Meta-analysis of FTO expression on the clinicopathologic characteristics and prognosis of gastric cancer
Bibliographic record
Abstract
BACKGROUND: Fat mass and obesity-related gene (FTO) is aberrantly expressed in various cancers including highly expressed in gastric cancer tissues. The aim of this meta-analysis was to explore the effect of FTO expression on clinicopathological and prognostic outcome of gastric cancer. METHODS: China National Knowledge Infrastructure (CNK), Wanfang database, VIP database, Chinese biomedical literature database (CBM), PubMed, Web of Science, the Cochrane library and EMBASE database were searched to screen the literatures according to the inclusion criteria. The search time was the database establishment until May 2023. The two researchers independently searched and screened the literature, extracted pathological data, and conducted The Newcastle-Ottawa scale (NOS) quality evaluation. Analyze the correlation between FTO and pathological indicators of gastric cancer patients and the impact on prognosis, use and Stata 12.0, software for Meta-analysis. RESULTS: A total of 1619 patients were studied in this study. The results of the Meta-analysis showed that higher expression levels of FTO were associated with TMN stage (OR = 1.83, 95% CI: 1.11-3.03, P = .019), liver metastases (OR = 3.73, 95% CI: 1.49-9.31, P = .005), vascular invasion (OR = 2.22, 95% CI: 1.36-3.61, P = .001), poorer overall survival (OS) (HR = 0.46, 95% CI: 0.34-0.58, P < .001) and recurrence-free survival (HR = 0.56, 95% CI: 0.40-0.73, P < .001) in gastric cancer patients. There was no significant relationship with the degree of differentiation (OR = 1.08, 95% CI: 0.49-2.35, P = .852), age (OR = 0.89, 95% CI: 0.71-1.11, P = .306), and gender (OR = 0.92, 95% CI: 0.74-1.14, P = .432). CONCLUSION: High expression of FTO was associated with risk of distant metastases and poor prognosis for patients with gastric cancer. FTO may be a potential prognostic biomarker for gastric cancer, but due to the limited number of literature, the above results need further research.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.039 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".