The molecular mechanisms and targeting strategies of transcription factors in cholangiocarcinoma
Bibliographic record
Abstract
INTRODUCTION: Cholangiocarcinoma consists of a cluster of malignant biliary tumors that tend to have a poor prognosis, ranking as the second most prevalent type of liver cancer, and their incidence rate has increased globally recently. The high-frequency driving mutations of cholangiocarcinoma, such as KRAS/IDH1/ARID1A/P53, imply the epigenetic instability of cholangiocarcinoma, leading to the dysregulation of various related transcription factors, thus affecting the occurrence and development of cholangiocarcinoma. Increasingly evidence indicates that the high heterogeneity and malignancy of cholangiocarcinoma are closely related to the dysregulation of transcription factors which promote cell proliferation, invasion, migration, angiogenesis, and drug resistance through reprogrammed transcriptional networks. It is of great significance to further explore and summarize the role of transcription factors in cholangiocarcinoma. AREAS COVERED: This review summarizes the oncogenic or tumor suppressive roles of key transcription factors in regulating cholangiocarcinoma progression and the potential targeting strategies of transcription factors in cholangiocarcinoma. EXPERT OPINION: Cholangiocarcinoma is a type of cancer highly influenced by transcriptional regulation, specifically transcription factors and epigenetic regulatory factors. Targeting transcription factors could be a potential and important strategy that is likely to impact future cholangiocarcinoma treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".