Xiaolei Hao1, Zhengguang Guo2, Haidan Sun2, Xiaoyan Liu2, Yang Zhang1, Liwei Zhang1, Wei Sun3, Yongji Tian4. 1. Department of Pediatric Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases, Center of Brain Tumor, Beijing Institute for Brain Disorders, Beijing Key Laboratory of Brain Tumor, China. 2. Core Facility of Instruments, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, 5 Dong Dan San Tiao, Beijing 100005, China. 3. Core Facility of Instruments, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, 5 Dong Dan San Tiao, Beijing 100005, China. Electronic address: sunwei1018@sina.com. 4. Department of Pediatric Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases, Center of Brain Tumor, Beijing Institute for Brain Disorders, Beijing Key Laboratory of Brain Tumor, China. Electronic address: ttyysw1tyj@163.com.
Abstract
OBJECTIVE: To identify candidate urinary protein biomarkers to distinguish medulloblastoma (MB) patients from healthy patients or benign brain disease control patients. METHODS: The tandem mass tag (TMT)-labeled quantitative proteomics approach was used to identify differential proteins in the urinary proteome of 9 pre- and postsurgery MB patients and 9 healthy control patients, respectively. Ingenuity pathway analysis was used for functional annotation of differential proteins. The biomarker candidates were validated by the parallel reaction monitoring (PRM) method in 112 samples (29 pre- and postsurgery MB patients, 26 healthy control patients, and 28 benign brain disease control patients). Receiver operating characteristic (ROC) curves were developed to evaluate candidate biomarkers. RESULTS: A total of 114 differential proteins were found. Bioinformatic analysis revealed that the urinary proteome could reflect changes in MB. Seventeen candidate biomarkers were validated by PRM. The combination of CADH1, FGFR4 and FIBB could be used to discriminate MB patients from healthy control patients with an area under the curve (AUC) of 0.973, and the combination of CADH1 and FIBB showed good discriminative power for differentiating MB from benign brain disease with an AUC of 0.884. CONCLUSION: This report describes the first application of a TMT-PRM workflow to identify and validate MB-specific biomarkers in urine. These findings might contribute to the application of urinary proteomics for detecting and monitoring MB. BIOLOGICAL SIGNIFICANCE: Medulloblastoma (MB) is among the most common pediatric brain malignancies. This tumor has a highly aggressive clinical course with a high tendency for relapses. Magnetic resonance imaging (MRI) is the major means of diagnosis and for radiographic surveillance after surgery. In MRI, sedation is often required in young children, which could expose them to a series of risks, including airway obstruction and even death. Aside from MRI, there is no reliable biomarker for clinical screening or monitoring of the disease. These facts introduce the clinical need of noninvasive biomarkers for early screening or monitoring of MB. This study is focused on the investigation of a marker panel based on urinary proteome, as a tool for the detection of MB in selected patients at risk. Upon evaluation of the marker model in an independent blinded set of 112 samples, the panel (CADH1, FGFR4 and FIBB) could be used to discriminate MB patients from healthy control patients with an area under the curve (AUC) of 0.973, and the combination of CADH1 and FIBB showed good discriminative power for differentiating MB from benign brain disease with an AUC of 0.884.
OBJECTIVE: To identify candidate urinary protein biomarkers to distinguish medulloblastoma (MB) patients from healthy patients or benign brain disease control patients. METHODS: The tandem mass tag (TMT)-labeled quantitative proteomics approach was used to identify differential proteins in the urinary proteome of 9 pre- and postsurgery MB patients and 9 healthy control patients, respectively. Ingenuity pathway analysis was used for functional annotation of differential proteins. The biomarker candidates were validated by the parallel reaction monitoring (PRM) method in 112 samples (29 pre- and postsurgery MB patients, 26 healthy control patients, and 28 benign brain disease control patients). Receiver operating characteristic (ROC) curves were developed to evaluate candidate biomarkers. RESULTS: A total of 114 differential proteins were found. Bioinformatic analysis revealed that the urinary proteome could reflect changes in MB. Seventeen candidate biomarkers were validated by PRM. The combination of CADH1, FGFR4 and FIBB could be used to discriminate MB patients from healthy control patients with an area under the curve (AUC) of 0.973, and the combination of CADH1 and FIBB showed good discriminative power for differentiating MB from benign brain disease with an AUC of 0.884. CONCLUSION: This report describes the first application of a TMT-PRM workflow to identify and validate MB-specific biomarkers in urine. These findings might contribute to the application of urinary proteomics for detecting and monitoring MB. BIOLOGICAL SIGNIFICANCE: Medulloblastoma (MB) is among the most common pediatric brain malignancies. This tumor has a highly aggressive clinical course with a high tendency for relapses. Magnetic resonance imaging (MRI) is the major means of diagnosis and for radiographic surveillance after surgery. In MRI, sedation is often required in young children, which could expose them to a series of risks, including airway obstruction and even death. Aside from MRI, there is no reliable biomarker for clinical screening or monitoring of the disease. These facts introduce the clinical need of noninvasive biomarkers for early screening or monitoring of MB. This study is focused on the investigation of a marker panel based on urinary proteome, as a tool for the detection of MB in selected patients at risk. Upon evaluation of the marker model in an independent blinded set of 112 samples, the panel (CADH1, FGFR4 and FIBB) could be used to discriminate MB patients from healthy control patients with an area under the curve (AUC) of 0.973, and the combination of CADH1 and FIBB showed good discriminative power for differentiating MB from benign brain disease with an AUC of 0.884.