MALDI-TOF MS combined with magnetic beads for detecting serum protein biomarkers and establishment of boosting decision tree model for diagnosis of colorectal cancer

MALDI-TOF-MS analysis in low molecular weight serum peptidome biomarkers for NSCLC

Objects

Lung cancer is one of the leading causes of death from cancer in the world. Screening new serum biomarkers is important for the early detection of lung cancer. The purpose of this study was to investigate the serum peptide model between non‐small cell lung cancer (NSCLC) patients and healthy controls, as well as between paired pre‐ and postoperative NSCLC patients, and to find the low molecular weight (LMW) potential tumor markers for NSCLC.

Methods

56 serum samples from NSCLC patients, 56 controls, and 20 matched pre‐ and postoperative patients were analyzed using magnetic‐bead (MB)‐based purification technique combined with MALDI‐TOF‐MS. To distinguish NSCLC from cancer‐free controls, three models were established. Finally, comparing the three groups of serum protein fingerprints, nano‐liquid chromatography–electrospray ionization tandem mass spectrometry was used to further identify the differential peptides.

Results

Among the three models constructed, the GA model had the best diagnostic efficacy. Five differential peaks were screened by combining the case group, healthy controls, and postoperative group analysis, which were up‐regulated in the case group and showed a tendency to return to healthy control values after surgery. The protein matching the mass spectrometry peak m/z 2953.73 was identified as fibrinogen α chain.

Conclusion

This study shows that the application of MALDI‐TOF‐MS is a promising approach for the identification of potential serum biomarkers for NSCLC, which is potentially valuable for establishing a new diagnostic method for lung cancer. In addition, we found that fibrinogen α chain may be an auxiliary diagnostic indicator for NSCLC.

A Meta-Analysis of Proteomic Blood Markers of Colorectal Cancer

BACKGROUND

Early diagnosis will significantly improve the survival rate of colorectal cancer (CRC); however, the existing methods for CRC screening were either invasive or inefficient. There is an emergency need for novel markers in CRC's early diagnosis. Serum proteomics has gained great potential in discovering novel markers, providing markers that reflect the early stage of cancer and prognosis prediction of CRC. In this paper, the results of proteomics of CRC studies were summarized through a meta-analysis in order to obtain the diagnostic efficiency of novel markers.

METHODS

A systematic search on bibliographic databases was performed to collect the studies that explore blood-based markers for CRC applying proteomics. The detection and validation methods, as well as the specificity and sensitivity of the biomarkers in these studies, were evaluated. Newcastle- Ottawa Scale (NOS) case-control studies version was used for quality assessment of included studies.

RESULTS

Thirty-four studies were selected from 751 studies, in which markers detected by proteomics were summarized. In total, fifty-nine proteins were classified according to their biological function. The sensitivity, specificity, or AUC varied among these markers. Among them, Mammalian STE20-like protein kinase 1/ Serine threonine kinase 4 (MST1/STK4), S100 calcium-binding protein A9 (S100A9), and Tissue inhibitor of metalloproteinases 1 (TIMP1) were suitable for effect sizes merging, and their diagnostic efficiencies were recalculated after merging. MST1/STK4 obtained a sensitivity of 68% and a specificity of 78%. S100A9 achieved a sensitivity of 72%, a specificity of 83%, and an AUC of 0.88. TIMP1 obtained a sensitivity of 42%, a specificity of 88%, and an AUC of 0.71.

CONCLUSION

MST1/STK4, S100A9, and TIMP1 showed excellent performance for CRC detection. Several other markers also presented optimized diagnostic efficacy for CRC early detection, but further verification is still needed before they are suitable for clinical use. The discovering of more efficient markers will benefit CRC treatment.

