Mechanism by which SUGT1 downregulates FH to promote proliferation and migration in serous ovarian cancer
Bibliographic record
Abstract
BACKGROUND: SUGT1 (Suppressor of the G2 allele of SKP1) and FH (fumarate hydratase) have recently garnered significant attention from the research community. SUGT1 functions as a molecular chaperone, regulating the stability and activity of various proteins, while FH is a key enzyme in the tricarboxylic acid cycle, catalyzing the reversible conversion of fumarate to malate. Existing literature has established their essential roles in signaling, tumorigenesis, and cancer progression. However, their functions and mechanisms in ovarian cancer (OC) remain poorly understood. RESULTS: We found that high SUGT1 expression is associated with a more advanced FIGO stage in OC. SUGT1 knockdown significantly inhibits OC cell proliferation and metastasis, while its overexpression has the opposite oncogenic effect. Mechanistically, we revealed that SUGT1 promotes FH protein degradation via the ubiquitin-proteasome pathway. Moreover, FH knockdown partly reversed the inhibitory effects of SUGT1 knockdown on tumor cell proliferation, migration, and proteins of phosphorylated PI3K/AKT and Vimentin. In summary, We demonstrated that SUGT1 exerts oncogenic functions in OC by regulating FH stability. CONCLUSIONS: Our study is the first to provide experimental evidence elucidating the SUGT1-FH relation and its role in OC progression, offering potential significance for clinical diagnosis and 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".