Identification of potential serum peptide biomarkers of biliary tract cancer using MALDI MS profiling

MALDI-TOF MS Analysis of Serum Peptidome Patterns in Cervical Cancer

BACKGROUND

Cervical cancer is the fourth most common cancer among females worldwide. Identifying peptide patterns discriminating healthy individuals from those with diseases has gained interest in the early detection of cancers. Our study aimed to determine signature peptide patterns for cervical cancer screening.

METHODS

Our study focused on the serum peptidome analysis of 83 healthy women and 139 patients with cervical cancer. All spectra derived from matrix-assisted laser desorption/ionization time-of-flight mass spectrometry were analyzed using FlexAnalysis 3.0 and ClinProTools 2.2 software.

RESULTS

In the mass range of 1000-10,000 Da, the total average spectra were represented as the signature pattern. Principal component analysis showed that all the groups were separately distributed. Furthermore, the peaks at m/z 1466.91, 1898.01, 3159.09, and 4299.40 significantly differed among the investigated groups (Wilcoxon/Kruskal-Wallis test and ANOVA, p < 0.001).

CONCLUSIONS

Laboratory-based rapid mass spectrometry showed that serum peptidome patterns could serve as diagnostic tools for diagnosing cervical cancer; however, verification through larger cohorts and association with clinical data are required, and the use of externally validated samples, such as patients with other types of cancers, should be investigated to validate the specific peptide patterns.

A Circulating Risk Score, Based on Combined Expression of Exo-miR-130a-3p and Fibrinopeptide A, as Predictive Biomarker of Relapse in Resectable Non-Small Cell Lung Cancer Patients

Simple Summary

To date, the five-year survival rate of early stages of non-small cell lung cancer (NSCLC) is still disappointing and reliable prognostic factors are mandatory. Here, we performed in-depth high-throughput analyses of plasma circulating markers, including exosomal microRNAs and peptidome to identify a prognostic score. The miRnome profile selected the Exo-miR-130a-3p as the most overexpressed in relapsed patients. Peptidome analysis identified four progressively more degraded forms of fibrinopeptide A (FpA), which were depleted in relapse patients. Notably, a stepwise algorithm selected Exo-miR-130a-3p and the greatest FpA (2–16) to build a prognostic score, where high-risk patients had 18 months of median disease-free survival. Overexpression of miR-130a-3p cells led to a deregulation of pathways such as angiogenesis as well as the coagulation and metalloprotease, which might be linked to FpA reduction. The risk score integrating circulating markers may help clinicians predict early-stage NSCLC patients who are more likely to relapse after surgery.

Abstract

To date, the 5-year overall survival rate of 60% for early-stage non-small cell lung cancer (NSCLC) is still unsatisfactory. Therefore, reliable prognostic factors are needed. Growing evidence shows that cancer progression may depend on an interconnection between cancer cells and the surrounding tumor microenvironment; hence, circulating molecules may represent promising markers of cancer recurrence. In order to identify a prognostic score, we performed in-depth high-throughput analyses of plasma circulating markers, including exosomal microRNAs (Exo-miR) and peptides, in 67 radically resected NSCLCs. The miRnome profile selected the Exo-miR-130a-3p as the most overexpressed in relapsed patients. Peptidome analysis identified four progressively more degraded forms of fibrinopeptide A (FpA), which were depleted in progressing patients. Notably, stepwise Cox regression analysis selected Exo-miR-130a-3p and the greatest FpA (2-16) to build a score predictive of recurrence, where high-risk patients had 18 months of median disease-free survival. Moreover, in vitro transfections showed that higher levels of miR-130a-3p lead to a deregulation of pathways involved in metastasis and angiogenesis, including the coagulation process and metalloprotease increase which might be linked to FpA reduction. In conclusion, by integrating circulating markers, the identified risk score may help clinicians predict early-stage NSCLC patients who are more likely to relapse after primary surgery.

Application of peptide biomarkers in life analysis based on liquid chromatography-mass spectrometry technology

Biomedicine is developing rapidly in the 21st century. Among them, the qualitative and quantitative analysis of peptide biomarkers is of considerable importance for the diagnosis and therapy of diseases and the quality evaluation of drugs and food. The identification and quantitative analysis of peptides have been going on for decades. Traditionally, immunoassays or biological assays are generally used to quantify peptides in biological matrices. However, the selectivity and sensitivity of these methods cannot meet the requirements of the application. The separation and analysis technique of liquid chromatography-mass spectrometry (LC-MS) supplies a reliable alternative. In contrast to immunoassays, LC-MS methods are capable of providing the analytical prowess necessary to satisfy the demands of peptide biomarker research in the life sciences arena. This review article provides a historical account of the in-roads made by LC-MS technology for the detection of peptide biomarkers in the past 10 years, with the focus on the qualification/quantification developments and their applications.

