Abstract A020: TLR10 as a key innate immune effector regulating metabolic and redox homeostasis in head and neck squamous cell carcinoma
Bibliographic record
Abstract
Abstract Head and Neck Squamous Cell Carcinoma (HNSCC) is a highly aggressive malignancy originating from the mucosal epithelium. While several Toll-like receptors (TLRs) are known to influence tumor behavior, the role of TLR10 in innate immune regulation within the tumor context remains poorly defined. In this study, I generated TLR10-deficient HNSCC cell lines using CRISPR/Cas9 to investigate its functional significance. Loss of TLR10 impaired cell viability and proliferation, suppressed cell cycle regulators (Cyclin A, Cyclin B, CDK2), and disrupted metabolic activity, as evidenced by decreased glycolysis (reduced ECAR, HK2, LDHA, PKM2) and lactate production. Additionally, TLR10-deficient cells showed elevated ROS levels and reduced antioxidant enzyme expression (SOD2, PRDX3, PRDX5, GPX4), suggesting disruption in redox homeostasis. These findings establish TLR10 as a novel innate immune effector that modulates cellular metabolism and redox balance in HNSCC, and may serve as a potential therapeutic target. Citation Format: Bokyung Joo, Hyeon Ji Kim, Hyo-Jin Song. TLR10 as a key innate immune effector regulating metabolic and redox homeostasis in head and neck squamous cell carcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr A020.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".