The predictive implication of programmed cell death ligand 1 expression in extranodal natural killer/T-Cell lymphoma and its correlation with clinicopathological features: a systematic review and meta-analysis

BACKGROUND: Although programmed cell death ligand 1 (PD-L1) expression and function in hematologic malignancies have aroused extensive attention, its prognostic value for extranodal natural killer/T-cell lymphoma (ENKTL) is still unknown. Therefore, we conducted this meta-analysis to explore the predictive value of neoplastic PD-L1 expression for ENKTL. METHODS: The PubMed, Embase, Web of Science, and CNKI databases were searched to identify eligible observational studies reporting PD-L1 expression and survival outcomes of ENKTL patients. The search was conducted in accordance with the Meta-analyses Of Observative Studies in Epidemiology (MOOSE) guidelines. The pooled hazard ratios (HRs) and 95% confidence intervals (95% CIs) were adopted to analyze survival outcomes, and the odds ratios (ORs) and 95% CIs were adopted for clinicopathological parameters. Review Manager 5.3 and STATA 17.0 were used for statistical analysis. Potential publication bias was evaluated by funnel plot and Egger’s test. RESULTS: A total of 433 patients with ENKTL were included across seven studies. The pooled results showed no significant relationship between neoplastic PD-L1 expression and overall survival (OS) (HR =1.35, 95% CI: 0.49–3.75, P=0.559). We also performed subgroup analyses. However, increased PD-L1 expression was associated with a low international prognostic index (IPI) score of 0–1 (OR =2.46; 95% CI: 1.11–5.45, P=0.03), good performance status (OR =1.97; 95% CI: 1.11–3.51, P=0.02), and a good treatment effect (OR =2.61; 95% CI: 1.01–6.70, P=0.05). CONCLUSIONS: PD-L1-positive expression in patients with ENKTL was correlated with favorable clinical features. Thus, PD-L1-positive expression appears to be a potential predictor of treatment benefits. Additional large-scale, high-quality studies are needed to further explore its predictive value.