E2F1-mediated PKMYT1 upregulation promotes prostate cancer progression by inhibiting the PPAR signaling pathway
Bibliographic record
Abstract
Protein kinase membrane associated tyrosine/threonine 1 (PKMYT1) is a protein-coding gene associated with cell cycle regulation and cancer development, but its specific mechanism in prostate cancer (PCa) has not been clarified. This study sought to elucidate the role of PKMYT1 in PCa. Expression patterns, prognostic significance and potential mechanisms of PKMYT1 were explored by the TCGA database. Single-cell sequencing was performed using the GSE137829 dataset. Multi-database prediction identified potential transcription factors regulating PKMYT1 expression. PC3 cell line with PKMYT1 knockdown was established. Functional analyses (CCK-8, Wound healing, and Transwell assays) were performed to investigate the changes in tumor malignant behavior after PKMYT1 silencing. Western blot experiments were performed to analyze the effects of PKMYT1 on epithelial-mesenchymal transition (EMT) and PPAR signaling pathway. PKMYT1 was overexpressed in prostate cancer samples and its high expression was significantly associated with poor prognosis and Th2 cell infiltration. Knockdown of PKMYT1 could effectively inhibit the proliferation, migration, and EMT process of PCa cells. Mechanistically, E2F1 is an important factor regulating PKMYT1 expression, and PKMYT1 could inhibit the activity of the PPAR signaling pathway, thus ensuring the reinforcement of PCa progression. PKMYT1 can accelerate the progression of PCa by regulating the cell cycle, EMT process and PPAR signaling pathway. Targeting PKMYT1 provide a new perspective for the treatment of prostate cancer.
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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 | 0.001 |
| 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".