Environmentally relevant concentration PFNA promotes degradation of SMAD7 to drive progression of ovarian cancer via TGF-β/SMADs signaling pathway
Bibliographic record
Abstract
Perfluorononanoic acid (PFNA), an acknowledged environmental endocrine disruptor, is increasingly utilized as a substitute for perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA). Despite its growing use, limited research has been conducted to investigate its potential impact on tumorigenesis and progression, and the potential molecular mechanisms. Earlier studies linked perfluoroalkyl and polyfluoroalkyl substances (PFAS) exposure to breast and gynecological cancer progression in humans, lacking a clear understanding of the underlying mechanisms, notably in ovarian cancer. Our investigation into PFNA's effects at environmental concentrations (0.25-2 mM) showed no significant impact on cell proliferation but a notable increase in invasion and migration of ovarian cancer cells. This led to alterations in epithelial-mesenchymal transition (EMT) markers, including Claudin1, Vimentin, and Snail. Notably, PFNA exposure activated the TGF-β/SMADs signaling pathway. Crucially, SMAD7 degradation through the ubiquitin-proteasome system emerged as PFNA's pivotal molecular target for inducing EMT, corroborated in mouse models. In summary, this study presented evidence that environmentally relevant concentrations of PFNA could induce SMAD7 degradation via the proteasome pathway, subsequently activating the TGF-β/SMADs signaling pathway, and promoting EMT in ovarian cancer. These results illuminated the association between PFNA exposure and metastasis of 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) | 0.003 | 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".