Ribosomal RACK1 promotes chemoresistance and growth in human hepatocellular carcinoma
Bibliographic record
Abstract
Coordinated translation initiation is coupled with cell cycle progression and cell growth, whereas excessive ribosome biogenesis and translation initiation often lead to tumor transformation and survival. Hepatocellular carcinoma (HCC) is among the most common and aggressive cancers worldwide and generally displays inherently high resistance to chemotherapeutic drugs. We found that RACK1, the receptor for activated C-kinase 1, was highly expressed in normal liver and frequently upregulated in HCC. Aberrant expression of RACK1 contributed to in vitro chemoresistance as well as in vivo tumor growth of HCC. These effects depended on ribosome localization of RACK1. Ribosomal RACK1 coupled with PKCβII to promote the phosphorylation of eukaryotic initiation factor 4E (eIF4E), which led to preferential translation of the potent factors involved in growth and survival. Inhibition of PKCβII or depletion of eIF4E abolished RACK1-mediated chemotherapy resistance of HCC in vitro. Our results imply that RACK1 may function as an internal factor involved in the growth and survival of HCC and suggest that targeting RACK1 may be an efficacious strategy for HCC treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".