Functional mapping of tissue-specific elements of the human α-fetoprotein gene enhancer
Bibliographic record
Abstract
Serum alpha-fetoprotein (AFP) levels in hepatocellular carcinoma (HCC) patients and expression of the protein in cultured HCC cell lines are highly variable. These observations may arise from features correlated with tissue-specific expression of the gene. Extremely strong and potent liver-specific enhancer activity is confined from -4.1 to -3.3 kb upstream to the human AFP gene in contrast with that of the rodent which exists in three widely separated regions. To understand the tissue-specific expression of AFP, we examined cis-acting elements in the enhancer. Results revealed binding sites for selected liver-enriched transcription factors (LETFs) in both domains A (-4120 to -3756 bp) and B (-3492 to -3300 bp) of the gene. These sites included: one hepatocyte nuclear factor (HNF)-1 and HNF-4, two HNF-3, and two C/EBP binding sites in domain A. An adjacent domain B contained one HNF-3 site and three C/EBP sites plus a previously identified HNF-1 site. Each of these elements alone has the ability to stimulate heterogeneous promoter activity in a dose-dependent manner when transfected into AFP producing cells. A comparative study showed that the presence of two HNF-1 and one HNF-4 site is a characteristic feature of human but not rodent AFP enhancer. The mRNA levels of the liver-enriched transcription factors (LETFs) were variable in individual HCC cell lines and together with silencer activities may underlie differential expression of the AFP gene.
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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".