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Discovery and identification of potential biomarkers of pediatric acute lymphoblastic leukemia.

Shi L, Zhang J, Wu P, Feng K, Li J, Xie Z, Xue P, Cai T, Cui Z, Chen X, Hou J, Zhang J, Yang F - Proteome Sci (2009)

Bottom Line: The sensitivity and specificity of the model were found to be 91.8%, and 90.0%, respectively, in the test set.Two candidate protein peaks (7769 and 9290 m/z) were found to be down-regulated in ALL patients, where these were identified as platelet factor 4 (PF4) and pro-platelet basic protein precursor (PBP).Two other candidate protein peaks (8137 and 8937 m/z) were found up-regulated in the sera of ALL patients, and these were identified as fragments of the complement component 3a (C3a).

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China. shiii@126.com

ABSTRACT

Background: Acute lymphoblastic leukemia (ALL) is a common form of cancer in children. Currently, bone marrow biopsy is used for diagnosis. Noninvasive biomarkers for the early diagnosis of pediatric ALL are urgently needed. The aim of this study was to discover potential protein biomarkers for pediatric ALL.

Methods: Ninety-four pediatric ALL patients and 84 controls were randomly divided into a "training" set (45 ALL patients, 34 healthy controls) and a test set (49 ALL patients, 30 healthy controls and 30 pediatric acute myeloid leukemia (AML) patients). Serum proteomic profiles were measured using surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy (SELDI-TOF-MS). A classification model was established by Biomarker Pattern Software (BPS). Candidate protein biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays.

Results: A total of 7 protein peaks (9290 m/z, 7769 m/z, 15110 m/z, 7564 m/z, 4469 m/z, 8937 m/z, 8137 m/z) were found with differential expression levels in the sera of pediatric ALL patients and controls using SELDI-TOF-MS and then analyzed by BPS to construct a classification model in the "training" set. The sensitivity and specificity of the model were found to be 91.8%, and 90.0%, respectively, in the test set. Two candidate protein peaks (7769 and 9290 m/z) were found to be down-regulated in ALL patients, where these were identified as platelet factor 4 (PF4) and pro-platelet basic protein precursor (PBP). Two other candidate protein peaks (8137 and 8937 m/z) were found up-regulated in the sera of ALL patients, and these were identified as fragments of the complement component 3a (C3a).

Conclusion: Platelet factor (PF4), connective tissue activating peptide III (CTAP-III) and two fragments of C3a may be potential protein biomarkers of pediatric ALL and used to distinguish pediatric ALL patients from healthy controls and pediatric AML patients. Further studies with additional populations or using pre-diagnostic sera are needed to confirm the importance of these findings as diagnostic markers of pediatric ALL.

No MeSH data available.


Related in: MedlinePlus

Representative mapping of SELDI-TOF-MS analysis of sera from healthy controls and pediatric ALL patients. Differentially expressed proteins with potential diagnostic significance are indicated by arrows. The top group denotes serum from a healthy volunteer, in which 7769 m/z and 9290 m/z were up-regulated. The bottom group denotes sera from patients with ALL, in which 8137 m/z and 8937 m/z were over-expressed.
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Figure 1: Representative mapping of SELDI-TOF-MS analysis of sera from healthy controls and pediatric ALL patients. Differentially expressed proteins with potential diagnostic significance are indicated by arrows. The top group denotes serum from a healthy volunteer, in which 7769 m/z and 9290 m/z were up-regulated. The bottom group denotes sera from patients with ALL, in which 8137 m/z and 8937 m/z were over-expressed.

Mentions: Serum samples from the training set were evaluated by comparing the results obtained by SELDI-TOF-MS with those from the WCX2 chip. All MS data had baseline subtracted and were normalized using total ion current. Peak clusters were then generated by Biomarker Wizard software. Twenty-six peaks had statistically significant differences between pediatric patients and healthy children (p value < 0.05). In the ALL group, five protein peaks were found to be up-regulated and twenty-one peaks were found to be down-regulated (Table 2). Figure 1 shows the protein profiling patterns of sera from ALL patients and control samples. The results indicated that two protein peaks (8137 m/z, p value 6.07E-05 and 8937 m/z, p value 5.13E-05) were up-regulated in sera from ALL patients, while two other peaks (7769 m/z, p value 1.54E-07 and 9290 m/z, p value 7.59E-08) were down-regulated, compared with those from the healthy controls.


