Abstract：
To identify protein biomarkers capable of early prediction regarding the distinguishing malignant pleural effusion (MPE) from benign pleural effusion (BPE) in patients with lung disease. A four-dimensional data independent acquisition (4D-DIA) proteomic was performed to determine the differentially expressed proteins in samples from 20 lung adenocarcinoma MPE and 30 BPE. The significantly differential expressed proteins were selected for Gene Ontology (GO) enrichment and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis. Protein biomarkers with high capability to discriminate MPE from BPE patients were identified by Random Forest (RF) algorithm prediction model, whose diagnostic and prognostic efficacy in primary tumors were further explored in public datasets, and were validated by ELISA experiment. 50 important proteins (30 up-regulated and 20 down-regulated) were selected out as potential markers to distinguish the MPE from BPE group. GO analysis revealed that those proteins involving the most important cell component is extracellular space. KEGG analysis identified the involvement of cellular adhesion molecules pathway. Furthermore, the Area Under Curve (AUC) of these proteins were ranged from 0.717 to 1.000，with excellent diagnostic properties to distinguish the MPE. Finally, significant survival and gene and protein expression analysis demonstrated BPIFB1, DPP4, HPRT1 and ABI3BP had high discriminating values. SIGNIFICANCE: We performed a 4D-DIA proteomics to determine the differentially expressed proteins in pleural effusion samples from MPE and BPE. Some potential protein biomarkers were identified to distinguish the MPE from BPE patients., which may provide helpful diagnostic and therapeutic insights for lung cancer. This is significant because the median survival time of patients with MPE is usually 4-12 months, thus, it is particularly important to diagnose MPE early to start treatments promptly. The most common causes of MPE are lung cancers, while pneumonia and tuberculosis are the main causes of BPE. If more diagnostic markers could be identified periodically, there would be an important significance to clinical diagnose and treatment with drugs in lung cancer patients.
摘要：
目的鉴定能够早期预测肺部疾病患者恶性胸腔积液(MPE)与良性胸腔积液(BPE)的蛋白质生物标志物。进行四维数据非依赖性采集(4D-DIA)蛋白质组学以确定来自20个肺腺癌MPE和30BPE的样品中的差异表达的蛋白质。选择显著差异表达的蛋白质用于基因本体论(GO)富集和基因和基因组的京都百科全书(KEGG)途径分析。通过随机森林（RF）算法预测模型鉴定了具有高度区分MPE和BPE患者的蛋白质生物标志物，在公共数据集中进一步探讨了其在原发性肿瘤中的诊断和预后功效，并通过ELISA实验进行验证。选择50种重要的蛋白质(30种上调的和20种下调的)作为将MPE与BPE组区分开的潜在标记。GO分析显示，涉及最重要的细胞成分的那些蛋白质是细胞外空间。KEGG分析确定了细胞粘附分子途径的参与。此外，这些蛋白的曲线下面积(AUC)范围为0.717~1.000，具有很好的诊断特性来区分MPE。最后,显著的存活和基因和蛋白表达分析表明BPIFB1、DPP4、HPRT1和ABI3BP具有较高的鉴别值。意义：我们进行了4D-DIA蛋白质组学，以确定来自MPE和BPE的胸腔积液样品中差异表达的蛋白质。鉴定了一些潜在的蛋白质生物标志物以区分MPE与BPE患者。,这可能为肺癌提供有用的诊断和治疗见解。这很重要，因为MPE患者的中位生存时间通常为4-12个月，因此，早期诊断MPE以及时开始治疗尤为重要。MPE最常见的病因是肺癌,而肺炎和肺结核是BPE的主要病因。如果可以定期识别更多的诊断标记，对肺癌患者的临床诊断和药物治疗具有重要意义。
