HDAC8 Enhances the Function of HIF‐2α by Deacetylating ETS1 to Decrease the Sensitivity of TKIs in ccRCC
Bibliographic record
Abstract
Drug resistance after long-term use of Tyrosine kinase inhibitors (TKIs) has become an obstacle for prolonging the survival time of patients with clear cell renal cell carcinoma (ccRCC). Here, genome-wide CRISPR-based screening to reveal that HDAC8 is involved in decreasing the sensitivity of ccRCC cells to sunitinib is applied. Mechanically, HDAC8 deacetylated ETS1 at the K245 site to promote the interaction between ETS1 and HIF-2α and enhance the transcriptional activity of the ETS1/HIF-2α complex. However, the antitumor effect of inhibiting HDAC8 on sensitized TKI is not very satisfactory. Subsequently, inhibition of HDAC8 increased the expression of NEK1, and up-regulated NEK1 phosphorylated ETS1 at the T241 site to promote the interaction between ETS1 and HIF-2α by impeded acetylation at ETS1-K245 site is showed. Moreover, TKI treatment increased the expression of HDAC8 by inhibiting STAT3 phosphorylation in ccRCC cells is also found. These 2 findings highlight a potential mechanism of acquired resistance to TKIs and HDAC8 inhibitors in ccRCC. Finally, HDAC8-in-PROTACs to optimize the effects of HDAC8 inhibitors through degrading HDAC8 and overcoming the resistance of ccRCC to TKIs are synthesized. Collectively, the results revealed HDAC8 as a potential therapeutic candidate for resistance to ccRCC-targeted therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".