Cancer-associated fibroblasts as a potential therapeutic target for thyroid cancers
Bibliographic record
Abstract
Thyroid cancer, a prevalent endocrine malignancy, is influenced by its tumor microenvironment (TME), with cancer-associated fibroblasts (CAFs) playing a pivotal role in disease progression. Molecularly, CAFs orchestrate a pro-tumorigenic niche via cytokine secretion and extracellular matrix (ECM) stiffening, underscoring their targetability. Therapeutic strategies, including small molecule inhibitor-based therapies, immune-based therapies, nanoparticle-based approaches, and combination regimens, have been evaluated for their efficacy in disrupting CAF functionality. CAFs from resident fibroblasts or recruited precursors can promote the progression of thyroid cancer through ECM remodeling, angiogenesis, and epithelial-mesenchymal transition (EMT) induction while facilitating immune evasion. These processes can enhance tumor invasiveness, metastasis, and resistance to conventional therapies. Preclinical studies using thyroid cancer models have demonstrated promising outcomes, such as reduced tumor burden and enhanced drug sensitivity upon CAF inhibition. Emerging clinical trials have tested CAF-directed agents in patient cohorts and validated these findings. However, many challenges persist, including the identification of reliable CAF-specific biomarkers, optimization of treatment timing, and integration of the biomarkers into personalized medicine frameworks. This review explores the therapeutic potential of CAFs for thyroid cancers, emphasizing their origin, activation, and multifaceted contributions to tumor growth. This review synthesizes current evidence, highlighting CAFs as a novel therapeutic frontier for thyroid cancers. Future research should focus on refined biomarker discovery and strategic therapeutic sequencing to maximize clinical benefits, providing a roadmap for translating CAF-targeted approaches into effective treatments for thyroid cancers.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".