Identification and Validation of Novel Serum Autoantibody Biomarkers for Early Detection of Colorectal Cancer and Advanced Adenoma

Background: Colorectal cancer (CRC) comprises a large proportion of malignant tumors, and early detection of CRC is critical for effective treatment and optimal prognosis. We aimed to discover and validate serum autoantibodies for early detection of CRC. Methods: Combined with CRC-associated autoantibodies discovered by serological proteome and multiplex analyses, 26 predefined autoantibodies were evaluated in 315 samples (130 CRCs, 75 advanced adenomas, and 110 healthy controls) by protein microarray analysis. Autoantibodies with potential detection value were verified by enzyme-linked immunosorbent assays (ELISAs). Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the accuracy of the biomarkers. Results: Four serum autoantibodies (ALDH1B1, UQCRC1, CTAG1, and CENPF) showed statistically different levels between patients with advanced neoplasm (CRC or advanced adenoma) and controls in microarray analysis, which were validated by ELISAs. Among the four biomarkers, the ALDH1B1 autoantibody showed the highest detection value with area under the curve (AUC) values of 0.70 and 0.74 to detect CRC and advanced adenoma with sensitivities of 75.68 and 62.31% and specificities of 63.06 and 73.87%, respectively. By combining the four biomarkers, the performance was improved with an AUC of 0.79 to detect CRC and advanced adenomas. Conclusion: The ALDH1B1 autoantibody has a good potential for early detection of CRC and advanced adenoma, and measuring serum autoantibodies against tumor-associated antigens may improve detection of early CRC.

Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Breast Cancer Survivors

Due to the high effectiveness of cancer screening and therapies, the diagnosis of second primary cancers (SPCs) has increased in women with breast cancer. The present study was conducted to develop a novel machine learning-based classification scheme for predicting the risk factors of SPCs in breast cancer survivors. The proposed scheme was based on the XGBoost classifier with the following four comparable strategies: transformation, resampling, clustering, and ensemble learning, to improve the training balanced accuracy. Results suggested that the best prediction accuracy for an empirical case is the XGBoost associated with the strategies of resampling and clustering. The experimental results showed that age, sequence of radiotherapy and surgery, surgical margins of the primary site, human epidermal growth factor, high-dose clinical target volume, and estrogen receptors are relatively more important risk factors associated with SPCs in patients with breast cancer. These risk factors should be monitored for the early detection of breast cancer. In conclusion, the proposed scheme can support the important influence of personality and clinical symptom representations in all phases of the primary treatment trajectory. Our results further suggested that adaptive machine learning techniques require the incorporation of significant variables for optimal predictions.

Advances in liquid biopsy on-chip for cancer management: Technologies, biomarkers, and clinical analysis

Liquid biopsy, as a minimally invasive method of gleaning insight into the dynamics of diseases through a patient fluid sample, has been growing in popularity for cancer diagnosis, prognosis, and monitoring. While many technologies have been developed and validated in research laboratories, there has also been a push to expand these technologies into other clinical settings and as point of care devices. In this article, we discuss and evaluate microchip-based technologies for circulating tumor cell (CTC), exosome, and circulating tumor nucleic acid (ctNA) capture, detection, and analysis. Such integrated systems streamline otherwise multiple-step, manual operations to get a sample-to-answer quantitation. In addition, analysis of disease biomarkers is suited to point of care settings because of ease of use, low consumption of sample and reagents, and high throughput. We also cover the basics of biomarkers and their detection in biological fluid samples suitable for liquid biopsy on-chip. We focus on emerging technologies that process a small patient sample with high spatial-temporal resolution and derive clinically meaningful results through on-chip biomarker sensing and downstream molecular analysis in a simple workflow. This critical review is meant as a resource for those interested in developing technologies for capture, detection, and analysis platforms for liquid biopsy in a variety of settings.

Identifying Important Attributes for Prognostic Prediction in Traumatic Brain Injury Patients

