PFDA promotes cancer metastasis through macrophage M2 polarization mediated by Wnt/β-catenin signaling
Bibliographic record
Abstract
Perfluoroundecanoic acid (PFDA) is extensively utilized in the textile and food processing industries and may have a tumor-promoting effect by modulating the tumor microenvironment. Macrophages play crucial roles in tumor microenvironment as key regulators of tumor immunity. However, further investigation is needed to elucidate how PFDA interacts with macrophages and contributes to tumor progression. In this study, we treated the macrophage cell line RAW264.7 with various concentrations of PFDA and found that RAW264.7 transitioned into an M2 tumor-promoting phenotype. Through bioinformatic analysis and subsequent verification of molecular assays, we uncovered that PFDA could activate β-catenin and enhance its nuclear translocation. Additionally, it was also observed that inhibiting β-catenin nuclear translocation partly attenuated RAW264.7 M2 polarization induced by PFDA. The conditioned medium derived from PFDA-pretreated RAW264.7 cells significantly promoted the migration and invasion abilities of human ovarian cancer cells. Furthermore, in vivo studies corroborated that PFDA-pretreated RAW264.7 could promote tumor metastasis, which could be mitigated by pretreatment with the β-catenin inhibitor ICG001. In conclusion, our study demonstrated that PFDA could promote cancer metastasis through regulating macrophage M2 polarization in a Wnt/β-catenin-dependent manner.
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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".