ZNF545 loss promotes ribosome biogenesis and protein translation to initiate colorectal tumorigenesis in mice
Bibliographic record
Abstract
Abstract Ribosome biogenesis plays a pivotal role in tumorigenesis by supporting robust protein translation. We investigate the functional and molecular mechanism of Zinc finger protein 545 (ZNF545), a transcriptional repressor for ribosomal RNA (rRNA), in colorectal cancer (CRC). ZNF545 was silenced in CRC compared to adjacent normal tissues (P < 0.0001), implying a tumor-suppressive role. Colon-specificZnf545knockout in mice accelerated CRC inApcMin/+and azoxymethane/dextran sulfate sodium-induced CRC. Mechanistically, we demonstrated that ZNF545 uses its two zinc finger clusters to bind to minimal rDNA promoter, where it assembled transcriptional repressor complex by interacting with KAP1.Znf545deletion in mouse embryonic fibroblasts not only increased rRNA transcription rate and the nucleolar size and number but also altered the nucleolar composition and architecture with an increased number of fibrillar centers surrounded by net-like dense fibrillar components. Consequently,Znf545deletion promoted the gene expression of translation machinery, protein translation, and cell growth. Consistent with its tumor-suppressive role, ZNF545 overexpression in CRC cells induced growth arrest and apoptosis. Finally, administration of rRNA synthesis inhibitor, CX-5461, inhibited CRC development inZnf545Δ/ΔApcMin/+mice. In conclusion, ZNF545 suppresses CRC through repressing rRNA transcription and protein translation. Targeting rRNA biosynthesis in ZNF545-silenced tumors is a potential therapeutic strategy for CRC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".