Background: Generally, traumatic brain injury (TBI) patients do not have a stable condition, particularly after the first week of TBI. Hence, indicating the attributes in prognosis through a prediction model is of utmost importance since it helps caregivers with treatment-decision options, or prepares the relatives for the most-likely outcome. Objectives: This study attempted to determine and order the attributes in prognostic prediction in TBI patients, based on early clinical findings. A hybrid method was employed, which combines a decision tree (DT) and an artificial neural network (ANN) in order to improve the modeling process. Methods: The DT approach was applied as the initial analysis of the network architecture to increase accuracy in prediction. Afterwards, the ANN structure was mapped from the initial DT based on a part of the data. Subsequently, the designed network was trained and validated by the remaining data. 5-fold cross-validation method was applied to train the network. The area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy rate were utilized as performance measures. The important attributes were then determined from the trained network using two methods: change of mean squared error (MSE), and sensitivity analysis (SA). Results: The hybrid method offered better results compared to the DT method. The accuracy rate of 86.3 % vs. 82.2 %, sensitivity value of 55.1 % vs. 47.6 %, specificity value of 93.6 % vs. 91.1 %, and the area under the ROC curve of 0.705 vs. 0.695 were achieved for the hybrid method and DT, respectively. However, the attributes’ order by DT method was more consistent with the clinical literature. Conclusions: The combination of different modeling methods can enhance their performance. However, it may create some complexities in computations and interpretations. The outcome of the present study could deliver some useful hints in prognostic prediction on the basis of early clinical findings for TBI patients.

Proteomic Profiling of Serial Prediagnostic Serum Samples for Early Detection of Colon Cancer in the U.S. Military

Background: Serum proteomic biomarkers offer a promising approach for early detection of cancer. In this study, we aimed to identify proteomic profiles that could distinguish colon cancer cases from controls using serial prediagnostic serum samples.Methods: This was a nested case-control study of active duty military members. Cases consisted of 264 patients diagnosed with colon cancer between 2001 and 2009. Controls were matched to cases on age, gender, race, serum sample count, and collection date. We identified peaks that discriminated cases from controls using random forest data analysis with a 2/3 training and 1/3 validation dataset. We then included epidemiologic data to see whether further improvement of model performance was obtainable. Proteins that corresponded to discriminatory peaks were identified.Results: Peaks with m/z values of 3,119.32, 2,886.67, 2,939.23, and 5,078.81 were found to discriminate cases from controls with a sensitivity of 69% and a specificity of 67% in the year before diagnosis. When smoking status was included, sensitivity increased to 76% while histories of other cancer and tonsillectomy raised specificity to 76%. Peaks at 2,886.67 and 3,119.32 m/z were identified as histone acetyltransferases while 2,939.24 m/z was a transporting ATPase subunit.Conclusions: Proteomic profiles in the year before cancer diagnosis have the potential to discriminate colon cancer patients from controls, and the addition of epidemiologic information may increase the sensitivity and specificity of discrimination.Impact: Our findings indicate the potential value of using serum prediagnostic proteomic biomarkers in combination with epidemiologic data for early detection of colon cancer. Cancer Epidemiol Biomarkers Prev; 26(5); 711-8. ©2016 AACR.

Serum peptidome profiling analysis for the identification of potential biomarkers in cervical intraepithelial neoplasia patients

Cervical intraepithelial neoplasia (CIN) is a precancerous disease of cervical squamous cell carcinoma. We Used Mass Spectrometry based peptidome profile study to predict the transformation of CIN1, which is the primary stage of this lesion. . Serum samples of 34 Cervical squamous cell carcinoma patients, 31 healthy controls, and 29 CIN1 samples were analyzed. Peptides were purified by WCX magnetic beads (Bioyong), and analyzed by MALDI TOF (Bruker). Raw data were analyzed by BioExplorer software (Bioyong). The results showed 14 mass peaks with significant differences. The diagnosis model is established by analyzing peptide profiles of 15 SCC patients and 20 healthy women serum, with a sensitivity of 100% and specificity of 100.00%. In validation set, the SCC diagnosis model also had good performance with a sensitivity of 80%, a specificity of 100%. In addition, this model could predict 29 CIN1 patients with accuracy of 55.17%. These results would provide a new method to predict the trend of CIN1 and take effective measures for high risk group timely.

CorRECTreatment: a web-based decision support tool for rectal cancer treatment that uses the analytic hierarchy process and decision tree

BACKGROUND

The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians' decision making.

OBJECTIVE

The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer.

METHODS

The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set.

RESULTS

In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step.

CONCLUSIONS

The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options.

Colorectal cancer biomarker discovery and validation using LC-MS/MS-based proteomics in blood: truth or dare?

