SELDI-TOF MS Research Articles

A combined biomarker pattern improves the discrimination of lung cancer

A total of 227 sera were analysed using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) to find novel serum biomarkers for lung cancer. The results showed that the 11.53, 11.70, 13.78, 13.90 and 14.07 k m/z peaks identified as native serum amyloid A (SAA), SAA with N-terminal Arg cleaved, native transthyretin (TTR) and its two variants significantly differentiated lung cancer sera from normal control sera (p <0.01). A ‘biomarker pattern’ combining SAA and TTR was tested to distinguish lung cancer patients from normal control individuals, and the diagnostic positive rate of lung cancer was improved to 91.6%.

Incubation of Denatured Samples Increased Reproducibility and Quality of Proteomic Profile of SELDI-TOF MS

Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 Protein profiling with high-throughput proteomic technology, SELDI-TOF, is a new potential tool for diagnosis of human diseases. This advanced technique has increasingly been used for the detection of disease biomarker. However, analytical reproducibility is a significant challenge in SELDI-TOF profiling in order to have confidence in the results. &nbsp; Here, we showed a simple step to improve its analytical performance. Incubation of denaturated samples overnight at 4 o C increased significantly reproducibility and quality of proteomic profile of SELDI-TOF MS for IMAC30-Cu ProteinChip. This strategy could be concerned and apply to address reproducibility issue in this system. &nbsp;

Application of serum protein fingerprint in diagnosis of coronary artery disease

Objectives The object of this study is to establish serum protein fingerprint models for early diagnosis and evaluation of luminal stenosis severity of CAD. Design and methods A total of 189 samples were analyzed by surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS) coupled with CM10 chips and bioinformatics tools. Results Ten top-ranked proteins were selected as the potential fingerprints of pattern 1, which distinguishes patients with significant CAD (stenosis ≥ 50%) from control individuals. Blind test was analyzed on the second day with a specificity of 79.3% and sensitivity of 78.3%. Pattern 2 distinguishes significant CAD from minimal CAD with luminal stenosis between 25% and 50%. The specificity and sensitivity of blind test for pattern 2 are 77.3% and 73.9%, respectively. Conclusions The SELDI-TOF MS technique combined with bioinformatics approaches may be a novel noninvasive method which can not only facilitate the discovery of diagnostic models for CAD and its severity, but also provide a useful tool for molecular diagnosis.

Using SELDI-TOF MS to identify serum biomarkers in NSCLC

Abstract Non-small cell lung cancer (NSCLC) is the leading cause of cancer mortality worldwide with poor differential diagnosis of the disease and with low response rates to standard chemotherapy. Individuals classified as Stage I by the present clinical-based scoring system may display survival characteristics ranging from 10 to 90 %. Identification of reliable biomarkers to detect NSCLC at early stages is therefore a subject of extensive research. Surface-enhanced laser-desorption-ionization time-of-flight mass spectrophotometry (SELDI-TOF-MS) combines chromatographic surfaces with solid-state TOF-MS for the high throughput analysis of proteins in diverse clinical samples such as serum, urine, cerebrospinal fluids and has overcome the limitations of earlier proteomic approaches. The method has been widely used for biomarker discovery. Therefore, in this study we used the SELDI-TOF protein Chip system to achieve a stratification based on the protein expression profile and to identify differentially expressed proteins in sera from 50 patients with NSCLC and 50 healthy controls. IMAC 30-Cu protein chips were used to fractionate the proteins according to their metal binding affinity. The arrays were analyzed using the Ciphergen Protein Chip Reader PB IIa. The spectra were normalized to the total ion current of the mass to charge between 3000 — 10 000 Da. Protein peak clustering and classification analysis were performed using the Express 3.0 Biomarker Pattern Analysis Software. Several proteins with masses ranging from 2000-20000 Da were differentially expressed between NSCLC patients and healthy controls. Among the peak clusters revealing differences we selected two specific clusters as prospective candidate biomarkers in sera of the patients. High expression of one peak and underexpression of the other with respect to the healthy control sera were evaluated in a blind test in terms of the ability to classify a separate group of patients and controls.