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.

MALDI-TOF-MS Analysis in the Discovery and Identification of the Serum Peptide Pattern of Pancreatic Ductal Adenocarcinoma

OBJECTIVE

To explore the application of serum peptidomics in the early diagnosis of pancreatic ductal adenocarcinoma (PDAC).

METHODS

The serum specimens from 176 patients with PDAC and 158 healthy control patients were subjected to matrix-assisted laser desorption ionization time-of-flight mass spectrometry to obtain serum peptide profiles. Next, a classification model by differentiated peptides was established and verified to distinguish the 2 groups. Finally, the peptides were identified by tandem mass spectrometry.

RESULTS

A classification model was established by 13 peptides. For patients with PDAC in the early stage, the sensitivity and specificity of the model reached 100% and 96.7%, respectively. The amino acid sequences of the 13 peptides were then determined and the types of proteins were identified, including platelet basic protein, fibrinogen alpha, complement C3, and secreted frizzled-related protein 4. Some of the 13 peptides could be potential PDAC biomarkers.

CONCLUSION

Serum peptidomics may have potential application in the early diagnosis of PDAC.

Proteomics analysis identified serum biomarkers for occupational benzene exposure and chronic benzene poisoning

The study aimed to find novel effect biomarkers for occupational benzene exposure and chronic benzene poisoning (CBP), which might also provide clues to the mechanism of benzene toxicity.We performed a comparative serological proteome analysis between healthy control workers with no benzene exposure, workers with short-term benzene exposure, workers with long-term benzene exposure, and CBP patients using 2D-DIGE and MALDI-TOF-MS. Two of the differentially expressed proteins were then selected to be validated by immune turbidimetric analysis.A total of 10 proteins were found to be significantly altered between different groups. The identified deferentially expressed proteins were classified according to their molecular functions, biological processes, and protein classes. The alteration of 2 important serum proteins among them, apolipoprotein A-I and transthyretin, were further confirmed.Our findings suggest that the identified differential proteins could be used as biomarkers for occupational benzene exposure and CBP, and they may also help elucidate the mechanisms of benzene toxicity.

Proteome analysis of the potential serum biomarkers for chronic benzene poisoning

The aim of our study is to seek novel specific biomarkers which could provide clues to the mechanism of chronic benzene poisoning (CBP) and might also be used as specific markers for early detection and diagnosis. In this study, a comparative serological proteome analysis between normal controls and CBP patients at three different levels of poisoning were performed via a 2D-DIGE and MALDI-TOF-MS. As the result a total of 10 proteins were found significantly altered between the normal and the mild, moderate and severe poisoning. The identified differentially expressed proteins were classified according to their molecular functions, biological processes, and protein classes, and three important serum proteins among them, apolipoproteinA-1, alpha-1-antitrypsin and complement C3, were further confirmed by immune turbidimetric analysis for their significant up-regulation in the CBP patients. Our findings suggest that these differential proteins may help elucidate the mechanism of CBP and provide potential biomarkers for diagnosis.

Serum peptide expression and treatment responses in patients with advanced non-small-cell lung cancer

Epidermal growth factor receptor (EGFR) mutation is an important predictor for response to personalized treatments of patients with advanced non-small-cell lung cancer (NSCLC). However its usage is limited due to the difficult of obtaining tissue specimens. A novel prediction system using matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been reported to be a perspective tool in European countries to identify patients who are likely to benefit from EGFR tyrosine kinase inhibitor (TKI) treatment. In the present study, MALDI-TOF MS was used on pretreatment serum samples of patients with advanced non-small-cell lung cancer to discriminate the spectra between disease control and disease progression groups in one cohort of Chinese patients. The candidate features for classification were subsequently validated in a blinded fashion in another set of patients. The correlation between plasma EGFR mutation status and the intensities of representative spectra for classification was evaluated. A total of 103 patients that were treated with EGFR-TKIs were included. It was determined that 8 polypeptides peaks were significant different between the disease control and disease progression group. A total of 6 polypeptides were established in the classification algorithm. The sensitivity of the algorithm to predict treatment responses was 76.2% (16/21) and the specificity was 81.8% (18/22). The accuracy rate of the algorithm was 79.1% (34/43). A total of 3 polypeptides were significantly correlated with EGFR mutations (P=0.04, P=0.03 and P=0.04, respectively). The present study confirmed that MALDI-TOF MS analysis can be used to predict responses to EGFR-TKI treatment of the Asian population where the EGFR mutation status differs from the European population. Furthermore, the expression intensities of the three polypeptides in the classification model were associated with EGFR mutation.

