FLI1 promotes protein translation via the transcriptional regulation of MKNK1 expression
Bibliographic record
Abstract
The disruption of protein translation machinery is a common feature of cancer initiation and progression, and drugs that target protein translation offer new avenues for therapy. The translation initiation factor, eukaryotic initiation factor 4E (eIF4E), is induced in a number of cancer cell lines and is one such candidate for therapeutic intervention. Friend leukemia integration 1 (FLI1) is a potent oncogenic transcription factor that promotes various types of cancer by promoting several hallmarks of cancer progression. FLI1 has recently been implicated in protein translation through yet unknown mechanisms. This study identified a positive association between FLI1 expression and mitogen‑activated protein kinase (MAPK)‑interacting serine/threonine kinase1 (MKNK1), the immediate upstream regulator of the eIF4E initiation factor. The short hairpin RNA (shRNA)‑mediated silencing or overexpression of FLI1 in leukemic cell lines downregulated or upregulated MKNK1 expression, respectively. Promoter analysis identified a potent FLI1 binding site in the regulatory region of the MKNK1 promoter. In transient transfection experiments, FLI1 increased MKNK1 promoter activity, which was blocked by mutating the FLI1 binding site. FLI1 specifically affected the expression of MKNK1, but not that of MKNK2. The siRNA‑mediated downregulation of MKNK1 downregulated the expression of survivin (BIRC5) and significantly suppressed cell proliferation in culture. FLI1 inhibitory compounds were shown to downregulate this oncogene through the suppression of MAPK/extracellular‑regulated kinase (ERK) signaling and the subsequent activation of miR‑145, leading to a lower MKNK1 expression and the suppression of leukemic growth. These results uncover a critical role for FLI1 in the control of protein translation and the importance of targeting its function and downstream mediators, such as MKNK1, for cancer therapy.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".