Subtypes
Bibliographic record
Abstract
Ovarian cancer (OVCA) is the most lethal gynaecological cancer with a 5-year survival rate less than 50%. Despite new therapeutic strategies, such as immune checkpoint blockers (ICBs), tumor recurrence and drug resistance remain key obstacles in achieving long-term therapeutic success. Therefore, there is an urgent need to understand the cellular mechanisms of immune dysregulation in chemoresistant OVCA in order to harness the host's immune system to improve survival. The over-expression of plasma gelsolin (pGSN) mRNA is associated with a poorer prognosis in OVCA patients; however, its immuno-modulatory role has not been elucidated. In this study, for the first time, we report pGSN as an inhibitor of M1 macrophage anti-tumor functions in OVCA chemoresistance. Increased epithelial pGSN expression was associated with the loss of chemoresponsiveness and poor survival. While patients with increased M1 macrophage infiltration exhibited better survival due to nitric-oxide-induced ROS accumulation in OVCA cells, cohorts with poor survival had a higher infiltration of M2 macrophages. Interestingly, increased epithelial pGSN expression was significantly associated with the reduced survival benefits of infiltrated M1 macrophages, through apoptosis via increased caspase-3 activation and reduced production of iNOS and TNFα. Additionally, epithelial pGSN expression was an independent prognostic marker in predicting progression-free survival. These findings support our hypothesis that pGSN is a modulator of inflammation and confers chemoresistance in OVCA, in part by resetting the relative abundance and function of macrophage subtypes in the ovarian tumor microenvironment. Our findings raise the possibility that pGSN may be a potential therapeutic target for immune-mediated chemoresistance in OVCA.
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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 | 0.001 |
| 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 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".