E2F7 promotes mammalian target of rapamycin inhibitor resistance in hepatocellular carcinoma after liver transplantation
Bibliographic record
Abstract
The mammalian target of rapamycin (mTOR) pathway is frequently deregulated and has critical roles in cancer progression. mTOR inhibitor has been widely used in several kinds of cancers and is strongly recommended in patients with hepatocellular carcinoma (HCC) after liver transplantation (LT). However, the poor response to mTOR inhibitors due to resistance remains a challenge. Hypoxia-associated resistance limits the therapeutic efficacy of targeted drugs. The present study established models of HCC clinical samples and cell lines resistance to mTOR inhibitor sirolimus and screened out E2F7 as a candidate gene induced by hypoxia and promoting sirolimus resistance. E2F7 suppressed mTOR complex 1 via directly binding to the promoter of the TSC1 gene and stabilizes hypoxia-inducible factor-1α activating its downstream genes, which are responsible for E2F7-dependent mTOR inhibitor resistance. Clinically, low E2F7 expression could be an effective biomarker for recommending patients with HCC for anti-mTOR-based therapies after LT. Targeting E2F7 synergistically inhibited HCC growth with sirolimus in vivo. E2F7 is a promising target to reverse mTOR inhibition resistance. Collectively, our study points to a role for E2F7 in promoting mTOR inhibitor resistance in HCC and emphasizes its potential clinical significance in patients with HCC after LT.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".