Immunochemotherapy-associated protein patterns in tumour tissue and serum of patients with metastatic renal cell carcinoma

Context: Systemic treatment of metastatic renal cell carcinoma (mRCC) with targeted therapies became widely accepted; however, there are few patients who greatly benefit from immunochemotherapy (ICT). It is crucial to recognize these patients for individual treatment.Objectives: Definition of protein patterns in tissue and serum from mRCC-patients to predict benefit from ICT.Materials and methods: Twenty-five tissue samples and 59 sera were analysed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Protein peaks of interest were identified by 2D-PAGE and peptide mass fingerprinting. Validation was carried out by Western Blot and ELISA.Results: Protein patterns associated with therapy response were determined. Caveolin-1 (CAV-1) and plasminogen activator inhibitor 1 (PAI-1) were identified in tissue; serum amyloid A (SAA) and transthyretin (TTR) were found in serum.Conclusion: Individual prediction of therapy benefit and selecting patients for ICT based on molecular biological profiles appear to be feasible in the future.

Targeting phosphoprotein profiling by combination of hydroxyapatite-based phosphoprotein enrichment and SELDI-TOF MS

Over the last decade surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) has evolved as a versatile tool in the field of proteomic research. Although the chromatographic matrices on the currently provided SELDI affinity arrays allow efficient on-chip protein enrichment, it can be advantageous to combine SELDI with additional sample pre-fractionation steps. In this study, we demonstrate the potential of combining hydroxyapatite-based phosphoprotein enrichment with SELDI-TOF MS analysis. A straightforward method for the enrichment of phosphoproteins on ceramic hydroxyapatite was developed using fluorescently-labelled model proteins. Hydroxyapatite-based pre-fractionation of proteins derived from cell lysates was performed. SELDI-TOF MS analysis of the pre-fractionation eluate confirmed a considerable reduction of sample complexity and an enhancement of selected protein signals.

Proteomic profiling of cerebrospinal fluid in parkinsonian disorders

Parkinson’s disease (PD) and atypical parkinsonian disorders (APD), including multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD), are a group of neurodegenerative diseases sharing many similar signs and symptoms but distinguished by their particular clinical features, treatment response, prognosis and mortality. The differential diagnosis may be challenging, especially in early disease stages. Considering the importance of an accurate diagnosis both for clinical management and for research, new diagnostic tools are needed. In this study, we investigated 56 PD, 42 MSA, 39 PSP, 9 CBD patients, and 24 healthy controls. After screening the cerebrospinal fluid (CSF) proteome using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS), we identified 4 proteins (ubiquitin [mass-to-charge ratio ( m/ z) 8590], β2-microglobulin [ m/ z 11730], and 2 secretogranin 1 [chromogranin B] fragments [ m/ z 7260 and m/ z 6250]) that differentiated healthy controls and PD patients from patients with APD. However, they could not differentiate PD patients from controls. As none of these changes were APD subgroup-specific, they most likely reflect the intensity and/or extent of the neurodegenerative process in general.

Proteomic Identification of Early Biomarkers of Acute Kidney Injury After Cardiac Surgery in Children

Serum creatinine is a delayed marker of acute kidney injury (AKI). Our purpose is to discover and validate novel early urinary biomarkers of AKI after cardiac surgery. Diagnostic test study. Children undergoing cardiopulmonary bypass surgery. The test set included 15 participants with AKI and 15 matched controls (median age, 1.5 year) of 45 participants without AKI. The validation set included 365 children (median age, 1.9 year). Biomarkers identified using proteomic profiling: α(1)-microglobulin, α(1)-acid glycoprotein, and albumin. AKI, defined as ≥50% increase in serum creatinine level from baseline within 3 days of surgery. Proteomic profiling using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) showed 3 protein peaks that appeared consistently within 2 hours in children who developed AKI after cardiopulmonary bypass surgery. The proteins were identified as α(1)-microglobulin, α(1)-acid glycoprotein, and albumin. Using clinical assays, results were confirmed in a test set and validated in an independent prospective cohort. In the validation set, 135 (37%) developed AKI, in whom there was a progressive increase in urinary biomarker concentrations with severity of AKI. Areas under the curve for urinary α(1)-microglobulin, α(1)-acid glycoprotein, and albumin at 6 hours after cardiac surgery were 0.84 (95% CI, 0.79-0.89), 0.87 (95% CI, 0.83-0.91), and 0.76 (95% CI, 0.71-0.81), respectively. Participants with increasing quartiles of biomarkers showed increasing lengths of hospital stays and durations of AKI (P < 0.001). Single-center study of children with normal kidney function at recruitment. The SELDI-TOF MS technique has limited sensitivity for the detection of proteins greater than the 20-kDa range. Urinary α(1)-microglobulin, α(1)-acid glycoprotein, and albumin represent early, accurate, inexpensive, and widely available biomarkers of AKI after cardiac surgery. They also offer prognostic information about the duration of AKI and length of hospitalization after cardiac surgery.

