Multivariate classification of urine metabolome profiles for breast cancer diagnosis.
Multivariate classification of urine metabolome profiles for breast cancer diagnosis.
复制标题
DOI：
10.1186/1471-2105-11-s2-s4
复制
发表时间：
2010-04-16
影响因子：
3
通讯作者：
Lee D
中科院分区：
文献类型：
--
作者：
Kim Y;Koo I;Jung BH;Chung BC;Lee D
使用尿液的诊断技术是非侵入性的，廉价的，并且易于在临床环境中执行。尿液中的代谢物作为细胞过程的终产物，与表型密切相关。因此，尿代谢组学在标记物发现和临床应用中是非常有用的。然而，只有单变量的方法已被用于分类研究使用尿液代谢组。由于乳腺癌等复杂疾病的发生发展涉及多个基因或蛋白质，因此包括代谢物在内的多种化合物与复杂疾病相关，需要多变量方法来鉴定这些多代谢物标记物。此外，由于标记物之间的组合效应会严重影响疾病的发展，并且在癌症进展中还存在遗传组成的个体差异或异质性，因此单个标记物不足以识别癌症。我们提出了使用多变量分类技术的分类模型，并开发了一个分析程序，使用代谢组数据进行分类研究。通过这一策略，我们以高准确性鉴定了五种潜在的乳腺癌尿液生物标志物，其中四种生物标志物候选物仅通过单变量方法无法鉴定。我们还提出了潜在的诊断规则，以帮助临床决策。此外，我们发现多个生物标志物之间的组合效应可以提高乳腺癌的区分能力。在这项研究中，我们成功地证明了需要多变量分类来精确诊断乳腺癌。在经过独立队列的进一步验证和实验确认后，这些候选标记物可能会导致临床上适用于乳腺癌早期诊断的检测。
Diagnosis techniques using urine are non-invasive, inexpensive, and easy to perform in clinical settings. The metabolites in urine, as the end products of cellular processes, are closely linked to phenotypes. Therefore, urine metabolome is very useful in marker discoveries and clinical applications. However, only univariate methods have been used in classification studies using urine metabolome. Since multiple genes or proteins would be involved in developments of complex diseases such as breast cancer, multiple compounds including metabolites would be related with the complex diseases, and multivariate methods would be needed to identify those multiple metabolite markers. Moreover, because combinatorial effects among the markers can seriously affect disease developments and there also exist individual differences in genetic makeup or heterogeneity in cancer progressions, single marker is not enough to identify cancers. We proposed classification models using multivariate classification techniques and developed an analysis procedure for classification studies using metabolome data. Through this strategy, we identified five potential urinary biomarkers for breast cancer with high accuracy, among which the four biomarker candidates were not identifiable by only univariate methods. We also proposed potential diagnosis rules to help in clinical decision making. Besides, we showed that combinatorial effects among multiple biomarkers can enhance discriminative power for breast cancer. In this study, we successfully showed that multivariate classifications are needed to precisely diagnose breast cancer. After further validation with independent cohorts and experimental confirmation, these marker candidates will likely lead to clinically applicable assays for earlier diagnoses of breast cancer.