Biology and significance of alpha‐fetoprotein in hepatocellular carcinoma
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related deaths globally due, in part, to the majority of patients being diagnosed with intermediate or advanced stage disease. Our increased understanding of the heterogeneous molecular pathogenesis of HCC has led to significant developments in novel targeted therapies. Despite these advances, there remains a high unmet need for new treatment options. HCC is a complex disease with multiple pathogenic mechanisms caused by a variety of risk factors, making it difficult to characterize with a single biomarker. In fact, numerous biomarkers have been studied in HCC, but alpha-fetoprotein (AFP) remains the most widely used and accepted serum marker since its discovery over 60 years ago. This review summarizes the most relevant studies associated with the regulation of AFP at the gene and protein levels; the pathophysiology of AFP as a pro-proliferative protein; and the correlation of AFP with molecular HCC subclasses, the vascular endothelial growth factor pathway and angiogenesis. Also described are the historical and current uses of AFP for screening and surveillance, diagnosis, its utility as a prognostic and predictive biomarker and its role as a tumour antigen in HCC. Taken together, these data demonstrate the relevance of AFP for patients with HCC and identify several remaining questions that will benefit from future research.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".