Two-protein signature of novel serological markers apolipoprotein-A2 and serum amyloid alpha predicts prognosis in patients with metastatic renal cell cancer and improves the currently used prognostic survival models

BackgroundIn metastatic renal cell cancer (mRCC), the Memorial Sloan-Kettering Cancer Center (MSKCC) risk model is widely used for clinical trial design and patient management. To improve prognostication, we applied proteomics to identify novel serological proteins associated with overall survival (OS). Patients and methodsSera from 114 mRCC patients were screened by surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS). Identified proteins were related to OS. Three proteins were subsequently validated with enzyme-linked immunosorbent assays and immunoturbidimetry. Prognostic models were statistically bootstrapped to correct for overestimation. ResultsSELDI-TOF MS detected 10 proteins associated with OS. Of these, apolipoprotein A2 (ApoA2), serum amyloid alpha (SAA) and transthyretin were validated for their association with OS (P = 5.5 × 10-9, P = 1.1 × 10-7 and P = 0.0004, respectively). Combining ApoA2 and SAA yielded a prognostic two-protein signature [Akaike's Information Criteria (AIC) = 732, P = 5.2 × 10-7]. Including previously identified prognostic factors, multivariable Cox regression analysis revealed ApoA2, SAA, lactate dehydrogenase, performance status and number of metastasis sites as independent factors for survival. Using these five factors, categorization of patients into three risk groups generated a novel protein-based model predicting patient prognosis (AIC = 713, P = 4.3 × 10-11) more robustly than the MSKCC model (AIC = 729, P = 1.3 × 10-7). Applying this protein-based model instead of the MSKCC model would have changed the risk group in 38% of the patients. ConclusionsProteomics and subsequent validation yielded two novel prognostic markers and survival models which improved prediction of OS in mRCC patients over commonly used risk models. Implementation of these models has the potential to improve current risk stratification, although prospective validation will still be necessary.

Probing the dynamic nature of signalling pathways by IMAC and SELDI-tof MS

One major obstacle to the analysis of signalling pathways is the dynamic nature of signalling response to environmental stimuli. To overcome this limitation we applied immobilized metal affinity chromatography (IMAC) in combination with SELDI-tof MS to investigate the temporal variation of protein phosphorylation. We analysed the phospho-proteome variations in our model organism, Leishmania donovani, in response to changes in pH and temperature, which induce differentiation from promastigotes to amastigotes. Investigation of total cell extracts did not allow promastigote and amastigote life cycle stages to be distinguished. However, using IMAC enriched samples, the pattern and intensity of phospho-proteins analysed distinguished both stages reproducibly. Approximately 61% of the phospho-proteins analysed were significantly different in abundance (p<0.02). Of these 61%, 73% showed an increased phosphorylation in promastigotes while 27% showed an increase phosphorylation in amastigotes. The workflow developed is currently being applied to the temporal analysis of environmental stimuli.

