ABSTRACT
Objective: To establish and evaluate the fingerprint diagnostic models of cerebrospinal protein profile in glioma Using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and bioinformatics analysis, for seeking new tumor markers.
Methods: SELDI-TOF-MS was used to detect the cerebrospinal protein bond to ProteinChip H4. The cerebrospinal protein profiles were obtained and analyzed using artificial neural network. The fingerprint diagnostic models of cerebrospinal protein profiles for distinguishing glioma from non-brain-tumor, and distinguishing glioma from benign brain tumor were established, respectively. The support vector machine algorithm was used for verification of established diagnosis models. The tumor markers were screened.
Results: In 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. 7 candidate tumor markers were obtained. In 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 marker of glioma. The established diagnosis models have provided a new way for clinical diagnosis of glioma, especially for qualitative diagnosis.
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