Detection of hepatocellular carcinoma in hepatitis C patients: biomarker discovery by LC-MS.
Detection of hepatocellular carcinoma in hepatitis C patients: biomarker discovery by LC-MS.
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DOI：
10.1016/j.jchromb.2014.02.043
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发表时间：
2014-09-01
影响因子：
3
通讯作者：
Raftery, Daniel
中科院分区：
文献类型：
--
作者：
Bowers, Jeremiah;Hughes, Emma;Skill, Nicholas;Maluccio, Mary;Raftery, Daniel
关键词：
肝细胞癌在世界范围内占大多数肝癌病例；丙型肝炎病毒感染被认为是肝癌的主要危险因素，即使个体没有发展为正式的肝硬变。对单独患有丙型肝炎或肝细胞癌(有潜在的丙型肝炎病毒)的患者的血清样本，采用了全球性的、无针对性的代谢谱方法。这项研究的主要目标是确定与癌症风险相关的基于代谢物的生物标记物，长期目标是最终改善早期发现和预后。采用高效液-质联用技术对37例肝细胞癌和21例丙型肝炎患者的血清总代谢物进行分析。将基于生物学意义和统计学意义的偏最小二乘判别分析(偏最小二乘判别分析)模型的选择与仅使用p值的传统方法进行了对比。用前一种方法建立的偏最小二乘法-DA模型的灵敏度为92%，特异度为95%，AUROC为0.93。一系列迭代使用三到七种显著改变(p<；0.05)和充分改变(FC≤0.7或FC≥1.3)的代谢物的偏最小二乘-DA模型仅使用p值就显示出最佳性能，该模型能够产生73%的灵敏度、95%的特异度和0.92的AUROC。来自LC-MS的代谢谱很容易将肝细胞癌和丙型肝炎患者与仅有丙型肝炎病毒患者区分开来。高危人群和肝细胞癌之间代谢特征的差异提示有可能在高危患者中识别肝癌的早期发展。在PLSDA建模之前，使用生物学意义作为选择过程可能会为新发现的生物标记物转化为临床应用提供更高的概率。
Hepatocellular carcinoma (HCC) accounts for most cases of liver cancer worldwide; contraction of hepatitis C (HCV) is considered a major risk factor for liver cancer even when individuals have not developed formal cirrhosis. Global, untargeted metabolic profiling methods were applied to serum samples from patients with either HCV alone or HCC (with underlying HCV). The main objective of the study was to identify metabolite based biomarkers associated with cancer risk, with the long term goal of ultimately improving early detection and prognosis. Serum global metabolite profiles from patients with HCC (n=37) and HCV (n=21) were obtained using high performance liquid chromatography-mass spectrometry (HPLC-MS) methods. The selection of statistically significant metabolites for partial least-squares discriminant analysis (PLS-DA) model creation based on biological and statistical significance was contrasted to that of a traditional approach utilizing p-values alone. A PLS-DA model created using the former approach resulted in a model with 92% sensitivity, 95% specificity, and an AUROC of 0.93. A series of PLS-DA models iteratively utilizing three to seven metabolites that were altered significantly (p<0.05) and sufficiently (FC≤0.7 or FC≥1.3) showed the best performance using p-values alone, the PLS-DA model was capable of generating 73% sensitivity, 95% specificity, and an AUROC of 0.92. Metabolic profiles derived from LC-MS readily distinguish patients with HCC and HCV from those with HCV only. Differences in the metabolic profiles between highrisk individuals and HCC indicate the possibility of identifying the early development of liver cancer in at risk patients. The use of biological significance as a selection process prior to PLSDA modeling may offer improved probabilities for translation of newly discovered biomarkers to clinical application.
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