Discovery of serum biomarkers for pancreatic adenocarcinoma using proteomic analysis

Background and aims:The serum/plasma proteome was explored for biomarkers to improve the diagnostic ability of CA19-9 in pancreatic adenocarcinoma (PC).Methods:A Training Set of serum samples from 20 resectable and 18 stage IV PC patients, 54 disease controls (DCs) and 68 healthy volunteers (HVs) were analysed by surface-enhanced laser desorption and ionisation time-of-flight mass spectrometry (SELDI-TOF MS). The resulting protein panel was validated on 40 resectable PC, 21 DC and 19 HV plasma samples (Validation-1 Set) and further by ELISA on 33 resectable PC, 28 DC and 18 HV serum samples (Validation-2 Set). Diagnostic panels were derived using binary logistic regression incorporating internal cross-validation followed by receiver operating characteristic (ROC) analysis.Results:A seven-protein panel from the training set PC vs DC and from PC vs HV samples gave the ROC area under the curve (AUC) of 0.90 and 0.90 compared with 0.87 and 0.91 for CA19-9. The AUC was greater (0.97 and 0.99, P<0.05) when CA19-9 was added to the panels and confirmed on the validation-1 samples. A simplified panel of apolipoprotein C-I (ApoC-I), apolipoprotein A-II (ApoA-II) and CA19-9 was tested on the validation-2 set by ELISA, in which the ROC AUC was greater than that of CA19-9 alone for PC vs DC (0.90 vs 0.84) and for PC vs HV (0.96 vs 0.90).Conclusions:A simplified diagnostic panel of CA19-9, ApoC-I and ApoA-II improves the diagnostic ability of CA19-9 alone and may have clinical utility.

An early pregnancy urinary proteomic fingerprint accurately predicts later pre-eclampsia

BackgroundPre-eclampsia affects approximately 5% of pregnancies and is a leading cause of maternal and fetal morbidity and mortality worldwide. Given that placentation is complete by 18-week gestation and proteinuria is...

Identification of a 17-protein signature in the serum of lung cancer patients

Early detection of lung cancer may potentially help to improve the outcome of this fatal disease. Currently, no satisfactory laboratory tests are available to screen for this type of cancer. The aim of this study was to improve diagnostic procedures for lung cancer through the discovery of serum biomarkers using SELDI-TOF MS (surface-enhanced laser desorption/ionization time-of-flight mass spectrometry). Mass spectrometric profiling was applied to the serum of a total of 139 lung cancer patients and 158 healthy individuals for developing a prognostic signature. For validation, two separate groups were employed, comprising of 126 and 50 individuals, respectively. Informative regions of mass spectra were identified by forming protein mass clusters and identifying predictive clusters in a logistic regression model. A total of 17 differential predictive protein mass clusters were identified in patients with metastatic lung cancer disease compared to healthy individuals. These clusters provide for a robust risk prediction model. The sensitivity and specificity of this model was estimated to be 87.3 and 81.9%, respectively, for the first validation set, and 96.0 and 66.7%, respectively, for a second validation set of patients with early disease (stages I and II). A differential 11.5/11.7 kDa double-peak could be identified as serum amyloid alpha (SAA) by peptide mapping. Our data suggest that the SELDI-TOF MS technology in combination with a careful statistical analysis appears to be a useful experimental platform which delivers a rapid insight into the proteome of body fluids and may guide to identify novel biomarkers for lung cancer disease.

TKI therapy-related proteomic patterns in serum from patients with metastatic renal cell carcinoma.

e15090 Background: Development of targeted therapies including tyrosine kinase inhibitors (TKIs) established new therapeutical options in patients with metastatic renal cell carcinoma. However, the primary clinical response, development of resistance as well as serious side effects are different in individual patients. Currently, no predictive biomarkers exist for these treatment options. Therefore, our aim was to define protein patterns in serum of patients treated by TKIs correlating with therapy benefit. Methods: Thirty patients who received TKI therapy (sorafenib or sunitinib) were included in our study. Clinical response and follow-up data were known in all cases. Data were analyzed considering best response after 3 month and response or progression after 9 month for the respective therapy. To establish biomarkers pre-therapeutic samples were analyzed by Surface-enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF MS) in combination with the ProteinChip Arrays Q10, CM10 and IMAC30. Bioinformatic analysis was performed using the Fuzzy c- means method for clustering and the relevance index by Kiendl. Results: For both TKI treatments it was possible to define specific protein patterns in serum which can be related to clinical response and development of drug-resistance. Using bioinformatics, candidate protein peaks were identified which are highly relevant for prediction of therapy response. Conclusions: From our results it seems possible to predict therapy response analyzing serum samples obtained before targeted therapy. In this ungoing study, candidate proteins have to be identified to recognize their role in therapy response and development of resistance. Author Disclosure Employment or Leadership Position Consultant or Advisory Role Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Bayer, Wyeth