Globally, colorectal cancer (CRC) is the third most common malignant neoplasm. However, highly sensitive, specific, noninvasive tests that allow CRC diagnosis at an early stage are still needed. As circulatory blood reflects the physiological status of an individual and/or the disease status for several disorders, efforts have been undertaken to identify candidate diagnostic CRC markers in plasma and serum. In this review, the challenges, bottlenecks and promising properties of mass spectrometry (MS)-based proteomics in blood are discussed. More specifically, important aspects in clinical design, sample retrieval, sample preparation, and MS analysis are presented. The recent developments in targeted MS approaches in plasma or serum are highlighted as well.

Biomarkers for early detection of colorectal cancer and polyps: systematic review

There is growing interest in early detection of colorectal cancer as current screening modalities lack compliance and specificity. This study systematically reviewed the literature to identify biomarkers for early detection of colorectal cancer and polyps. Literature searches were conducted for relevant papers since 2007. Human studies reporting on early detection of colorectal cancer and polyps using biomarkers were included. Methodologic quality was evaluated, and sensitivity, specificity, and the positive predictive value (PPV) were reported. The search strategy identified 3,348 abstracts. A total of 44 papers, examining 67 different tumor markers, were included. Overall sensitivities for colorectal cancer detection by fecal DNA markers ranged from 53% to 87%. Combining fecal DNA markers increased the sensitivity of colorectal cancer and adenoma detection. Canine scent detection had a sensitivity of detecting colorectal cancer of 99% and specificity of 97%. The PPV of immunochemical fecal occult blood test (iFOBT) is 1.26%, compared with 0.31% for the current screening method of guaiac fecal occult blood test (gFOBT). A panel of serum protein biomarkers provides a sensitivity and specificity above 85% for all stages of colorectal cancer, and a PPV of 0.72%. Combinations of fecal and serum biomarkers produce higher sensitivities, specificities, and PPVs for early detection of colorectal cancer and adenomas. Further research is required to validate these biomarkers in a well-structured population-based study.

Application of proteomics in diagnosis of ADHD, schizophrenia, major depression, and suicidal behavior

This report focuses on the application of different proteomic techniques in diagnosis and treatment of psychiatric disorders such as major depression, suicidal behavior, schizophrenia, and attention deficit/hyperactivity disorder (ADHD). Firstly, we briefly describe different analytic approaches that can be applied for the discovery of specific biomarkers for diagnosing the above disorders, as well as for monitoring the effect of their treatment. Secondly, we discussed the types of biomarkers in general used in biomedicine for characterizing different disorders and diseases. Next, the potential applications of these biomarkers for diagnosing and managing major depression, suicidal behavior, schizophrenia, and ADHD are discussed in details. Forensic aspects of these biomarkers for the above disorders are also considered. Finally, we discuss the potential of specific biomarkers for distinguishing between comorbid psychiatric disorders in clinical setup as well as their potential for understanding mechanisms underlying the disorders and in discovery of new treatment strategies.

Biomarkers screening between preoperative and postoperative patients in pancreatic cancer

OBJECTIVE

To investigate discriminating protein patterns and potential biomarkers in serum samples between pre/postoperative pancreatic cancer patients and healthy controls.

METHODS

23 serum samples from PC patients (12 preoperative and 11 postoperative) and 76 from healthy controls were analyzed using matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS) technique combined with magnetic beads-based weak cation-exchange chromatography (MB-WCX). ClinProTools software selected several markers that made a distinction between pancreatic cancer patients and healthy controls.

RESULTS

49 m/z distinctive peaks were found among the three groups, of which 33 significant peaks with a P < 0.001 were detected. Two proteins could distinguish the preoperative pancreatic cancer patients from the healthy controls. About 15 proteins may be potential biomarkers in assessment of pancreatic cancer resection.

CONCLUSION

MB-MALDI-TOF-MS method could generate serum peptidome profiles of pancreatic cancer and provide a new approach to identify potential biomarkers for diagnosis and prognosis of this malignancy.

Proteomic approaches in biomarker discovery: new perspectives in cancer diagnostics

Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies.

Pituitary adenoma biomarkers identified using proteomic fingerprint technology

OBJECTIVE

To determine whether pituitary adenomas can be diagnosed by identifying protein biomarkers in the serum.

METHODS

We compared serum proteins from 65 pituitary adenoma patients and 90 healthy donors using proteomic fingerprint technology combining magnetic beads with matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS).

