上海交通大学学报（医学版）

›› 2012, Vol. 32 ›› Issue (1): 77-.doi: 10.3969/j.issn.1674-8115.2012.01.015

• 论著（临床研究） • 上一篇    下一篇

基于多种判别分析的原发性肝肿瘤代谢谱研究

李 芬1, 赵爱华1, 杨景雷2, 陈天璐2, 贾 伟2   

  1. 1.上海交通大学药学院, 上海 200240； 2.上海交通大学系统生物医学研究院, 上海 200240
  • 出版日期:2012-01-28 发布日期:2012-01-29
  • 作者简介:李 芬(1987—), 女, 硕士生；电子信箱: sophialf@sjtu.edu.cn。
  • 基金资助:

    上海市自然科学基金（10ZR1414800）

Metabolic profiling research of primary liver tumor based on multiple discriminant analysis

LI Fen1, ZHAO Ai-hua1, YANG Jing-lei2, CHEN Tian-lu2, JIA Wei2   

  1. 1.School of Pharmacy, Shanghai Jiaotong University, Shanghai 200240, China；2.Shanghai Center for Systems Biomedicine, Shanghai Jiaotong University, Shanghai 200240, China
  • Online:2012-01-28 Published:2012-01-29
  • Supported by:

    Natural Science Foundation of Shanghai, 10ZR1414800

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摘要：

目的 应用判别分析方法研究临床样本，比较不同的判别方法对疾病代谢谱的分类性能。方法 应用线性判别分析（LDA）、二次判别分析（QDA）和逻辑判别分析（LogDA）对临床109例健康人、87例良性肝肿瘤患者以及31例恶性肝肿瘤患者的血清样本代谢谱数据进行分析；从健康对照和原发性肝肿瘤（包括良性和恶性）以及良性和恶性肝肿瘤两个类别，对3种方法的诊断性能进行比较。结果 对于临床代谢谱数据，3种方法对正常与肝肿瘤的区分效果均优于恶性肿瘤与良性肿瘤的区分。QDA的总体性能优于LDA和LogDA，对正常和肝肿瘤的诊断精密度达到87.67％，对良性和恶性肝肿瘤的诊断精密度达到67.80％。结论 LDA、QDA和LogDA 3种方法中，QDA最适于原发性肝肿瘤代谢谱数据的分析。

关键词: 判别分析, 代谢轮廓分析, 代谢组学, 肝肿瘤, 肝细胞癌

Abstract:

Objective To compare the diagnosis performance of discriminant analysis methods through application on clinical serum samples. Methods Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and logistic discriminant analysis (LogDA) were applied to metabolic profiling analysis deriving from clinical serum samples of 109 healthy controls, 87 patients with benign liver tumor and 31 patients with malignant liver tumor. The diagnosis performance of these three methods was compared in discrimination of healthy controls and patients with liver tumor(benign tumor and malignant tumor) and in discrimination of patients with benign tumor and those with malignant tumor. Results Based on current clinical metabolic profiling data, the effectiveness of all these three methods worked better in discrimination of healthy controls and patients with liver tumor than in discrimination of patients with benign tumor and those with malignant tumor. The overall performance of QDA was superior to LDA and LogDA, with the precision of 87.67% in discrimination of healthy controls and patients with liver tumor and the precision of 67.80% in discrimination of patients with benign tumor and those with malignant tumor. Conclusion QDA outperforms LDA and LogDA in processing primary liver tumor metabolic data.

Key words: discriminant analysis, metabolic profiling, metabonomics, primary liver tumor, hepatocellular carcinoma