Targeting cellular metabolism to reduce head and neck cancer growth
Bibliographic record
Abstract
Head and neck squamous cell carcinoma (HNSCC) presents a major public health concern because of delayed diagnosis and poor prognosis. Malignant cells often reprogram their metabolism in order to promote their survival and proliferation. Aberrant glutaminase 1 (GLS1) expression enables malignant cells to undergo increased glutaminolysis and utilization of glutamine as an alternative nutrient. In this study, we found a significantly elevated GLS1 expression in HNSCC, and patients with high expression levels of GLS1 experienced shorter disease-free periods after therapy. We hypothesized that the GLS1 selective inhibitor, bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl)ethyl sulfide (BPTES), which curtails cells' glutamine consumption, may inhibit HNSCC cell growth. Our results support the idea that BPTES inhibits HNSCC growth by inducing apoptosis and cell cycle arrest. Considering that metformin can reduce glucose consumption, we speculated that metformin would enhance the anti-neoplasia effect of BPTES by suppressing malignant cells' glucose utilization. The combination of both compounds exhibited an additive inhibitory effect on cancer cell survival and proliferation. All of our data suggest that GLS1 is a promising therapeutic target for HNSCC treatment. Combining BPTES with metformin might achieve improved anti-cancer effects in HNSSC, which sheds light on using novel therapeutic strategies by dually targeting cellular metabolism.
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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) | 0.001 | 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".