Detection and Identification of Serum Peptides Biomarker in Papillary Thyroid Cancer

Background

Papillary thyroid cancer (PTC) is currently the most commonly diagnosed endocrine malignancy. In addition, the sex- and age-adjusted incidence of PTC has exhibited a greater increase over the last 2 decades than in many other malignancies. Thus, discovering noninvasive specific serum biomarker to distinguish PTC from cancer-free controls in its early stages remains an important goal.

Material/Methods

Serum samples from 88 PTC patients and 80 cancer-free controls were randomly allocated into training or validation sets. Serum peptide profiling was performed by matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF-MS) after using weak cation exchange magnetic beads (WCX-MB), and the results were evaluated by use of ClinProTools™ Software. To distinguish PTC from cancer-free controls, quick classifier (QC), supervised neural network (SNN), and genetic algorithm (GA) models were established. The models were blindly validated to verify their diagnostic capabilities. The most discriminative peaks were subsequently identified with a nano-liquid chromatography-electrospray ionization-tandem mass spectrometry system.

Results

Six peptide ions were identified as the most discriminative peaks between the PTC and cancer-free control samples. The QC model exhibited satisfactory sensitivity and specificity among the 3 models that were validated. Two peaks, at m/z 2671.17 and m/z 1464.68, were identified as fragments of the alpha chain of fibrinogen, while a peak at m/z 1738.92 was a fragment of complement component 4A/B.

Conclusions

MS combined with ClinProTools™ software was able to detect peptide biomarkers in PTC patients. In addition, the constructed classification models provided a serum peptidome pattern for distinguishing PTC from cancer-free controls. Both fibrinogen α and complement C4A/B were identified as potential markers for diagnosis of PTC.

MALDI-TOF-MS analysis in discovery and identification of serum proteomic patterns of ovarian cancer

Background

Due to high mortality and lack of efficient screening, new tools for ovarian cancer (OC) diagnosis are urgently needed. To broaden the knowledge on the pathological processes that occur during ovarian cancer tumorigenesis, protein-peptide profiling was proposed.

Methods

Serum proteomic patterns in samples from OC patients were obtained using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF). Eighty nine serum samples (44 ovarian cancer and 45 healthy controls) were pretreated using solid-phase extraction method. Next, a classification model with the most discriminative factors was identified using chemometric algorithms. Finally, the results were verified by external validation on an independent test set of samples.

Results

Main outcome of this study was an identification of potential OC biomarkers by applying liquid chromatography coupled with tandem mass spectrometry. Application of this novel strategy enabled the identification of four potential OC serum biomarkers (complement C3, kininogen-1, inter-alpha-trypsin inhibitor heavy chain H4, and transthyretin). The role of these proteins was discussed in relation to OC pathomechanism.

Conclusions

The study results may contribute to the development of clinically useful multi-component diagnostic tools in OC. In addition, identifying a novel panel of discriminative proteins could provide a new insight into complex signaling and functional networks associated with this multifactorial disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3467-2) contains supplementary material, which is available to authorized users.

Application Value of Mass Spectrometry in the Differentiation of Benign and Malignant Liver Tumors

Background

Differentiation of malignant from benign liver tumors remains a challenging problem. In recent years, mass spectrometry (MS) technique has emerged as a promising strategy to diagnose a wide range of malignant tumors. The purpose of this study was to establish classification models to distinguish benign and malignant liver tumors and identify the liver cancer-specific peptides by mass spectrometry.

Material/Methods

In our study, serum samples from 43 patients with malignant liver tumors and 52 patients with benign liver tumors were treated with weak cation-exchange chromatography Magnetic Beads (MB-WCX) kits and analyzed by the Matrix-Assisted Laser Desorption Time of Flight Mass Spectrometry (MALDI-TOF-MS). Then we established genetic algorithm (GA), supervised neural networks (SNN), and quick classifier (QC) models to distinguish malignant from benign liver tumors. To confirm the clinical applicability of the established models, the blinded validation test was performed in 50 clinical serum samples. Discriminatory peaks associated with malignant liver tumors were subsequently identified by a qTOF Synapt G2-S system.

Results

A total of 27 discriminant peaks (p<0.05) in mass spectra of serum samples were found by ClinPro Tools software. Recognition capabilities of the established models were 100% (GA), 89.38% (SNN), and 80.84% (QC); cross-validation rates were 81.67% (GA), 81.11% (SNN), and 86.11% (QC). The accuracy rates of the blinded validation test were 78% (GA), 84% (SNN), and 84% (QC). From the 27 discriminatory peptide peaks analyzed, 3 peaks of m/z 2860.34, 2881.54, and 3155.67 were identified as a fragment of fibrinogen alpha chain, fibrinogen beta chain, and inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), respectively.

