Oral inflammation promotes oral squamous cell carcinoma invasion
Bibliographic record
Abstract
// Cameron Goertzen 1 , Hayder Mahdi 1 , Catherine Laliberte 1, 3 , Tomer Meirson 2 , Denise Eymael 1 , Hava Gil-Henn 2 and Marco Magalhaes 1, 3, 4 1 Cancer Invasion and Metastasis Laboratory, Faculty of Dentistry, University of Toronto, Toronto, Canada 2 Cell Migration and Invasion Laboratory, Faculty of Medicine in the Galilee, Bar-Ilan University, Safed, Israel 3 Oral Pathology and Oral Medicine, Faculty of Dentistry, University of Toronto, Toronto, Canada 4 Sunnybrook Health Sciences Centre, Toronto, Canada Correspondence to: Marco Magalhaes, email: marco.magalhaes@utoronto.ca Keywords: cancer; metastasis; inflammation; neutrophils; TNF Received: November 07, 2017 Accepted: May 07, 2018 Published: June 26, 2018 ABSTRACT Oral squamous cell carcinoma (OSCC) represents 95% of oral malignancies and invasion, and metastasis underlies disease morbidity and mortality. We recently established a direct link between oral inflammation and cancer invasion by showing that neutrophils increase OSCC invasion through a tumor necrosis factor (TNFα)-dependent mechanism. The objective of this study was to characterize OSCC-associated inflammation and to determine the molecular mechanisms underlying inflammation-mediated OSCC invasion. Our results showed a significant increase in neutrophil infiltration, the neutrophil-to-lymphocyte ratio in the OSCC microenvironment and increased inflammatory markers, particularly TNFα in saliva. We performed next-generation sequencing of the TNFα-treated OSCC cells and showed marked overexpression of over 180 genes distributed among clusters related to neutrophil recruitment, invasion, and invadopodia. At the molecular level, TNFα treatment increased phosphoinositide 3-kinase (PI3K)-mediated invadopodia formation and matrix metalloproteinase (MMP)-dependent invasion. We show here that TNFα promotes a pro-inflammatory and pro-invasion phenotype leading to the recruitment and activation of inflammatory cells in a paracrine mechanism. Increased TNFα in the tumor microenvironment tips the balance towards invasion leading to decreased overall survival and disease-free survival. This represents a significant advancement of oral cancer research and will support new treatment approaches to control OSCC invasion and metastasis.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".