Prognostic and Clinicopathological Value of Programmed Cell Death Ligand1 Expression in Patients With Small Cell Lung Cancer: A Meta-Analysis

程序性细胞死亡配体 1 表达对小细胞肺癌患者的预后和临床病理学价值:一项荟萃分析

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作者:Huarong Cai, Haimei Zhang, Yuequan Jiang

Background

Programmed death-ligand 1 (PD-L1) is an immune checkpoint molecule expressed by cancer cells. Previous studies have demonstrated the prognostic role of PD-L1 expression in patients with small cell lung cancer (SCLC), where the

Conclusions

This meta-analysis suggests that PD-L1 expression is not a significant prognostic factor of poor survival in SCLC. Because of significant variations, high-quality studies are needed to validate our results.

Methods

We searched the PubMed, Embase, ISI Web of Science, and Cochrane Library databases for articles published before and on March 2nd, 2020. Data of PD-L1 expression in tumor cells detected using immunohistochemistry methods were extracted for analysis. Pooled hazard ratios (HRs) with confidence intervals (CIs) and odds ratios (ORs) with 95% CIs were calculated to assess the correlations among PD-L1, overall survival (OS), and clinicopathological factors.

Results

Nine studies of 921 patients published between 2015 and 2019 were included in this meta-analysis. The pooled data (HR = 0.91, 95% CI = 0.46-1.80, p = 0.787) indicated that PD-L1 expression is not a significant predictor of poor OS. Moreover, the results also revealed that PD-L1 expression is not significantly associated with gender (OR = 1.12, 95% CI = 0.73-1.74, p = 0.601), age (OR = 1.15, 95% CI = 0.58-2.30, p = 0.683), pN stage (OR = 0.65, 95% CI = 0.24-1.72, p = 0.381), pT stage (OR = 1.16, 95% CI = 0.26-5.23, p = 0.847), serum lactate dehydrogenase level (OR = 1.06, 95% CI = 0.13-8.43, p = 0.958), or performance status (OR = 0.69, 95% CI = 0.24-1.95, p = 0.479). No significant publication bias was detected in this meta-analysis. Conclusions: This meta-analysis suggests that PD-L1 expression is not a significant prognostic factor of poor survival in SCLC. Because of significant variations, high-quality studies are needed to validate our results.

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