Nuclear TIGAR mediates an epigenetic and metabolic autoregulatory loop via NRF2 in cancer therapeutic resistance
Bibliographic record
Abstract
Metabolic and epigenetic reprogramming play important roles in cancer therapeutic resistance. However, their interplays are poorly understood. We report here that elevated TIGAR (TP53-induced glycolysis and apoptosis regulator), an antioxidant and glucose metabolic regulator and a target of oncogenic histone methyltransferase NSD2 (nuclear receptor binding SET domain protein 2), is mainly localized in the nucleus of therapeutic resistant tumor cells where it stimulates NSD2 expression and elevates global H3K36me2 mark. Mechanistically, TIGAR directly interacts with the antioxidant master regulator NRF2 and facilitates chromatin recruitment of NRF2, H3K4me3 methylase MLL1 and elongating Pol-II to stimulate the expression of both new (NSD2) and established (NQO1/2, PRDX1 and GSTM4) targets of NRF2, independent of its enzymatic activity. Nuclear TIGAR confers cancer cell resistance to chemotherapy and hormonal therapy in vitro and in tumors through effective maintenance of redox homeostasis. In addition, nuclear accumulation of TIGAR is positively associated with NSD2 expression in clinical tumors and strongly correlated with poor survival. These findings define a nuclear TIGAR-mediated epigenetic autoregulatory loop in redox rebalance for tumor therapeutic resistance.
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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".