Meng Cui, Zhiyong Wan, Jia Yang, Dan Liao, Yang Yang, Yin Xiang
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引用次数: 0
Abstract
The expression of programmed cell death-ligand 1 (PD-L1) on circulating tumor cells offers a noninvasive method for the detection of PD-L1 expression in lung cancer, and could serve as a potential surrogate for cancer tissue. However, discrepant results make it difficult to apply PD-L1 on circulating tumor cells to clinical practice. Therefore, we conducted a meta-analysis to investigate the diagnostic value of PD-L1 on circulating tumor cells in lung cancer. To identify the relationship between the expression of PD-L1 on circulating tumor cells and lung cancer, the PubMed, Web of Science, Embase, China National Knowledge Infrastructure, and Wanfang databases were searched from inception to March 2023. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and the corresponding 95% confidence intervals were calculated to assess the diagnostic performance of PD-L1. We also conducted subgroup and sensitivity analyses. A total of 11 studies including 472 lung cancer patients were included in our study. The overall performance in terms of pooled sensitivity and specificity was 0.72 (0.52-0.86) and 0.54 (0.25-0.81), respectively. The positive likelihood ratio, negative likelihood ratio, and area under the curve were 1.57 (0.87-2.84), 0.52 (0.30-0.90), and 0.70 (0.66-0.74), respectively. Deeks' funnel plot test indicated no publication bias. Our analysis demonstrated that positive PD-L1 expression on circulating tumor cells (CTCs) exhibited a moderate diagnostic value in lung cancer, and CTCs may serve as a feasible alternative tissue analysis for the detection of PD-L1 in lung cancer.
期刊介绍:
IJBM is an international, online only, peer-reviewed Journal, which publishes original research and critical reviews primarily focused on cancer biomarkers. IJBM targets advanced topics regarding the application of biomarkers in oncology and is dedicated to solid tumors in adult subjects. The clinical scenarios of interests are screening and early diagnosis of cancer, prognostic assessment, prediction of the response to and monitoring of treatment.