School of Medicine, Nanjing University, Nanjing, Jiangsu, China; Guangzhou Huayin Medical Laboratory Center, Guangzhou, Guangdong, China.
Department of Respiratory and Critical Care Medicine, The First People's Hospital of Foshan, Foshan, Guangdong, China; Department of Geriatric Respiratory Medicine, Guangdong Provincial Geriatrics Institute,Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China.
J Proteomics. 2024 Jun 30;302:105201. doi: 10.1016/j.jprot.2024.105201. Epub 2024 May 18.
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.
为了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）的蛋白生物标志物。我们采用了一种 4D-DIA 蛋白质组学方法，以确定来自 20 例肺腺癌 MPE 和 30 例 BPE 样本中的差异表达蛋白。对差异表达蛋白进行基因本体论（GO）富集和京都基因与基因组百科全书（KEGG）通路分析。通过随机森林（RF）算法预测模型鉴定出能够高能力区分 MPE 与 BPE 患者的蛋白生物标志物，进一步在公共数据集探索其在原发性肿瘤中的诊断和预后效力，并通过 ELISA 实验进行验证。鉴定出 50 个重要蛋白（30 个上调和 20 个下调）作为潜在标志物来区分 MPE 与 BPE 组。GO 分析表明，这些蛋白最主要涉及的细胞组份是细胞外空间。KEGG 分析鉴定出细胞黏附分子途径的参与。此外，这些蛋白的曲线下面积（AUC）范围从 0.717 到 1.000，具有良好的诊断性能来区分 MPE。最后，显著的生存和基因及蛋白表达分析表明 BPIFB1、DPP4、HPRT1 和 ABI3BP 具有高鉴别价值。意义：我们进行了 4D-DIA 蛋白质组学分析，以确定来自 MPE 和 BPE 的胸腔积液样本中的差异表达蛋白。鉴定出一些潜在的蛋白生物标志物来区分 MPE 与 BPE 患者，这可能为肺癌提供有帮助的诊断和治疗见解。这是重要的，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 以迅速开始治疗尤为重要。MPE 的最常见原因是肺癌，而肺炎和肺结核是 BPE 的主要原因。如果能够定期鉴定出更多的诊断标志物，将对肺癌患者的临床诊断和药物治疗具有重要意义。
