Association of liquid biopsy and gastric cancer patients' prognosis: Comprehensive synopsis and a meta-analysis.
Bibliographic record
Abstract
25 Background: Liquid biopsy including circulating tumor cells (CTCs) and cell-free nucleic acids (cfNAs) has offered a minimally invasive approach for detection and measurement of gastric cancer (GC). Reports regarding associations between liquid biopsy and gastric cancer have been emerging rapidly in recent decades, yet their prognostic value still remains paradoxical. Methods: We searched Medline, Embase, Cochrane Central Register of Controlled Trials database for relevant studies that assessed the prognosis significance of CTCs and cfNAs in gastric cancer from peripheral blood(PB). Newcastle-Ottawa Scale (NOS) was used to assess the quality of evidence. Stata 12.0 was used for pooled analysis and subgroup analysis was performed to determine the association of CTCs or cfNAs’ presence and major clinical characteristics. Pooled results were displayed as hazard ratios (HRs) with their 95% confidence intervals (95% CI) with random effect models. Results: We identified 1258 studies, and then 43 were finally eligible for analysis. A total of 3792 patients were included for final evaluation. Pooled analysis showed that detection of certain CTCs, ctDNA or circulating miRNA was associated with poorer overall survival (OS) (CTCs, HR=2.05, 95%CI 1.65-2.55, p < 0.001; circulating miRNA HR=1.74, 95%CI 1.13-2.69, p=0.013; ctDNA, HR=1.77, 95%CI 1.28-2.44, p=0.001) and disease-free survival(DFS) (CTCs, HR=2.92, 95%CI 1.93-4.40, p < 0.001; circulating miRNA, HR=3.30, 95%CI 2.39-4.55, p < 0.001; ctDNA, HR=4.69, 95%CI 2.23-9.86, p < 0.001) of gastric cancer patients, regardless of the disease’s early or late stage. Conclusions: Several high-quality circulating biomarkers or detection methods for gastric cancer prognosis prediction were identified by subgroup analysis, including the Cellsearch system, cytokeratins, miR-20a, miR-200c, etc. Detection of these certain dysregulation circulating markers in patients’ PB indicates poor prognosis with advanced GC 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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.042 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".