Immune Microenvironment in Oral Potentially Malignant Disorders and Oral Cancer: A Narrative Review
Bibliographic record
Abstract
Multiple studies have investigated the impact of the tumor immune microenvironment (TIME) on oral squamous cell carcinoma (OSCC), with most focusing on three key cellular components: lymphocytes, macrophages, and neutrophils, as well as the molecular mechanisms underlying inflammation-mediated OSCC invasion. Although the specific roles of each cell type vary depending on their subtypes and the characteristics of OSCC, several consistent patterns have been identified. TIME plays a critical role at every stage of OSCC progression, from tumor initiation and growth to invasion and metastasis. Understanding the communication signals-the language-between tumor cells and the TIME, encoded through various proteins secreted by immune cells, is essential for controlling tumor progression and developing effective treatments for OSCC. This review provides an overview of how TIME influences the progression of the Oral Potentially Malignant Disorders (OPMDs) to OSCC as well as OSCC's invasion, focusing on the contributions of various immune cells within the TIME. Additionally, we discuss recent advances in immunotherapy for OSCC, highlighting strategies to enhance immune responses and improve treatment outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".