Forming cytoplasmic stress granules PURα suppresses mRNA translation initiation of IGFBP3 to promote esophageal squamous cell carcinoma progression
Bibliographic record
Abstract
Esophageal squamous cell carcinoma (ESCC) is one of the most fatal malignancies worldwide. Recently, our group identified purine-rich element binding protein alpha (PURα), a single-stranded DNA/RNA-binding protein, to be significantly associated with the progression of ESCC. Additional immunofluorescence staining demonstrated that PURα forms cytoplasmic stress granules to suppress mRNA translation initiation. The expression level of cytoplasmic PURα in ESCC tumor tissues was significantly higher than that in adjacent epithelia and correlated with a worse patient survival rate by immunohistochemistry. Functionally, PURα strongly preferred to bind to UG-/U-rich motifs and mRNA 3´UTR by CLIP-seq analysis. Moreover, PURα knockout significantly increased the protein level of insulin-like growth factor binding protein 3 (IGFBP3). In addition, it was further demonstrated that PURα-interacting proteins are remarkably associated with translation initiation factors and ribosome-related proteins and that PURα regulates protein expression by interacting with translation initiation factors, such as PABPC1, eIF3B and eIF3F, in an RNA-independent manner, while the interaction with ribosome-related proteins is significantly dependent on RNA. Specifically, PURα was shown to interact with the mRNA 3´UTR of IGFBP3 and inhibit its expression by suppressing mRNA translation initiation. Together, this study identifies cytoplasmic PURα as a modulator of IGFBP3, which could be a promising therapeutic target for ESCC treatment.
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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) | 0.002 | 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".