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Metabolomic analysis based on 1H-nuclear magnetic resonance spectroscopy metabolic profiles in tuberculous malignant and transudative pleural effusion

机译:基于1H核磁共振波谱代谢谱的结核恶性和渗出性胸腔积液代谢组学分析

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摘要

Pleural effusion is a common clinical manifestation with various causes. Current diagnostic and therapeutic methods have exhibited numerous limitations. By involving the analysis of dynamic changes in low molecular weight catabolites, metabolomics has been widely applied in various types of disease and have provided platforms to distinguish many novel biomarkers. However, to the best of our knowledge, there are few studies regarding the metabolic profiling for pleural effusion. In the current study, 58 pleural effusion samples were collected, among which 20 were malignant pleural effusions, 20 were tuberculous pleural effusions and 18 were transudative pleural effusions. The small molecule metabolite spectrums were obtained by adopting 1H nuclear magnetic resonance technology, and pattern-recognition multi-variable statistical analysis was used to screen out different metabolites. One-way analysis of variance, and Student-Newman-Keuls and the Kruskal-Wallis test were adopted for statistical analysis. Over 400 metabolites were identified in the untargeted metabolomic analysis and 26 metabolites were identified as significantly different among tuberculous, malignant and transudative pleural effusions. These metabolites were predominantly involved in the metabolic pathways of amino acids metabolism, glycometabolism and lipid metabolism. Statistical analysis revealed that eight metabolites contributed to the distinction between the three groups: Tuberculous, malignant and transudative pleural effusion. In the current study, the feasibility of identifying small molecule biochemical profiles in different types of pleural effusion were investigated reveal novel biological insights into the underlying mechanisms. The results provide specific insights into the biology of tubercular, malignant and transudative pleural effusion and may offer novel strategies for the diagnosis and therapy of associated diseases, including tuberculosis, advanced lung cancer and congestive heart failure.
机译:胸腔积液是具有多种原因的常见临床表现。当前的诊断和治疗方法表现出许多局限性。通过分析低分子量分解代谢物的动态变化,代谢组学已广泛应用于各种疾病,并提供了区分许多新型生物标志物的平台。然而,据我们所知,关于胸腔积液的代谢谱分析的研究很少。本研究收集了58例胸腔积液样本,其中恶性胸腔积液20例,结核性胸腔积液20例,渗出性胸腔积液18例。采用 1 H核磁共振技术获得小分子代谢物谱,并利用模式识别多变量统计分析法筛选出不同的代谢物。方差的单向分析以及Student-Newman-Keuls和Kruskal-Wallis检验用于统计分析。在非靶向代谢组学分析中鉴定出400多种代谢物,在结核性,恶性和渗出性胸膜积液之间鉴定出26种代谢物具有显着差异。这些代谢物主要参与氨基酸代谢,糖代谢和脂质代谢的代谢途径。统计分析表明,八种代谢产物有助于区分这三类:结核性,恶性和渗出性胸腔积液。在当前的研究中,调查了在不同类型的胸腔积液中识别小分子生化特征的可行性,从而揭示了对潜在机制的新生物学见解。结果为结核,恶性和渗出性胸腔积液的生物学研究提供了具体见解,并可能为诊断和治疗相关疾病(包括结核,晚期肺癌和充血性心力衰竭)提供新的策略。

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