Mitochondrial ferritin, a new target for inhibiting neuronal tumor cell proliferation
Bibliographic record
Abstract
Mitochondrial ferritin (FtMt) has a significant effect on the regulation of cytosolic and mitochondrial iron levels. However, because of the deficiency of iron regulatory elements (IRE) in FtMt's gene sequence, the exact function of FtMt remains unclear. In the present study, we found that FtMt dramatically inhibited SH-SY5Y cell proliferation and tumor growth in nude mice. Interestingly, excess FtMt did not adversely affect the development of drosophila. Additionally, we found that the expression of FtMt in human normal brain tissue was significantly higher than that of neuroblastoma, but not higher than that of neurospongioma. However, the expression of transferrin receptor 1 is completely opposite. We therefore hypothesized that increased expression of FtMt may negatively affect the vitality of neuronal tumor cells. Therefore, we further investigated the underlying mechanisms of FtMt's inhibitory effects on neuronal tumor cell proliferation. As expected, FtMt overexpression disturbed the iron homeostasis of tumor cells and significantly downregulated the expression of proliferating cell nuclear antigen. Moreover, FtMt affected cell cycle, causing G1/S arrest by modifying the expression of cyclinD1, cyclinE, Cdk2, Cdk4 and p21. Remarkably, FtMt strongly upregulated the expression of the tumor suppressors, p53 and N-myc downstream-regulated gene-1 (NDRG1), but dramatically decreased C-myc, N-myc and p-Rb levels. This study demonstrates for the first time a new role and mechanism for FtMt in the regulation of cell cycle. We thus propose FtMt as a new candidate target for inhibiting neuronal tumor cell proliferation. Appropriate regulation of FtMt expression may prevent tumor cell growth. Our study may provide a new strategy for neuronal cancer therapy.
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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".