Discovery and identification of potential biomarkers of pediatric acute lymphoblastic leukemia.

Shi L, Zhang J, Wu P, Feng K, Li J, Xie Z, Xue P, Cai T, Cui Z, Chen X, Hou J, Zhang J, Yang F - Proteome Sci (2009)

Representative mapping of SELDI-TOF-MS analysis of sera from healthy controls and pediatric ALL patients. Differentially expressed proteins with potential diagnostic significance are indicated by arrows. The top group denotes serum from a healthy volunteer, in which 7769 m/z and 9290 m/z were up-regulated. The bottom group denotes sera from patients with ALL, in which 8137 m/z and 8937 m/z were over-expressed.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2662805&req=5

Figure 1: Representative mapping of SELDI-TOF-MS analysis of sera from healthy controls and pediatric ALL patients. Differentially expressed proteins with potential diagnostic significance are indicated by arrows. The top group denotes serum from a healthy volunteer, in which 7769 m/z and 9290 m/z were up-regulated. The bottom group denotes sera from patients with ALL, in which 8137 m/z and 8937 m/z were over-expressed.
Mentions: Serum samples from the training set were evaluated by comparing the results obtained by SELDI-TOF-MS with those from the WCX2 chip. All MS data had baseline subtracted and were normalized using total ion current. Peak clusters were then generated by Biomarker Wizard software. Twenty-six peaks had statistically significant differences between pediatric patients and healthy children (p value < 0.05). In the ALL group, five protein peaks were found to be up-regulated and twenty-one peaks were found to be down-regulated (Table 2). Figure 1 shows the protein profiling patterns of sera from ALL patients and control samples. The results indicated that two protein peaks (8137 m/z, p value 6.07E-05 and 8937 m/z, p value 5.13E-05) were up-regulated in sera from ALL patients, while two other peaks (7769 m/z, p value 1.54E-07 and 9290 m/z, p value 7.59E-08) were down-regulated, compared with those from the healthy controls.

Bottom Line: The sensitivity and specificity of the model were found to be 91.8%, and 90.0%, respectively, in the test set.Two candidate protein peaks (7769 and 9290 m/z) were found to be down-regulated in ALL patients, where these were identified as platelet factor 4 (PF4) and pro-platelet basic protein precursor (PBP).Two other candidate protein peaks (8137 and 8937 m/z) were found up-regulated in the sera of ALL patients, and these were identified as fragments of the complement component 3a (C3a).

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China. shiii@126.com

ABSTRACT

Background: Acute lymphoblastic leukemia (ALL) is a common form of cancer in children. Currently, bone marrow biopsy is used for diagnosis. Noninvasive biomarkers for the early diagnosis of pediatric ALL are urgently needed. The aim of this study was to discover potential protein biomarkers for pediatric ALL.

Methods: Ninety-four pediatric ALL patients and 84 controls were randomly divided into a "training" set (45 ALL patients, 34 healthy controls) and a test set (49 ALL patients, 30 healthy controls and 30 pediatric acute myeloid leukemia (AML) patients). Serum proteomic profiles were measured using surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy (SELDI-TOF-MS). A classification model was established by Biomarker Pattern Software (BPS). Candidate protein biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays.

Results: A total of 7 protein peaks (9290 m/z, 7769 m/z, 15110 m/z, 7564 m/z, 4469 m/z, 8937 m/z, 8137 m/z) were found with differential expression levels in the sera of pediatric ALL patients and controls using SELDI-TOF-MS and then analyzed by BPS to construct a classification model in the "training" set. The sensitivity and specificity of the model were found to be 91.8%, and 90.0%, respectively, in the test set. Two candidate protein peaks (7769 and 9290 m/z) were found to be down-regulated in ALL patients, where these were identified as platelet factor 4 (PF4) and pro-platelet basic protein precursor (PBP). Two other candidate protein peaks (8137 and 8937 m/z) were found up-regulated in the sera of ALL patients, and these were identified as fragments of the complement component 3a (C3a).

Conclusion: Platelet factor (PF4), connective tissue activating peptide III (CTAP-III) and two fragments of C3a may be potential protein biomarkers of pediatric ALL and used to distinguish pediatric ALL patients from healthy controls and pediatric AML patients. Further studies with additional populations or using pre-diagnostic sera are needed to confirm the importance of these findings as diagnostic markers of pediatric ALL.

No MeSH data available.


Related in: MedlinePlus