Prognostic and clinicopathological significance of DEPTOR expression in cancer patients: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: DEP domain containing mammalian target of rapamycin (mTOR)-interacting protein (DEPTOR), a recently discovered endogenous inhibitor of mTOR, has been found to be abnormally expressed in various tumors. Recent studies have demonstrated that DEPTOR could serve as a potential prognostic biomarker in several kinds of cancer. However, the prognostic value of DEPTOR is still controversial so far. PATIENTS AND METHODS: PubMed, Embase and Web of Science were systematically searched to obtain all relevant articles about the prognostic value of DEPTOR in cancer patients. ORs or HRs with corresponding 95% CIs were pooled to estimate the association between DEP-TOR expression and the clinicopathological characteristics or survival of cancer patients. RESULTS: A total of nine eligible studies with 974 cancer patients were included in our meta-analysis. Our results demonstrated that the expression of DEPTOR was not associated with the overall survival (OS) (pooled HR=0.795, 95% CI=0.252-2.509) and event-free survival (EFS) (pooled HR=1.244, 95% CI=0.543-2.848) in cancer patients. Furthermore, subgroup analysis divided by sample size, type of cancer, Newcastle-Ottawa Scale (NOS) score and evaluation of DEPTOR expression showed identical prognostic value. In addition, our analysis also revealed that there was no significant association between expression level of DEPTOR and clinicopathological characteristics, such as tumor stage, lymph node metastasis, differentiation grade and gender. CONCLUSION: Our meta-analysis suggested that despite the fact that DEPTOR could be overexpressed or downregulated in cancer patients, it might not be a potential marker to predict the prognosis of cancer patients.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.035 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".