Expression of iron regulatory genes in a rat model of hepatocellular carcinoma
Bibliographic record
Abstract
BACKGROUND/AIMS: The altered iron metabolism in hepatocellular carcinomas (HCCs), characterized by the iron-deficient phenotype, is suggested to be of importance for tumour growth. However, the underlying molecular mechanisms remain poorly understood. We asked whether these iron perturbations would involve altered expression of genes controlling iron homeostasis. METHODS: HCCs were induced in rats by the Solt and Farber protocol of chemical hepatocarcinogenesis, and to evaluate the effects of iron loading, one group of animals were supplemented with dietary iron during tumour progression. Tissue iron contents were determined, labelling indices of S-phase nuclei were calculated, and mRNA levels of iron-regulatory genes were quantitated. Protein levels of ferroportin1 were determined with Western blot. RESULTS: HCCs displayed reduced amount of tissue iron and lack of histologically stainable iron. HCCs expressed significantly higher mRNA levels of genes involved in iron uptake (transferrin receptor-1, divalent metal ion transporter-1), ferroxidase activity (Ferritin-H), and iron extrusion (ferroportin1). The protein levels of ferroportin1 in iron-deficient HCCs were similar as in control livers, and did not increase in HCCs exposed to iron. Hepcidin mRNA levels were decreased in iron-deficient HCCs, rose in response to iron loading and correlated to the tissue iron content. CONCLUSIONS: Taken together, the altered expressions of iron-regulatory genes in HCCs possibly reflect an increased demand for bioavailable iron and a high iron turnover in neoplastic cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".