Tumour‐Associated Macrophages in Oral Squamous Cell Carcinoma
Bibliographic record
Abstract
OBJECTIVE: Tumour-associated macrophages (TAMs) are crucial in the progression and treatment response of oral squamous cell carcinoma (OSCC). TAMs infiltrate OSCC, adopting an M2-like phenotype that promotes tumour growth, metastasis and immune suppression. The current narrative review explored the roles of TAMs in OSCC, focusing on their impact on the tumour microenvironment, invasion, metastasis, angiogenesis, immunosuppression and potential therapeutic targeting. METHODS: A comprehensive analysis of the current literature on TAMs in OSCC was conducted. Specifically, we evaluated the biological functions of TAMs, their interactions within the tumour microenvironment, and their influence on disease progression and treatment outcomes. RESULTS: TAMs contribute to OSCC progression by secreting cytokines, such as IL-10 and TGF-β, that inhibit effector immune cells. They facilitate angiogenesis, extracellular matrix remodelling and the epithelial-mesenchymal transition, which are essential for tumour invasion and metastasis. TAMs support cancer stem cells and recruit regulatory T cells and myeloid-derived suppressor cells, enhancing resistance to therapies. Their presence correlates with advanced OSCC stages, lymph node metastasis and poor prognosis. CONCLUSION: TAMs regulate OSCC progression and therapy resistance. Reprogramming them to an M1-like phenotype or depleting them enhances treatments. Understanding TAM-OSCC interactions is crucial for developing interventions against their tumour-promoting functions and restoring anti-tumour immunity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".