Systematic Review, Meta-Analysis and Bioinformatic Analysis of Biomarkers for Prognosis of Malignant Pleural Mesothelioma

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Abstract

In previous studies, non-invasive diagnostic biomarkers showed great benefit in the early-stage diagnosis of malignant pleural mesothelioma (MPM). However, the accuracy of different biomarkers was controversial. In this study, meta-analysis and bioinformatics analysis were conducted to compare the accuracy of the following three biomarkers and explore the relationship between the gene expression levels and MPM. A systematic search of meta-analysis was conducted using PubMed, EMBASE and Cochrane Library to identify relevant studies from the inception to March 2021. QUADAS-2 for Quality Assessment of Diagnostic Accuracy Studies was used to evaluate the quality of eligible studies. The meta-analysis was performed utilizing Stata 15.0 and Review Manager 5.4 software. The meta-analysis results showed that 31 studies that involved 8750 participants were included. The pooled sensitivity and specificity (SPE) were 0.90 (95% CI: 0.74, 0.97) and 0.91 (95% CI: 0.84, 0.95) for Fibulin-3, 0.66 (95% CI, 0.51–0.78) and 0.91 (95% CI, 0.82–0.96) for mesothelin (MSLN), 0.68 (95% CI: 0.63,0.73) and 0.86 (95% CI: 0.82,0.90) for soluble mesothelin-related peptides (SMRP), and 0.74 (95% CI, 0.66-0.80) and 0.89 (95% CI, 0.85–0.91) for MSLN + SMRP + Fibulin-3. Compared with the other two biomarkers, Fibulin-3 may be more appropriate to be one of the indicators for combined diagnosis. Bioinformatics analysis showed that the low expression level of the MSLN gene was significantly related to longer survival time and better prognosis of MPM patients. However, considering the limitation in the quality and sample size of the included research, further studies are required.

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Lu, Z., Zhang, W., Huang, K., Zhu, M., Gu, X., Wei, D., … Wang, H. (2022, September 1). Systematic Review, Meta-Analysis and Bioinformatic Analysis of Biomarkers for Prognosis of Malignant Pleural Mesothelioma. Diagnostics. Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/diagnostics12092210

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