eISSN: 1897-4309
ISSN: 1428-2526
Contemporary Oncology/Współczesna Onkologia
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SCImago Journal & Country Rank
2/2014
vol. 18
 
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abstract:
Original paper

Mass spectrometric analysis of cerebrospinal fluid protein for glioma and its clinical application

Jian Liu
,
Jiekai Yu
,
Hong Shen
,
Jianmin Zhang
,
Weiguo Liu
,
Zhe Chen
,
Shuda He
,
Shu Zheng

Contemp Oncol (Pozn) 2014; 18 (2): 100–105
Online publish date: 2014/06/03
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Aim of the study: To establish and evaluate the fingerprint diagnostic models of cerebrospinal protein profile in glioma with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and bioinformatics analysis, in order to seek new tumor markers.

Material and methods: SELDI-TOF-MS was used to detect the cerebrospinal protein bond to ProteinChip H4. The cerebrospinal protein profiles were obtained and analyzed using the artificial neural network (ANN) method. Fingerprint diagnostic models of cerebrospinal protein profiles for distinguishing glioma from non-brain-tumor, and distinguishing glioma from benign brain tumor, were established. The support vector machine (SVM) algorithm was used for verification of established diagnostic models. The tumor markers were screened.

Results: In a fingerprint diagnostic model of cerebrospinal protein profiles for distinguishing glioma from non-brain tumor, the sensitivity and specificity of glioma diagnosis were 100% and 91.7%, respectively. Seven candidate tumor markers were obtained. In a fingerprint diagnostic model for distinguishing glioma from benign brain tumor, the sensitivity and specificity of glioma diagnosis were 88.9% and 100%, respectively, and 8 candidate tumor markers were gained.

Conclusions: The combination of SELDI-TOF-MS and bioinformatics tools is a very effective method for screening and identifying new markers of glioma. The established diagnostic models have provided a new way for clinical diagnosis of glioma, especially for qualitative diagnosis.
keywords:

glioma, cerebrospinal fluid, SELDI-TOF-MS, artificial neural network, support vector machine, diagnostic model, tumor markers

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