The paracrine effect of cancer‐associated fibroblast‐induced interleukin‐33 regulates the invasiveness of head and neck squamous cell carcinoma
Bibliographic record
Abstract
Head and neck squamous cell carcinoma (HNSCC) is one of the leading causes of cancer-related death worldwide. The prognosis of HNSCC is usually poor because of its propensity for extensive invasion, local recurrence and frequent regional lymph node metastasis, even at initial diagnosis. Carcinoma-associated fibroblasts (CAFs), a major type of tumour-surrounding stromal cell, generate mediators through which they interact with tumours and contribute to cancer progression. The orchestration between CAFs and cancer cells is complex. Despite recent studies demonstrating the paracrine effect of stromal cells in the tumour microenvironment on initiation and progression of cancer cells, the major mediator related to CAFs and its underlying mechanism remain unknown. In the present study, we used organotypic culture to investigate CAFs that promote aggressive behaviour of HNSCC cells. Using microarray analysis, we detected abundant expression of interleukin-33 (IL-33) in CAFs and identified IL-33 as a critical mediator in CAF-induced invasiveness. Counteracting IL-33 activity diminished the aggressive phenotype of cancer cells induced by CAFs. Administration of IL-33 promoted cancer cell migration and invasion through induction of epithelial-to-mesenchymal transdifferentiation and increased IL-33 gene expression in cancer cells. In 40 patients with HNSCC, IL-33 expression in CAFs correlated with IL-33 expression in cancer cells. Most cases with a low invasion pattern grading score (IPGS) showed low or no expression of IL-33, whereas most HNSCC cases with high IPGS displayed over-expression of IL-33 in CAFs and cancer cells. High IL-33 expression associated with poor prognosis in terms of nodal metastasis-free survival. These results indicate that CAFs promote cancer invasiveness via paracrine and autocrine effects on microenvironmental IL-33 signalling, and suggest that IL-33 is a potential prognostic biomarker that could be considered in therapeutic strategies for the treatment of patients with HNSCC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".