Overexpression of iron regulatory protein 1 suppresses growth of tumor xenografts
Bibliographic record
Abstract
Iron is essential for proliferation of normal and neoplastic cells. Cellular iron uptake, utilization and storage are regulated by transcriptional and post-transcriptional mechanisms. We hypothesized that the disruption of iron homeostasis may modulate the growth properties of cancer cells. To address this, we employed H1299 lung cancer cells engineered for tetracycline-inducible overexpression of the post-transcriptional regulator iron regulatory protein 1 (IRP1). The induction of IRP1 (wild-type or the constitutive IRP1(C437S) mutant) did not affect the proliferation of the cells in culture, and only modestly reduced their efficiency to form colonies in soft agar. However, IRP1 dramatically impaired the capacity of the cells to form solid tumor xenografts in nude mice. Tumors derived from IRP1-transfectants were <20% in size compared to those from parent cells. IRP1 coordinately controls the expression of transferrin receptor 1 (TfR1) and ferritin by binding to iron-responsive elements (IREs) within their mRNAs. Biochemical analysis revealed high expression of epitope-tagged IRP1 in tumor tissue, which was associated with a profound increase in IRE-binding activity. As expected, this response misregulated iron metabolism by increasing TfR1 levels. Surprisingly, IRP1 failed to suppress ferritin expression and did not affect the levels of the iron transporter ferroportin. Our results show that the overexpression of IRP1 is associated with an apparent tumor suppressor phenotype and provide a direct regulatory link between the IRE/IRP system and cancer.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".