The transcription factor FLI1 promotes cancer progression by affecting cell cycle regulation
Bibliographic record
Abstract
Binding of transcription factors to mutated DNA sequences is a likely regulator of cancer progression. Noncoding regulatory mutations such as those on the core promoter of the gene encoding human telomerase reverse transcriptase have been shown to affect gene expression in cancer. Using a protein microarray of 667 transcription factor DNA-binding domains and subsequent functional assays, we looked for transcription factors that preferentially bind the mutant hTERT promoter and characterized their downstream effects. One of them, friend leukemia integration 1 (FLI1), which belongs to the E26 transforming-specific family of transcription factors, exhibited particularly strong effects with respect to regulating hTERT expression, while the even better binding ELK3 did not. Depletion of FLI1 decreased expression of the genes for cyclin D1 (CCND1) and E2F transcription factor 2 (E2F2) resulting in a G1/S cell cycle arrest and in consequence a reduction of cell proliferation. FLI1 also affected CMTM7, another gene involved in G1/S transition, although by another process that suggests a balanced regulation of the tumor suppressor gene's activity via opposing regulation processes. FLI1 expression was found upregulated and correlated with an increase in CCND1 expression in pancreatic cancer and brain tumors. In non-neoplastic lung cells, however, FLI1 depletion led to rapid progression through the cell cycle. This coincides with the fact that FLI1 is downregulated in lung tumors. Taken together, our data indicate a cell cycle regulatory hub involving FLI1, hTERT, CCND1 and E2F2 in a tissue- and context-dependent manner.
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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 | 0.001 |
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".