基于多重珠血清生物标志物分析的新型非小细胞肺癌检测方法。

A novel detection method of non-small cell lung cancer using multiplexed bead-based serum biomarker profiling.

机构信息

Department of Molecular Oncology, Cancer Research Institute, Seoul National University Graduate School of Medicine, Seoul, Korea.

出版信息

J Thorac Cardiovasc Surg. 2012 Feb;143(2):421-7. doi: 10.1016/j.jtcvs.2011.10.046. Epub 2011 Nov 20.

DOI:10.1016/j.jtcvs.2011.10.046

PMID:22104668

Abstract

OBJECTIVES

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality. Development of an early diagnosis method may improve survivals. We aimed to develop a new diagnostic model for NSCLC using serum biomarkers.

METHODS

We set up a patient group diagnosed with NSCLC (n = 122) and a healthy control group (n = 225). Thirty serum analytes were selected on the basis of previous studies and a literature search. An antibody-bead array of 30 markers was constructed using the Luminex bead array platform (Luminex Inc, Austin, Tex) and was analyzed. Each marker was ranked by importance using the random forest method and then selected. Using selected markers, multivariate classification algorithms were constructed and were validated by application to independent validation cohort of 21 NSCLC and 28 control subjects.

RESULTS

There was no difference in demographics between patients and the control population except for age (64.8 ± 10.0 for patients vs 53.0 ± 7.6 years for the control group). Among the 30 serum proteins, 23 showed a difference between the 2 groups (12 increased and 11 decreased in the patient group). We found the highest accuracy of multivariate classification algorithms when using the 5 highest-ranked biomarkers (A1AT, CYFRA 21-1, IGF-1, RANTES, AFP). When we applied the algorithms on a validation cohort, each method recognized the patients from the controls with high accuracy (89.8% with random forest, 91.8% with support vector machine, 88.2% with linear discriminant analysis, and 90.5% with logistic regression).

CONCLUSIONS

We confirmed that a new diagnostic method using 5 serum biomarkers profiling constructed by multivariate classification algorithms could distinguish NSCLC from healthy controls with high accuracy.

摘要

目的

非小细胞肺癌（NSCLC）是癌症相关死亡的主要原因。开发早期诊断方法可能会提高存活率。我们旨在使用血清生物标志物为 NSCLC 开发新的诊断模型。

方法

我们建立了一个诊断为 NSCLC 的患者组（n=122）和一个健康对照组（n=225）。根据先前的研究和文献检索，选择了 30 种血清分析物。使用 Luminex 珠子阵列平台（Luminex Inc，Austin，Tex）构建了包含 30 种标志物的抗体珠阵列，并进行了分析。使用随机森林方法对每种标志物进行重要性排序，然后进行选择。使用选定的标志物，构建了多变量分类算法，并通过应用于 21 例 NSCLC 和 28 例对照的独立验证队列进行验证。

结果

除年龄外（患者为 64.8±10.0 岁，对照组为 53.0±7.6 岁），患者和对照组的人口统计学特征没有差异。在 30 种血清蛋白中，有 23 种在两组之间存在差异（患者组中 12 种增加，11 种减少）。当使用 5 种排名最高的生物标志物时，我们发现多变量分类算法的准确性最高（随机森林为 89.8%，支持向量机为 91.8%，线性判别分析为 88.2%，逻辑回归为 90.5%）。当我们将这些算法应用于验证队列时，每种方法都能以较高的准确率识别出患者和对照组（随机森林为 89.8%，支持向量机为 91.8%，线性判别分析为 88.2%，逻辑回归为 90.5%）。