Cerebrospinal fluid metabolomic profiles can discriminate patients with leptomeningeal carcinomatosis from patients at high risk for leptomeningeal metastasis.
Cerebrospinal fluid metabolomic profiles can discriminate patients with leptomeningeal carcinomatosis from patients at high risk for leptomeningeal metastasis.
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DOI：
10.18632/oncotarget.20983
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发表时间：
2017-11-24
期刊：
影响因子：
--
通讯作者：
Gwak HS
中科院分区：
文献类型：
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作者：
Yoo BC;Lee JH;Kim KH;Lin W;Kim JH;Park JB;Park HJ;Shin SH;Yoo H;Kwon JW;Gwak HS
早期诊断软脑膜癌病(LMC)对于改善这一可怕疾病的预后是必要的。然而，脑脊液(CSF)细胞学检查经常是假阴性。我们研究了脑脊液代谢组图谱是否可以用来区分LMC患者和有发生LMC风险的患者。使用PCA-DA对10,905个LMI进行了评估。LMI对第二组的敏感性为85%，特异性为91%。在选择包括二乙酰精胺和纤维蛋白原片段在内的33个LMI后，脑脊液代谢组学分析对区分1b组和其他组的敏感性为100%，特异性为93%。在选择了包括磷脂酰胆碱在内的21个LMI后，脑脊液代谢组学分析将LMC(组2)患者与组3和组4的高危组区分开来，灵敏度和特异度均为100%。我们前瞻性地收集了五组患者的脑脊液：1a组，系统性肿瘤；1b组，无肿瘤；第2组，LMC；第3组，脑转移；第4组，脑转移以外的脑肿瘤。用质谱仪检测脑脊液样品中的所有代谢物为低质量离子(LMI)。使用基于主成分分析的判别分析(PCA-DA)和两种搜索算法来选择区分感兴趣患者组和对照组的LMI。脑脊液代谢产物分析可用于诊断LMC并排除高危患者，准确率为100%。我们期待未来的验证性试验来评估支持脑脊液细胞学的脑脊液代谢谱。
Early diagnosis of leptomeningeal carcinomatosis (LMC) is necessary to improve outcomes of this formidable disease. However, cerebrospinal fluid (CSF) cytology is frequently false negative. We examined whether CSF metabolome profiles can be used to differentiate patients with LMC from patients having a risk for development of LMC. A total of 10,905 LMIs were evaluated using PCA-DA. The LMIs defined Group 2 with a sensitivity of 85% and a specificity of 91%. After selecting 33 LMIs, including diacetylspermine and fibrinogen fragments, the CSF metabolomics profile had a sensitivity of 100% and a specificity of 93% for discriminating Group 1b from the other groups. After selecting 21 LMIs, including phosphatidylcholine, the CSF metabolomics profile differentiated LMC (Group 2) patients from the high-risk groups of Group 3 and Group 4 with 100% sensitivity and 100% specificity. We prospectively collected CSF from five groups of patients: Group 1a, systemic cancer; Group 1b, no tumor; Group 2, LMC; Group 3, brain metastasis; Group 4, brain tumor other than brain metastasis. All metabolites in the CSF samples were detected as low-mass ions (LMIs) using mass spectrometry. Principal component analysis-based discriminant analysis (PCA-DA) and two search algorithms were used to select the LMIs that differentiated the patient groups of interest from controls. Analysis of CSF metabolite profiles could be used to diagnose LMC and exclude patients at high-risk of LMC with a 100% accuracy. We expect a future validation trial to evaluate CSF metabolic profiles supporting CSF cytology.