Conclusions

Our results demonstrated that MS technique can be helpful in differentiation of benign and malignant liver tumors. Fibrinogen and ITIH4 might be used as biomarkers for the diagnosis of malignant liver tumors.

Challenges in biomarker discovery with MALDI-TOF MS

MALDI-TOF MS technique is commonly used in system biology and clinical studies to search for new potential markers associated with pathological conditions. Despite numerous concerns regarding a sample preparation or processing of complex data, this strategy is still recognized as a popular tool and its awareness has risen in the proteomic community over the last decade. In this review, we present comprehensive application of MALDI mass spectrometry with special focus on profiling research. We also discuss major advantages and disadvantages of universal sample preparation methods such as micro-SPE columns, immunodepletion or magnetic beads, and we show the potential of nanostructured materials in capturing low molecular weight subproteomes. Furthermore, as the general protocol considerably affects spectra quality and interpretation, an alternative solution for improved ion detection, including hydrophobic constituents, data processing and statistical analysis is being considered in up-to-date profiling pattern. In conclusion, many reports involving MALDI-TOF MS indicated highly abundant proteins as valuable indicators, and at the same time showed the inaccuracy of available methods in the detection of low abundant proteome that is the most interesting from the clinical perspective. Therefore, the analytical aspects of sample preparation methods should be standardized to provide a reproducible, low sample handling and credible procedure.

Identification of Serum Peptidome Signatures of Non-Small Cell Lung Cancer

Due to high mortality rates of lung cancer, there is a need for identification of new, clinically useful markers, which improve detection of this tumor in early stage of disease. In the current study, serum peptide profiling was evaluated as a diagnostic tool for non-small cell lung cancer patients. The combination of the ZipTip technology with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for the analysis of peptide pattern of cancer patients (n = 153) and control subjects (n = 63) was presented for the first time. Based on the observed significant differences between cancer patients and control subjects, the classification model was created, which allowed for accurate group discrimination. The model turned out to be robust enough to discriminate a new validation set of samples with satisfactory sensitivity and specificity. Two peptides from the diagnostic pattern for non-small cell lung cancer (NSCLC) were identified as fragments of C3 and fibrinogen α chain. Since ELISA test did not confirm significant differences in the expression of complement component C3, further study will involve a quantitative approach to prove clinical utility of the other proteins from the proposed multi-peptide cancer signature.

Harvesting Low Molecular Weight Biomarkers Using Gold Nanoparticles

We developed and characterized a platform based on gold (Au) nanoparticles (NPs) coated with poly(acrylic acid) (PAA) for harvesting positively charged, low molecular weight (LMW) proteins. The particles are synthesized using a layer by layer (LbL) procedure: first the gold NPs are coated with positively charged polyethylenimine (PEI) and subsequently with PAA. This simple procedure produces stable PAA-PEI-Au (PPAu) NPs with high selectivity and specificity. PPAu NPs successfully harvested, separated, and detected various LMW proteins and peptides from serum containing a complex mixture of abundant high molecular weight (HMW) proteins, including bovine serum albumin (BSA) and Immunoglobulin G (IgG). In addition, PPAu NPs selectively harvested and separated LMW proteins from serum in the presence of another positively charged competing protein. Furthermore, PPAu NPs successfully harvested a LMW biomarker in a mock diseased state. This system can be applied in various biomedical applications where selective harvesting and identifying of LMW proteins is required. A particularly useful application for this system can be found in early cancer diagnosis as described hereinafter.

Influence of honeybee sting on peptidome profile in human serum

The aim of this study was to explore the serum peptide profiles from honeybee stung and non-stung individuals. Two groups of serum samples obtained from 27 beekeepers were included in our study. The first group of samples was collected within 3 h after a bee sting (stung beekeepers), and the samples were collected from the same person a second time after at least six weeks after the last bee sting (non-stung beekeepers). Peptide profile spectra were determined using MALDI-TOF mass spectrometry combined with Omix, ZipTips and magnetic beads based on weak-cation exchange (MB-WCX) enrichment strategies in the mass range of 1–10 kDa. The samples were classified, and discriminative models were established by using the quick classifier, genetic algorithm and supervised neural network algorithms. All of the statistical algorithms used in this study allow distinguishing analyzed groups with high statistical significance, which confirms the influence of honeybee sting on the serum peptidome profile. The results of this study may broaden the understanding of the human organism’s response to honeybee venom. Due to the fact that our pilot study was carried out on relatively small datasets, it is necessary to conduct further proteomic research of the response to honeybee sting on a larger group of samples.