Searching for serum protein biomarkers of breast cancer patients using MALDI-TOF-MS and magnetic beads technology

doi:10.3877/cma. j. issn.1674-0807.2012.02.002

Abstract

Abstract:

Objective

To explore the different expressions of proteins in serum among patients with breast cancer, benign mammary disease and healthy people and find out potential serum biomarkers differentiating breast cancer from benign mammary disease and healthy people.

Methods

This study included two experiment groups, the decision tree model group with three subgroups including 110 cases of breast cancer,113 cases of benign mammary disease and 70 healthy controls to build breast cancer diagnosis model, and the blind test group with three subgroups including 7 cases of breast cancer,13 cases of benign mammary disease and 14 healthy controls to test the sensitivity and specificity of the decision tree model. The serum proteins were captured using the weak cation magnetic beads, and differently expressed proteins were identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Biomarker Wizard TM Software 3.1 and Biomarker Patterns TM Software (BPS) 5.0 were used to analyze the data. Variance analysis or rank sum test was applied for statistical analysis. The accuracy rate of the decision tree model and the sensitivity and specificity of the model tested by blind test were calculated.

Results

A total of 47 statistically different protein peaks (P＜0.05)were tested in the decision tree model group. Based on the principle of least relative loss, four protein peaks with the relative molecular mass (M_r, equal to m/z) of 9292.54,11707.2,15504.5 and 16107.9 were selected from the 47 protein peaks, and they used to construct the decision tree model for diagnosis of breast cancer using BPS 5.0. The accuracy rate of the decision tree identifying breast cancer, benign mammary disease and healthy people was 99.09%,95.58% and 92.86%,respectively. The blind test showed that the sensitivity and specificity of the decision tree diagnosing breast cancer was 71.43% and 88.89%,respectively.

Conclusions

Using the technique of MALDI-TOF MS combined with magnetic beads, different serum protein peaks in breast cancer can be detected. The decision tree model constructed with the four potential biomarkers has good accuracy and better sensitivity and specificity of diagnosing breast cancer. The decision tree model can identify breast cancer from not only benign breast disease but also healthy person. The four protein peaks of M_r 9292.54, M_r 11707.2, M_r 15504.5 and M_r 16107.9 selected are promising serum protein biomarkers for breast cancer.

Key words: breast neoplasms, proteomics, matrix-assisted laser desorption ionization time of-flight mass spectrometry, magnetic beads, protein peak, decision tree model

Xin HUANG, Ya-li XU, Li PENG, Yi-dong ZHOU, Feng MAO, Jing-hong GUAN, Yan LIN, Qiang SUN. Searching for serum protein biomarkers of breast cancer patients using MALDI-TOF-MS and magnetic beads technology[J]. Chinese Journal of Breast Disease(Electronic Edition), 2012, 06(02): 125-139.

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URL: https://zhrxbzz.cma-cmc.com.cn/EN/10.3877/cma. j. issn.1674-0807.2012.02.002

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Figures/Tables 8

分组	4个蛋白峰的浓度值( $\bar{x}$ ±s)
分组	M_r9292.5	M_r11707.2	M_r15504.5	M_r16107.9
乳腺癌组	27.2514±14.0391	5.0502±4.3075	0.3321±0.4461	15.4986±14.7589
乳腺良性疾病组	15.9320±11.0348	2.1630±1.4015	0.1729±0.2187	6.0756±5.3795
健康组	15.9320±11.0348	3.6622±2.2818	0.1414±0.1324	6.2325±4.5782
统计量	31.986	25.912	10.563	30.723
P值	0.000^a	0.000^a	0.000^b	0.000^a

分组	4个蛋白峰的浓度值( $\bar{x}$ ±s)
分组	M_r9292.5	M_r11707.2	M_r15504.5	M_r16107.9
乳腺癌组	27.2514±14.0391	5.0502±4.3075	0.3321±0.4461	15.4986±14.7589
乳腺良性疾病组	15.9320±11.0348	2.1630±1.4015	0.1729±0.2187	6.0756±5.3795
健康组	15.9320±11.0348	3.6622±2.2818	0.1414±0.1324	6.2325±4.5782
统计量	31.986	25.912	10.563	30.723
P值	0.000^a	0.000^a	0.000^b	0.000^a

实验分组	病理结果	决策树模型结果	准确性(%)	敏感性(%)	特异性(%)
决策树模型组
CA	110	109	99.09
DC	113	108	95.58
HC	70	65	92.86
盲法验证组
CA	7	5		71.43	88.89
DC	13	10
HC	14	14

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