Identification of genes and molecular pathways involved in the progression of premalignant oral epithelia
Bibliographic record
Abstract
An early interventional effort in oral premalignancy requires novel molecular targets and diagnostic biomarkers to delay or reverse incidences of malignant progression. Microarray-based transcriptional profiling in disease states provides global insight into the causal biomolecular processes and novel pathways involved. In this study, we investigated transcript profiles in precancerous oral lesions to identify nearly 1,700 genes as significantly overexpressed or underexpressed and a primarily affected metabolic pathway that may be responsible for irreversible transition to progressive stages of oral cancer. For the first time, we show a convergence of several genes and pathways known for their oncogenic capabilities, in progression of premalignant oral epithelial tissues. This study consequently provides a molecular basis for persistent proinflammatory conditions in oral premalignant tissues. We found that lipocalin-type prostaglandin D(2) synthase (PTGDS), a key enzyme in the arachidonic acid metabolism pathway, as repressed in premalignant stages. We show the protective role of these enzyme-derived metabolites in inhibiting cell proliferation using an in vitro oral cancer progression model. We have also confirmed the overexpression of two invasion-related biomarkers, psoriasin (PSOR1) and versican (CSPG2), in oral premalignant and malignant archival tissues. Our results clearly indicate that pharmacologic intervention with anti-inflammatory prostaglandin D(2)-like analogues may help prevent or delay oral epithelial carcinogenesis because of metabolic restoration of a negative feedback regulatory loop through its several cognate receptors or target molecules. Further studies directed toward a multitude of possible protective mechanisms of this lipocalin-type enzyme or its products in oral cancer progression are warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".