BIOM-55. PROGNOSTIC UTILITY OF SERUM LACTATE DEHYDROGENASE IN GLIOMA PATIENTS: A SYSTEMATIC REVIEW

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Abstract BACKGROUND Gliomas account for the majority of malignant brain tumors. Prognostication currently relies on molecular markers such as isocitrate dehydrogenase (IDH) mutation and O6-methylguanine-DNA methyltransferase (MGMT) methylation. More recently, lactate dehydrogenase (LDH) has shown promising results to be used as a laboratory marker to predict prognosis in patients with tumors, which leads to the question of whether it can perform a similar role in glioma prognosis. Despite growing interest, a comprehensive synthesis of the literature is lacking. METHODS A systematic review was conducted following the PRISMA 2020 guidelines. Searches were performed across of PubMed/MEDLINE, Scopus, and Cochrane Library databases from inception through April 15, 2025, using a predefined search strategy. Two independent reviewers screened 1,350 articles with disagreements resolved by a third. RESULTS Eight studies were included, covering 1,908 glioma cases. LDH cutoff values for risk stratification ranged mainly between 170 and 300 U/L. Elevated LDH levels correlated with more aggressive tumor subtypes, especially high-grade gliomas, and were independently predictive of decreased overall survival and progression-free survival across multiple cohorts with hazard ratios commonly ranging from 1.4 to >2.0 after adjustment for confounders such as age and performance status. Median OS differences between high and low LDH groups ranged from 5 to 11 months. Receiver operating characteristic (ROC) analyses demonstrated moderate predictive accuracy for LDH levels, with area under the curve (AUC) values typically between 0.60 and 0.74. Some studies integrated LDH into prognostic scoring systems that effectively stratified patients by survival risk. CONCLUSIONS This review offers comprehensive overview of LDH s a prognostic biomarker in glioma patients. LDH appears to correlate with tumor aggressiveness and clinical outcomes and may serve as a cost-effective, accessible adjunct to current prognostic models in neuro-oncology

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