RESULTS

A total of 42 M/Z peaks were identified as related to pituitary adenoma (P<0.01). A diagnostic model established based on three biomarkers (3382.0, 4601.9, 9191.2) showed that the sensitivity of diagnosing pituitary adenoma was 90.0% and the specificity was 88.3%. The model was further tested by blind analysis showing that the sensitivity was 88.0% and the specificity was 83.3%.

CONCLUSIONS

These results suggest that proteomic fingerprint technology can be used to identify pituitary adenoma biomarkers and the model based on three biomarkers (3382.0, 4601.9, 9191.2) provides a powerful and reliable method for diagnosing pituitary adenoma.

Two classifiers based on serum peptide pattern for prediction of HBV-induced liver cirrhosis using MALDI-TOF MS

Chronic infection with hepatitis B virus (HBV) is associated with the majority of cases of liver cirrhosis (LC) in China. Although liver biopsy is the reference method for evaluation of cirrhosis, it is an invasive procedure with inherent risk. The aim of this study is to discover novel noninvasive specific serum biomarkers for the diagnosis of HBV-induced LC. We performed bead fractionation/MALDI-TOF MS analysis on sera from patients with LC. Thirteen feature peaks which had optimal discriminatory performance were obtained by using support-vector-machine-(SVM-) based strategy. Based on the previous results, five supervised machine learning methods were employed to construct classifiers that discriminated proteomic spectra of patients with HBV-induced LC from those of controls. Here, we describe two novel methods for prediction of HBV-induced LC, termed LC-NB and LC-MLP, respectively. We obtained a sensitivity of 90.9%, a specificity of 94.9%, and overall accuracy of 93.8% on an independent test set. Comparisons with the existing methods showed that LC-NB and LC-MLP held better accuracy. Our study suggests that potential serum biomarkers can be determined for discriminating LC and non-LC cohorts by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. These two classifiers could be used for clinical practice in HBV-induced LC assessment.

SELDI-TOF MS combined with magnetic beads for detecting serum protein biomarkers and establishment of a boosting decision tree model for diagnosis of pancreatic cancer

AIM

New technologies for the early detection of pancreatic cancer (PC) are urgently needed. The aim of the present study was to screen for the potential protein biomarkers in serum using proteomic fingerprint technology.

METHODS

Magnetic beads combined with surface-enhanced laser desorption/ionization (SELDI) TOF MS were used to profile and compare the protein spectra of serum samples from 85 patients with pancreatic cancer, 50 patients with acute-on-chronic pancreatitis and 98 healthy blood donors. Proteomic patterns associated with pancreatic cancer were identified with Biomarker Patterns Software.

RESULTS

A total of 37 differential m/z peaks were identified that were related to PC (P<0.01). A tree model of biomarkers was constructed with the software based on the three biomarkers (7762 Da, 8560 Da, 11654 Da), this showing excellent separation between pancreatic cancer and non-cancer., with a sensitivity of 93.3% and a specificity of 95.6%. Blind test data showed a sensitivity of 88% and a specificity of 91.4%.

CONCLUSIONS

The results suggested that serum biomarkers for pancreatic cancer can be detected using SELDI-TOF-MS combined with magnetic beads. Application of combined biomarkers may provide a powerful and reliable diagnostic method for pancreatic cancer with a high sensitivity and specificity.

Profiling plasma peptides for the identification of potential ageing biomarkers in Chinese Han adults

Advancing age is associated with cardiovascular disease, diabetes mellitus and cancer, and shows significant inter-individual variability. To identify ageing-related biomarkers we performed a proteomic analysis on 1890 Chinese Han individuals, 1136 males and 754 females, aged 18 to 82 years, using weak cation exchange magnetic bead based MALDI-TOF-MS analysis. The study identified 44 peptides which varied in concentration in different age groups. In particular, apolipoprotein A-I (ApoA1) concentration gradually increased between 18 to 50 years of age, the levels of fibrinogen alpha (FGA) decreased over the same age span, while albumin (ALB) was significantly degraded in middle-aged individuals. In addition, the plasma peptide profiles of FGA and four other unidentified proteins were found to be gender-dependent. Plasma proteins such as FGA, ALB and ApoA1 are significantly correlated with age in the Chinese Han population and could be employed as indicative ageing-related biomarkers.

