Inverse Regulation of <scp>TLR4</scp> and <scp>PD</scp> ‐ <scp>L1</scp> Shapes the Inflammatory Tumor Microenvironment in Oral Squamous Cell Carcinomas
Bibliographic record
Abstract
BACKGROUND: The interactions between malignant cells and immune cells within the tumor microenvironment (TME) significantly influence cancer development and progression. This study aimed to analyze and correlate the expression of TLR4 and PD-L1 with the immune response, clinical characteristics, and prognosis of oral squamous cell carcinomas (OSCC). METHODS: Our retrospective multicentric study consisted of the assessment of 166 OSCC specimens for TLR4, PD-L1, CD8, and Ki-67 expression in a TMA-based immunohistochemistry analysis. RESULTS: Our findings indicated an inverse correlation between the expression of PD-L1 and TLR4 (r = -0.348, p = 0.014, and r = -0.269, p = 0.049, superficial tumor site and in overall analysis, respectively). On the other hand, PD-L1 expression in the deep and superficial invasive front positively correlated with CD8+ T tumor infiltrating lymphocytes (TIL) in a statistically significant manner. A logistic regression analysis was performed to assess the impact of each variable on the clinical outcome with at least 5-year follow-up after the initial OSCC diagnosis. The multivariate model revealed that advanced T stage (T3-T4), presence of lymph node metastasis (N+), as well as performing chemotherapy were statistically significantly associated with OSCC mortality. CONCLUSION: These findings taken together suggest that there is a differential regulation of the immune response coordinated by activation of PD-L1 or TLR4 affecting T cell response.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".