KLF5 and NFYA factors as novel regulators of prostate cancer cell metabolism
Bibliographic record
Abstract
Prostate cancer (PCa) cells rely on the androgen receptor (AR) signaling axis to reprogram metabolism to sustain aberrant proliferation. Whether additional transcription factors participate to this reprogramming remains mostly unknown. To identify such factors, DNA motif analyses were performed in the promoter and regulatory regions of genes sensitive to androgens in PCa cells. These analyses identified two transcription factors, KLF5 and NFYA, as possibly associated with PCa cell metabolism. In clinical datasets, KLF5 and NFYA expression levels were associated with disease aggressiveness, being significantly decreased and increased, respectively, during PCa progression. Their expression was next investigated by qPCR and Western blot in human PCa cell models, revealing a positive regulation of KLF5 by androgens and a correlation between NFYA and AR protein expression status. siRNA-mediated knockdown of KLF5 increased human PCa cell proliferation rate in AR-positive cell models, suggesting a tumor suppressor function. Live-cell metabolic assays showed that knockdown of KLF5 promoted mitochondrial respiration, a key metabolic pathway associated with PCa progression. The opposite was observed for knockdown of NFYA regarding proliferation and respiration. RNA-seq analyses following the knockdown of either KLF5 and NFYA confirmed that both factors regulated distinct metabolic gene signatures, as well as other gene signatures, explaining their differential impact on PCa cell proliferation and metabolism. Overall, our findings identify KLF5 and NFYA as novel regulators of PCa cell metabolism.
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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) | 0.001 | 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".