Comparison of plasma from healthy nonsmokers, smokers, and lung cancer patients: pattern-based differentiation profiling of low molecular weight proteins and peptides by magnetic bead technology with MALDI-TOF MS

CONTEXT

Smoking is the major contributor of lung cancer (LC), which accounts for millions of death.

OBJECTIVE

This study focused on the correlation between the proteomic profiling of LC patients, and healthy nonsmokers and smokers.

METHOD

Pattern-based peptide profiling of 186 plasma samples was performed through reversed-phase chromatography-18 magnetic bead fractionation coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis and resulted data were evaluated statistically by ClinProTool.

RESULTS

Marker peaks at m/z 1760, 5773, 5851, 2940, and 7172 were found with an excellent statistical figure.

CONCLUSION

Selected marker peaks can be served as a differentiated tool of LC patients with high sensitivity and specificity.

New serum biomarkers for detection of endometriosis using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

This study used proteomic fingerprint technology, combining nano-sized magnetic beads with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), to screen for potential protein biomarkers for the diagnosis of endometriosis. Serum proteins from 126 patients with endometriosis and 120 healthy controls were profiled and compared. Biomarker pattern software identified 46 discriminating mass-to-charge m/z ratio peaks that were related to endometriosis. The model constructed by the software, based on three of these peaks (m/z 5988.7, 7185.3 and 8929.8), generated excellent separation between the endometriosis and control groups. The sensitivity was 91.4% and the specificity 95.0%. Blind testing on a second series of serum samples from patients with endometriosis and healthy controls indicated a sensitivity of 89.3% and a specificity of 90.0%. Biomarkers for endometriosis can be discovered in serum by MALDI-TOF-MS in combination with nano-sized magnetic beads. The pattern of combined markers provides a powerful and reliable diagnostic method for endometriosis, with high sensitivity and specificity.

Effect of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry on identifying biomarkers of laryngeal carcinoma

The aim is to study the serum protein fingerprint of patients with laryngeal carcinoma (LC) and to screen for protein molecules closely related to LC during the onset and progression of the disease with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Serum samples from 68 patients with LC and 117 non-cancer control samples (75 healthy volunteers and 42 Vocal fold polyps). Q10 protein chips and PBSII-C protein chips reader (Ciphergen Biosystems Inc.) were used. The protein fingerprint expression of all the Serum samples and the resulting profiles between cancer and non-cancer groups were analyzed with Biomarker Wizard system. A group of proteomic peaks were detected. Three differently expressed potential biomarkers were identified with the relative molecular weights of 5,915, 6,440 and 9,190 Da. Among the three peaks, the one with m/z 6,440 was down-regulated, and the other two peaks with m/z 5,915 and 9,190 were up-regulated in LC. This diagnostic model could distinguish LC patients from controls with a sensitivity of 92.1% and a specificity of 91.9%. Moreover, blind test data showed a sensitivity of 86.7% and a specificity of 89.1%. The data suggested that SELDI technology could be used to screen proteins with altered expression levels in the serum of LC patients. These protein peaks were considered as specific serum biomarkers of LC and have the potential value for further investigation.

Pilot study of the application of magnetic bead protein profiling to the study of biomarkers in addiction research

OBJECTIVES

Proteomic technologies based on mass spectrometry are increasingly used as a valuable tool in clinical research allowing high-throughput protein and peptide profiling to be undertaken. Whilst previous research has focussed the application of this novel technology on the study of patients with disorders compared to comparable individuals from the healthy population, this current study seeks to determine the effect of successful treatment for alcoholism on the serum protein profile obtained.

METHODS

Serum samples were collected from patients after initial treatment for alcohol abuse and also 6 months after treatment. The serum samples were prepared for analysis using reverse phase magnetic bead fractionation and the resulting peptides analysed by matrix assisted laser desorption ionisation time-of-flight (MALDI-ToF) mass spectrometry.

RESULTS

Whilst the majority of the peptides detected by this approach exhibited constant levels between the two time points, three peptides were elevated at the 6-month time point compared to the initial sampling.

