Abstract
BACKGROUND: The main types of PD-L1 in the blood include soluble PD-L1 (sPD-L1), exosomal PD-L1 (exoPD-L1), and PD-L1 in circulating tumor cells (CTCs). However, the predictive and prognostic values of these three indicators in patients with non-small cell lung cancer (NSCLC) undergoing immune checkpoint inhibitor (ICI) therapy are unclear, warranting a systematic meta-analysis. METHODS: A systematic literature search was performed in the PubMed, Cochrane Library, and Embase databases. The pooled hazard ratio (HR) and 95% confidence interval (CI) values were extracted from the included studies to investigate the correlation between the three PD-L1 indicators and overall survival (OS) or progression-free survival (PFS). The Newcastle-Ottawa Scale (NOS) was used to examine the quality of the included studies. Subgroup analyses were employed to investigate the heterogeneity. The publication bias of the included studies was assessed using Begg's and Egger's tests. P < 0.05 was regarded as significantly different. RESULTS: The pooled results revealed that high pre-treatment sPD-L1 levels were significantly associated with inferior OS (HR = 2.32, 95% CI = 1.68-3.18, P < 0.001) and PFS (HR = 2.52, 95% CI = 1.72-3.68, P < 0.001). However, dynamic changes in sPD-L1 after immunotherapy were not statistically significant for OS (HR = 1.46, 95% CI = 0.65-3.26, P > 0.05) or PFS (HR = 1.62, 95% CI = 0.92-2.86, P > 0.05). Meanwhile, the upregulated pre-treatment exoPD-L1 levels were significantly associated with poor PFS (HR = 4.44, 95% CI = 2.87-6.89, P < 0.001), whereas the post-treatment dynamic upregulation of exoPD-L1 was significantly correlated with superior PFS (HR = 0.36, 95% CI = 0.24-0.54, P < 0.001) and OS (HR = 0.20, 95% CI = 0.07-0.53, P < 0.001). For PD-L1 in CTCs, the pooled results indicated that PD-L1 expression in CTCs was not significantly correlated with OS (HR = 0.75, 95% CI = 0.49-1.13, P = 0.170) and PFS (HR = 0.79, 95% CI = 0.59-1.06, P = 0.12). CONCLUSIONS: Blood-based PD-L1 analysis is a potential strategy for predicting treatment efficacy and prognosis in patients with cancer.