Detection of Hen Lysozyme on Proteic Profiles of Grana Padano Cheese through SELDI-TOF MS High-Throughput Technology during the Ripening Process

Lysozyme is used in cheese manufacture in order to prevent blowing in cheeses caused by Clostridium tyrobutyricum. Being an egg derivative, the presence of lysozyme must be included on the label for residual allergenic risk (2003/89/CE). The aim of this study was to evaluate the presence of lysozyme on proteic profiles of typical Italian cheeses such as Grana Padano through surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. The proteolytic activity of ripening (from 0 to 24 months), confirmed by a decrease in casein, did not influence the intensity of lysozyme peaks. Furthermore, ripened Grana Padano cheese could be differentiated on mass profiling from immature Grana Padano by the presence of particular signals that are probably related to casein proteolysis.

Changes in Protein Expression Profiles between a Low Phytic Acid Rice (Oryza sativa L. Ssp. japonica) Line and Its Parental Line: A Proteomic and Bioinformatic Approach

The seed proteome of a low phytic acid (lpa) rice line (Os-lpa-XS110-1), developed as a novel food source, was compared to that of its parental line, Xiushui 110 (XS-110). Analysis by surfaced enhanced laser desorption ionization-time-of-flight mass spectrometry (SELDI-TOF MS) and two-dimensional gel electrophoresis (2-DE) allowed the detection of a potential low molecular weight biomarker and identification of 23 differentially expressed proteins that include stress-related proteins, storage proteins, and potential allergens. Bioinformatic analyses revealed that triose phosphate isomerase (TPI) and fructose bisphosphatealdolase (FBA), two major differentially expressed proteins, are involved in myo-inositol metabolism. Accumulation of globulin was also significantly decreased in the lpa line. This study demonstrates the potential of proteomic and bioinformatic profiling techniques for safety assessment of novel foods. Furthermore, these techniques provide powerful tools for studying functional genomics due to the possibility of identifying genes related to the mutated traits.

Protein profiling in pathology: Analysis and evaluation of 239 frozen tissue biopsies for diagnosis of B‐cell lymphomas

We determined the potential value of protein profiling of tissue samples by assessing how precise this approach enables discrimination of B-cell lymphoma from reactive lymph nodes, and how well the profiles can be used for lymphoma classification. Protein lysates from lymph nodes (n=239) from patients with the diagnosis of reactive hyperplasia (n=44), follicular lymphoma (n=63), diffuse large B-cell lymphoma (n=43), mantle cell lymphoma (n=47), and chronic lymphocytic leukemia/small lymphocytic B-cell lymphoma (n=42) were analysed by SELDI-TOF MS. Data analysis was performed by (i) classification and regression tree-based analysis and (ii) binary and polytomous logistic regression analysis. After internal validation by the leave-one-out principle, both the classification and regression tree and logistic regression classification correctly identified the majority of the malignant (87 and 96%, respectively) and benign cases (73 and 75%, respectively). Classification was less successful since approximately one-third of the cases of each group were misclassified according to the histological classification. However, an additional mantle cell lymphoma case that was misclassified as chronic lymphocytic leukemia/small lymphocytic B-cell lymphoma initially was identified based on the protein profile. SELDI-TOF MS protein profiling allows for reliable identification of the majority of malignant lymphoma cases; however, further validation and testing robustness in a diagnostic setting is needed.

ULDA-based heuristic feature selection method for proteomic profile analysis and biomarker discovery

Uncorrelated linear discriminant analysis (ULDA)-based heuristic feature selection (ULDA-HFS) method was proposed for sample classification and feature extraction for SELDI-TOF MS ovarian cancer data. The ULDA-HFS method includes 4 steps: (1) noise reduction and normalization; (2) selection of discriminatory bins with CHI2 method; (3) peak detection and alignment for each selected bins; and (4) selection of several peaks as potential biomarkers by means of ULDA. As a result, 7 m/ z locations were selected in this study; they were 245.3, 559.4, 565.6, 704.2, 717.2, 2667 and 4074.4. To evaluate the classification impression, PCA, PLS-DA and ULDA were performed for discriminant analysis and ULDA obtained the perfect separation. Finally, the 7 selected potential biomarkers were evaluated by ULDA, both sensitivity and specificity were 100%. The 7 m/ z values obtained may provide clues for ovarian cancer biomarker discovery. Once the proteins were identified at these m/ z locations, it can be used as specific protein for early detection and diagnosis for ovarian cancer.