CONCLUSIONS

Whilst disorders with very clear biological causes (such as cancer) exhibit significantly different peptide profiles, psychiatric disorders such as alcohol addiction which are multifactorial show less obvious changes. Despite this the two groups of samples could statistically be distinguished by certain peptides expression levels.

Usefulness of serum mass spectrometry to identify women diagnosed with higher grades of cervical intraepithelial neoplasia may differ by race

BACKGROUND

An early detection of precursor lesions of cervical cancer will help to eliminate the worldwide burden of cervical cancer.

METHODS

This exploratory study aimed to identify, by matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS), serum protein profiles that distinguish cervical intraepithelial neoplasia grades CIN 1 or lower (≤CIN 1) from CIN 2+ among 127 women infected with human papillomavirus (HPV) 16. Of these 127 women, 25 and 23 were diagnosed with CIN 2 or CIN 3, respectively (cases), and 79 were diagnosed with ≤CIN 1 (non-cases). Serum protein profiles were generated by MALDI-TOF-MS. A total of 95 m/z peaks were tested for association with case status by two racial groups, African American (AAs) and Caucasian American (CAs).

RESULTS

Overall, 2 protein peaks identified by our study demonstrated higher specificity for identifying CIN 2+ than previously published studies. An increasing intensity of [m/z 4459] was associated with a higher risk of being a case, regardless of race with a specificity of 58% for CIN 2 and a specificity of 75% for CIN 3. An increasing intensity of [m/z 4154] was not only associated with a higher risk of being a case only among CAs, but also had an opposite effect among AAs.

CONCLUSION

Identification of specific proteins associated with the peaks detected in serum and development of antibody-based tests such as ELISA should lead to the development of race-specific, non-invasive and cost effective screening tests with higher specificity for identifying HPV 16 associated CIN 2+.

New serum biomarkers for detection of tuberculosis using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

BACKGROUND

New technologies for the early detection of tuberculosis (TB) are urgently needed. Pathological changes within an organ might be reflected in proteomic patterns in serum. The aim of the present study was to screen for the potential protein biomarkers in serum for the diagnosis of TB using proteomic fingerprint technology.

METHODS

Proteomic fingerprint technology combining protein chips with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) was used to profile the serum proteins from 50 patients with TB, 25 patients with lung disease other than TB, and 25 healthy volunteers. The protein fingerprint expression of all the serum samples and the resulting profiles between TB and control groups were analyzed with the Biomarker Wizard system.

RESULTS

A total of 30 discriminating m/z peaks were detected that were related to TB (p<0.01). The model of biomarkers constructed by the Biomarker Patterns Software based on the three biomarkers (2024, 8007, and 8598 Da) generated excellent separation between the TB and control groups. The sensitivity was 84.0% and the specificity was 86.0%. Blind test data indicated a sensitivity of 80.0% and a specificity of 84.2%.

CONCLUSIONS

The data suggested a potential application of SELDI-TOF MS as an effective technology to profile serum proteome, and with pattern analysis, a diagnostic model comprising three potential biomarkers was indicated to differentiate people with TB and healthy controls rapidly and precisely.

Identification of serum biomarkers for ovarian cancer using MALDI-TOF-MS combined with magnetic beads

BACKGROUND

The objective of this study was to search for potential protein biomarkers in serum for diagnosis of ovarian cancer, by use of proteomic fingerprint techniques.

METHOD

MALDI-TOF-MS was combined with magnetic beads to profile and compare serum protein spectra from 40 ovarian cancer patients and from 60 healthy controls.

RESULTS

The tree analysis model of potential cancer biomarkers was constructed with Biomarker Patterns software on the basis of three identified biomarkers (5486, 6440, and 13720 Da), resulting in excellent discrimination between the ovarian cancer and non-cancer in our tests. The sensitivity was 90% and the specificity was 86.7%. In a blind test the sensitivity was 88% and the specificity was 83.3%.

CONCLUSION

The results suggested that biomarkers for ovarian cancer diagnosis in serum could be identified by MALDI-TOF-MS combined with the use of magnetic beads. The use of combined biomarkers would further enable powerful and reliable diagnosis of ovarian cancer with high sensitivity and specificity.