<scp>mTOR</scp> pathway protein immunoexpression as a prognostic factor for survival in head and neck cancer patients: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Several mTOR pathway proteins are involved in the regulation of cellular anabolism, growth, proliferation, and survival. Activated proteins in the mTOR pathway are deregulated in multiple types of cancers and could influence prognosis. However, it is unclear whether deregulation of mTOR pathway proteins serves a prognostic role in patients with head and neck cancer (HNC). Furthermore, proteins in the mTOR pathway may be important targets for anticancer therapy. The aim of this study was to summarize existing cohort studies to determine whether immunoexpression of mTOR pathway proteins are important prognostic factors for survival in patients with HNC. MATERIALS AND METHODS: A systematic review was performed using the Cochrane, Lilacs, PubMed, ScienceDirect, Scopus, and Web of Science databases (up to 23 January 2015). A meta-analysis was conducted to measure the frequency of protein expression in head and neck cancer patient samples and the prognostic value of mTOR pathway proteins for overall survival (OS) and disease-free survival (DFS). RESULTS: Twelve studies were included in our final analysis. The meta-analysis revealed that the frequency of overall expression of mTOR pathway proteins was 74.42% (CI: 63.3 to 84.0, P < 0.001, n = 2016 samples). The survival meta-analysis showed a pooled hazard ratio for OS and DFS of 1.44 (95% confidence interval [95% CI] 1.14-1.73) and 1.18 (95% CI 0.71-1.64), respectively. CONCLUSION: This systematic review and meta-analysis support evidence that mTOR pathway proteins can be used as predictive markers for survival in patients with HNC because their expression was significantly associated with poor OS and short DFS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.029 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".