Apolipoprotein A-I and serum amyloid A plasma levels are biomarkers of acute painful episodes in patients with sickle cell disease

Acute painful episodes are the clinical hallmark of sickle cell disease and have been linked to morbidity and mortality in the sickle cell population. We undertook exploratory proteomic studies on paired plasma samples collected from a cohort of 26 adult sickle cell patients during steady state and on the first day of an acute painful episode. We screened for changes in abundance of specific protein peaks via surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS), and confirmed the identify of candidate protein peaks by specific immunoassays. The levels of hemoglobin, hematocrit, total protein, and albumin were lower and the levels of lactate dehydrogenase and absolute reticulocytes higher during acute painful episodes than during the steady state. Surface-enhanced laser desorption/ionization time of flight mass spectrometry spectral analysis consistently showed a mass-to-charge peak at 11.7 kDa with elevated intensities during acute painful episodes, which correlated significantly with the serum amyloid A immunoassay. Serum amyloid A levels were significantly elevated during acute painful episodes, especially in four patients with marked end-organ complications of such episodes. A second, recurring peak, less abundant during acute painful episodes, was present at 28.1 kDa; this peak was correlated significantly with immunoassay measurements of apolipoprotein A1. On the average, plasma serum amyloid A rises and apolipoprotein AI falls during acute painful episodes. The serum amyloid A/apolipoprotein AI ratio increased in 81% of the patients during acute painful episodes, potentially making it a useful objective marker of such episodes. We propose that these protein alterations, known to contribute to endothelial dysfunction in other settings, might do likewise acutely in acute painful episodes and present a new target for therapeutic intervention in sickle cell disease. (ClincalTrials.gov Identifier: NCT00081523).

Effect of amygdalin on serum proteinic biomarker in pulmonary fibrosis of bleomycin-induced rat

To evaluate effect of amygdalin on expression of four biomarkers in the animal model of pulmonary fibrosis induced by bleomycin. Rats were given one dose (5 mg/kg) of bleomycin in bleomycin-treated groups, amygdalin-treated groups and saline in controls by intratracheal instillation exposed surgically. The amygdalin-treated groups rats were treated with intraperitoneal injection of amygdalin (15 mg x kg(-1) x day(-1)). The rats were sacrificed 7, 14 and 28 days after bleomycin administration. Polarized light microscopy and Image-Pro Plus detected I and III collagen expressed in Paraffin-embedded lung sections stained with Sirius red. Surface-enhanced laser desorption-ionization time-of-flight mass spectrometry (SELDI-TOF MS) with weak cationic proteinchip (CM10) detected differentially expressed proteins in the pooled serum samples of all groups. Consistent fibrotic responses were found in all bleomycin and amygdalin-tread groups. On the 7th, 14th and 28th day after bleomycin or saline instillation, four differentially expressed proteins were detected in the pooled serum of all groups rats, consisting of 4 proteins with mass/charge ratio of 3530.7, 7043.5, 8332.6 and 9068.0, respectively. Compared with control groups, protein peaks intensity ratio with mass/charge ratio of 3530.7 on 7, 28 d and 7043.5, 8332.6 and 9068.0 on 7, 14 and 28 d was > 2 in bleomycin-treated groups. Compared with amygdalin-treated groups, protein peaks intensity with mass/charge ratio of 3530.7 at 7, 14, 28 d had no change almost, but protein peaks intensity ratio with mass/charge ratio of 7043.5 at 7 d, 8332.6 on 28 d and 9068.0 on 14 d was > 2 in bleomycin-tread groups. All the four protein peaks intensity had no change almost at other point. Amygdalin may reduce the bleomycin-induced increase of differentially expressed protein peak intensities in rat serum.