LDH and glycolytic activity as predictors of immunotherapy response in gastric cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Immune checkpoint inhibitors (ICIs) have transformed gastric cancer (GC) treatment, but response heterogeneity necessitates reliable prognostic biomarkers. This meta-analysis investigates the predictive value of metabolic (LDH, glycolytic activity) and inflammatory markers (NLR, PLR, LMR) in GC patients receiving ICIs. Methods: We systematically analyzed 17 studies (n=3,842) from major databases through March 2024. Pooled hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS) were calculated using random-effects models, with subgroup analyses by treatment type (monotherapy/combination). Study quality was assessed via Newcastle-Ottawa Scale. Results: Elevated LDH significantly predicted poorer OS (HR=2.01, 95%CI:1.72-2.34) and PFS (HR=2.23, 95%CI:1.29-3.66), with minimal heterogeneity (I²=0%). Similarly, high NLR (HR=1.73) and PLR (HR=1.65) correlated with worse outcomes, while elevated LMR showed protection (HR=0.73). These associations remained consistent across treatment modalities and geographic regions (all Asian studies). Conclusions: LDH and inflammatory markers are robust, clinically accessible prognostic biomarkers in GC immunotherapy. Their validation enables improved risk stratification and supports development of combination strategies targeting metabolic-immune crosstalk to enhance ICI efficacy.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.028 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".