Autotaxin is an inflammatory mediator and therapeutic target in thyroid cancer
Bibliographic record
Abstract
Autotaxin is a secreted enzyme that converts extracellular lysophosphatidylcholine to lysophosphatidate (LPA). In cancers, LPA increases tumour growth, metastasis and chemoresistance by activating six G-protein coupled receptors. We examined >200 human thyroid biopsies. Autotaxin expression in metastatic deposits and primary carcinomas was four- to tenfold higher than in benign neoplasms or normal thyroid tissue. Autotaxin immunohistochemical staining was also increased in benign neoplasms with leukocytic infiltrations. Malignant tumours were distinguished from benign tumours by high tumour autotaxin, LPA levels and inflammatory mediators including IL1β, IL6, IL8, GMCSF, TNFα, CCL2, CXCL10 and platelet-derived growth factor (PDGF)-AA. We determined the mechanistic explanation for these results and revealed a vicious regulatory cycle in which LPA increased the secretion of 16 inflammatory modulators in papillary thyroid cancer cultures. Conversely, treating cancer cells with ten inflammatory cytokines and chemokines or PDGF-AA and PDGF-BB increased autotaxin secretion. We confirmed that this autotaxin/inflammatory cycle occurs in two SCID mouse models of papillary thyroid cancer by blocking LPA signalling using the autotaxin inhibitor ONO-8430506. This decreased the levels of 16 inflammatory mediators in the tumours and was accompanied by a 50-60% decrease in tumour volume. This resulted from a decreased mitotic index for the cancer cells and decreased levels of vascular endothelial growth factor and angiogenesis in the tumours. Our results demonstrate that the autotaxin/inflammatory cycle is a focal point for driving malignant thyroid tumour progression and possibly treatment resistance. Inhibiting autotaxin activity provides an effective and novel strategy for decreasing the inflammatory phenotype in thyroid carcinomas, which should complement other treatment modalities.
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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".