Potential role of TIGAR in OSCC: tumorigenesis and survival
Bibliographic record
Abstract
Despite the improvement of treatment modalities, OSCC remains a prevalent disease in India and 50% of OSCC patients die within 5 years of disease detection. One of the major reasons for treatment failure is imbalances of metabolic profile in cancer. TIGAR (TP53-induced glycolysis and apoptosis regulator), is a p53-inducible protein that functions as fructose-2, 6bisphosphatase and fructose-1, 6-bisphosphatase, reducing the glycolytic rate and promoting the Pentose Phosphate Pathway. Consistent with increased activation of the PPP, cells expressing TIGAR have higher NADPH levels and a concomitantly enhanced ability to regulate levels of cellular ROS and thus reduce oxidative stress. Apart from the metabolic function of TIGAR, it has another role in the survival of cancer. Recently found that TIGAR is upregulated in some cancer, the main focus of our proposed study is to find out the potential role of TIGAR in tumorigenesis and survival of OSCC. For this we are using OSCC cell line as well as primary OSCC sample, some our recent finding suggesting that TIGAR is upregulated in OSCC cell line as well as patient sample compared their respective control, and also found that TIGAR knockdown decreases cell viability and drug sensitivity. Our main focus is how TIGAR regulated OSCC cell survival and what mechanism behind this, for this we will use RNA-Seq in TIGAR KD cell and TIGAR OE cell and found that how many pathway or which gene is affecting when targeting TIGAR. So overall our findings prove that TIGAR has some role in the survival of OSCC.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".