[Prognostic Value of STMN1 Expression in Non-small Cell Lung Cancer: A Meta-analysis].
Bibliographic record
Abstract
BACKGROUND: Lung cancer is one of the malignant tumors with the highest morbidity and mortality rates worldwide, seriously threatening human health. Non-small cell lung cancer (NSCLC) accounts for more than 85% of all lung cancer cases. STMN1 is a microtubule depolymerizing protein widely present in the cytoplasm and its expression level is associated with the prognosis of NSCLC patients. Through meta-analysis, this study aimed to investigate the predictive value of the expression level of STMN1 for the prognosis of lung cancer and screen for tumor markers with high sensitivity and specificity to optimize the whole-process management of lung cancer patients. METHODS: The PubMed, The Cochrane Library, Embase, WanFang and CNKI databases were searched from the inception to Sep 6, 2024 for relevant literature. The quality of included studies was assessed by the Newcastle-Ottawa Scale (NOS) score. The hazard ratio (HR) with 95%CI was combined to assess the relationship between STMN1 expression and prognostic factors. The prognostic indicators included the overall survival (OS) and disease-free survival (DFS). All statistical analysis was conducted by the STATA 17.0 software. RESULTS: A total of 5 high-quality studies (NOS score≥6 points) involving 754 patients were enrolled. The pooled results demonstrated that overexpression of STMN1 was significantly related to worse OS (HR=2.28, 95%CI: 1.79-2.91, P<0.001) and DFS (HR=2.14, 95%CI: 1.45-3.17, P<0.001). Overexpression of STMN1 was a risk factor for poor prognosis of NSCLC patients. CONCLUSIONS: Overexpression of STMN1 is a poor prognostic factor in NSCLC patients. STMN1 may serve as a prognostic biomarker for NSCLC patients. However, more researches are still needed to verify the above findings.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.064 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".