Circulating kynurenine, tryptophan levels, and their ratio in lung cancer: A meta-analysis revealing subtype and outcome specific variations.

e20548 Background: Dysregulation of the kynurenine pathway driven by indoleamine 2,3-dioxygenase-1 (IDO1), contributes to immune escape in lung cancer. Circulating kynurenine (Kyn), tryptophan (Trp), and the kynurenine-to-tryptophan (Kyn:Trp) ratio have been proposed as non-invasive biomarkers; however, reported associations with lung cancer risk, subtype, and outcomes remain inconsistent. Methods: We searched the databases PubMed, Embase, and Scopus for studies published in this context from inception to December 1, 2025. Eligible studies reported circulating Kyn, Trp, or Kyn:Trp in lung cancer patients and controls. Standardized mean differences (SMDs) was used to generate a pooled comparison of biomarker levels between lung cancer and controls. Additionally, we pooled the odds ratios (ORs) of lung cancer incidence and hazard ratios (HRs) for mortality based on high biomarker levels. Results: 8 studies encompassing 6,600+ lung cancer patients and 7,000+ controls were included. Compared with controls, lung cancer patients exhibited significantly lower Trp levels (SMD −1.54, p &lt; 0.00001) and higher Kyn/Trp ratios (SMD 0.48, p = 0.0008), while absolute Kyn levels did not differ significantly. High Kyn (OR 1.20) and high Kyn/Trp ratio (OR 1.43) were associated with increased odds of lung cancer. Subtype analysis revealed a strong association between elevated Kyn/Trp ratio and squamous cell carcinoma (SCC) (OR 2.21), but not adenocarcinoma or SCC. No significant association was observed between Kyn/Trp ratio and mortality. Conclusions: Lung cancer is characterized by systemic Trp depletion and increased Kyn pathway flux. The Kyn/Trp ratio emerges as a robust immune-metabolic biomarker, particularly relevant to SCC, supporting its potential utility in risk stratification and biological phenotyping. Study characteristics and patient demographics. Author and year Study location Study design No. of lung cancer patients Age and gender distribution Histological subtypes Proportion of active smokers Biomarker estimation method NOS score Chen et al. 2025 China Observational 195 51 (21-88)M:F: 61:39 AC: 77%SCC: 17% 20% HPLC 8 Chuang et al. 2013 Europe Case-control 893 59 (42-72)M:F: 62:38 AC: 32%SCC: 22%LCC: 7%SCLC: 16%Other: 23% 59% MS 9 Engin et al. 2009 Turkey Case-control 36 NRM:F: 31:5 NSCLC: 75%SCLC: 25% 11% HPLC 8 Huang et al. 2020 US/Europe/Australia/Asia Case-control 5,364 60 (44-72)M:F: 54:46 AC: 38.4%SCC: 15.5%SCLC: 9.2%LCC: 3.3%Other: 33.6% 47% MS 9 Zhao et al. 2014 China Case-control 27 60 (50-71)M:F: 18:19 NR NR AA analysis 7 Suzuki et al. 2009 Japan Cross-sectional 123 66.4 (10.1)M:F: 99:24 NSCLC: 88%SCLC: 12% NR MS 9 Creelan et al. 2013 USA Single-arm trial 33 62.4(7.9) ACC: 33%SCC: 30%Other: 36% 36% HPLC/MS 9 Mandarano et al. 2021 Italy <jats:td colspan="1"