Inhibition of Selenoprotein I promotes ferroptosis and reverses resistance to platinum chemotherapy by impairing Akt phosphorylation in ovarian cancer
Bibliographic record
Abstract
Abstract Ovarian cancer (OV) ranks among the deadliest gynecological cancer, known for its high risk of relapse and metastasis, and a general resistance to conventional platinum‐based chemotherapy. Selenoprotein I (SELENOI) is a crucial mediator implicated in human hereditary spastic paraplegia. However, its role in human tumors remains poorly elucidated. Here, we comprehensively analyzed SELENOI expression patterns, functions, and clinical implications across various malignancies through the integration of bulk transcriptomics, cancer databases, and in vitro and in vivo experiments. Pan‐cancer analysis indicated upregulated SELENOI expression across various cancers, correlating with augmented malignancy, suppressed tumor immunity and poor prognosis. Knockdown of SELENOI caused G0/G1‐phase cell cycle arrest and diminished aggressive cancer phenotypes in OV cells. Moreover, SELENOI inhibition augments ferroptosis and reverses the cisplatin resistance in OV cells by modulating Akt phosphorylation. Conversely, overexpression of SELENOI in OV cells enhanced therapeutic sensitivity to cisplatin by upregulating Akt phosphorylation. Importantly, in vivo studies demonstrated that SELENOI inhibition suppressed ovarian tumor growth and enhanced cisplatin's anticancer effects. These findings highlight the significant role of SELENOI in OV by modulating ferroptosis and chemotherapy resistance. Targeting SELENOI represents a promising therapeutic approach to promote the efficacy of platinum‐based chemotherapy in OV, particularly in cases of resistance.
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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".