Deciphering the Transcription Factor Landscape in Prostate Cancer Progression: A Novel Approach to Understand NE Transdifferentiation
Bibliographic record
Abstract
Prostate cancer (PCa) stands as a leading cause of cancer-related mortality among men, with treatment-induced neuroendocrine prostate cancer (NEPC) posing a challenge as an ARPI-resistant subtype. The role of transcription factors (TFs) in PCa progression and NEPC transdifferentiation remains inadequately understood, underscoring a critical gap in current research. In this study, an internal Z score-based approach is developed to identify lineage-specific TF profiles in prostatic adenocarcinoma and NEPC for a nuanced understanding of TF expression dynamics. Distinct TF profiles for adenocarcinoma and NEPC are unveiled, identifying 126 shared TFs, 46 adenocarcinoma-TFs, and 56 NEPC-TFs, validated across multiple cohorts. Gene Ontology is employed to validate their biological and functional roles in PCa progression. Implications are revealed in cell development, differentiation, and lineage determination. Knockdown experiments suggest that lineage-TFs are functionally important in maintaining lineage-specific cell proliferation. Additionally, a longitudinal study on NE transdifferentiation highlights dynamic TF expression shifts, proposing a three-phases hypothesis for PCa progression mechanisms. This study introduces a groundbreaking approach for deciphering the TF landscape in PCa, providing a molecular basis for adenocarcinoma to NEPC progression, and paving the way for innovative treatment strategies with potential impact on patient outcomes.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".