Lipid metabolites as biomarkers and therapeutic targets in oral squamous cell carcinoma
Bibliographic record
Abstract
This study explores the association of lipid metabolism disruption and Oral Squamous Cell Carcinoma (OSCC). We aim to identify specific lipid biomarkers and therapeutic targets for OSCC. We included 78 OSCC patients and 80 healthy controls, and applied non-target lipidomics and transcriptomics for comprehensive analysis. Using ultra-high-performance liquid chromatography quadrupole-Orbitrap high-resolution accurate mass spectrometry (UHPLC/Q-Orbitrap HRMS) coupled with machine learning for diagnostic modeling, we identified potential lipid biomarkers. Transcriptomic analysis helped in pinpointing genetic and metabolic targets relevant to lipid metabolism in OSCC. Notably, we observed 70 differential lipid metabolites in the OSCC group, with nine achieving an AUC > 0.95, suggesting high potential as biomarkers. A diagnostic model based on 10 differentiated lipids yielded accuracy rates of 98.2% in a training cohort and 95.7% in a validation cohort. Additionally, the overexpression of DGKG, linked to poor prognosis, was noted to enhance migration and invasion of OSCC cells, marking it a potential target for therapy. This research underscores the critical role of lipid metabolic alterations in OSCC and highlights innovative diagnostic and therapeutic avenues.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".