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

Liquid biopsy of peripheral blood using mass spectrometry detects primary extramedullary disease in multiple myeloma patients

Multiple myeloma (MM) is the second most prevalent hematological malignancy, characterized by infiltration of the bone marrow by malignant plasma cells. Extramedullary disease (EMD) represents a more aggressive condition involving the migration of a subclone of plasma cells to paraskeletal or extraskeletal sites. Liquid biopsies could improve and speed diagnosis, as they can better capture the disease heterogeneity while lowering patients' discomfort due to minimal invasiveness. Recent studies have confirmed alterations in the proteome across various malignancies, suggesting specific changes in protein classes. In this study, we show that MALDI-TOF mass spectrometry fingerprinting of peripheral blood can differentiate between MM and primary EMD patients. We constructed a predictive model using a supervised learning method, partial least squares-discriminant analysis (PLS-DA) and evaluated its generalization performance on a test dataset. The outcome of this analysis is a method that predicts specifically primary EMD with high sensitivity (86.4%), accuracy (78.4%), and specificity (72.4%). Given the simplicity of this approach and its minimally invasive character, this method provides rapid identification of primary EMD and could prove helpful in clinical practice.

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.

MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance

Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell dyscrasia that can progress to malignant multiple myeloma (MM). Specific molecular biomarkers to classify the MGUS status and discriminate the initial asymptomatic phase of MM have not been identified. We examined the serum peptidome profile of MGUS patients and healthy volunteers using MALDI-TOF mass spectrometry and developed a predictive model for classifying serum samples. The predictive model was built using a support vector machine (SVM) supervised learning method tuned by applying a 20-fold cross-validation scheme. Predicting class labels in a blinded test set containing randomly selected MGUS and healthy control serum samples validated the model. The generalization performance of the predictive model was evaluated by a double cross-validation method that showed 88% average model accuracy, 89% average sensitivity and 86% average specificity. Our model, which classifies unknown serum samples as belonging to either MGUS patients or healthy individuals, can be applied to clinical diagnosis.

Identification of MST1 as a potential early detection biomarker for colorectal cancer through a proteomic approach

Colorectal cancer (CRC) is a common malignant neoplasm worldwide. It is important to identify new biomarkers for the early detection of CRC. In this study, magnetic beads and the Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) platform were used to analyse CRC and healthy control (HC) serum samples. The CRC diagnosis pattern was established to have a specificity of 94.7% and sensitivity of 92.3% in a blind test. The candidate biomarker serine/threonine kinase 4 (STK4, also known as MST1) was identified by Tandem mass spectrometry (MS/MS) and verified with western blotting and enzyme-linked immunosorbent assay (ELISA). The results indicated that there was a higher concentration of MST1 in HC subjects than stage I CRC patients for the early detection of CRC and a lower concentration in stage IV patients than in other CRC patients. The sensitivity and specificity of MST1 combined with carcinoembryonic antigen (CEA) and faecal occult blood test (FOBT) in diagnosis of colorectal cancer were 92.3% and 100%, respectively. Additionally, low MST1 expression was associated with the poor prognosis. These results illustrate that MST1 is a potential biomarker for early detection, prognosis and prediction of distant metastasis of CRC.

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.

Development of a MALDI MS-based platform for early detection of acute kidney injury

PURPOSE

Septic acute kidney injury (AKI) is associated with poor outcome. This can partly be attributed to delayed diagnosis and incomplete understanding of the underlying pathophysiology. Our aim was to develop an early predictive test for AKI based on the analysis of urinary peptide biomarkers by MALDI-MS.

EXPERIMENTAL DESIGN

Urine samples from 95 patients with sepsis were analyzed by MALDI-MS. Marker search and multimarker model establishment were performed using the peptide profiles from 17 patients with existing or within the next 5 days developing AKI and 17 with no change in renal function. Replicates of urine sample pools from the AKI and non-AKI patient groups and normal controls were also included to select the analytically most robust AKI markers.

RESULTS

Thirty-nine urinary peptides were selected by cross-validated variable selection to generate a support vector machine multidimensional AKI classifier. Prognostic performance of the AKI classifier on an independent validation set including the remaining 61 patients of the study population (17 controls and 44 cases) was good with an area under the receiver operating characteristics curve of 0.82 and a sensitivity and specificity of 86% and 76%, respectively.

CONCLUSION AND CLINICAL RELEVANCE

A urinary peptide marker model detects onset of AKI with acceptable accuracy in septic patients. Such a platform can eventually be transferred to the clinic as fast MALDI-MS test format.

Magnetic Bead-Based Serum Peptidome Profiling in Patients with Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is a frequent medical condition during pregnancy. Early diagnosis and treatment of GDM are crucial for both the mother and the baby. In the present study, we aimed to identify specific biomarkers to assist in the early detection of GDM and give some clues to the possible causes of GDM by comparing serum peptide profile differences between GDM patients and healthy controls. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was used in combination with weak cation exchange magnetic bead (WCX-MB). Levels of four peptides (4418.9, 2219.7, 2211.5, and 1533.4 Da) were significantly different. Interestingly, three of them (4418.9, 2211.5, and 1533.4 Da) were identified when GDM patients with two degrees of glucose intolerance were compared. Additionally, peptides 2211.5 and 1533.4 Da showed a decreasing trend as glucose intolerance increased, while peptide 4418.9 Da exhibited the reverse tendency. In conclusion, our study provides novel insights into the altered serum peptide profile of GDM patients. The specific candidate biomarkers may contribute to the development of GDM.

A prognostic biomarker for gastric cancer with lymph node metastases

Gastric cancer is one of the leading causes of tumor-related deaths in China. The tumor, node, metastasis (TNM) classification system is useful for predicting clinical prognosis of patients with gastric cancer. However, determining the presence of lymph node involvement in the early stages of gastric cancer is difficult without biopsy. Therefore, it is necessary to identify novel serum biomarkers for TNM cancer staging and prognostic follow-up. In this study, we have reported fibrinopeptide-A (FPA) with alanine truncation at the N-terminal as a novel biomarker to differentiate gastric cancer with and without lymph node metastases. We analyzed 369 individual serum samples including gastric cancer patients without lymph node metastases (n = 33), gastric cancer patients with lymph node metastases (n = 157; confirmed by pathology), and age- and sex-matched healthy individuals (n = 179). The data showed that 85.4% of patients with lymph node metastases were positive for FPA with alanine truncation at the N-terminal (degAla-FPA, 1,465.63 Da), as determined by tandem mass spectrometry (MS). Using degAla-FPA as the biomarker, the sensitivity was 85.4% for gastric cancer patients with lymph node metastases, and the specificity was 100% for gastric cancer patients without lymph node metastases. The high sensitivity and specificity achieved with serum degAla-FPA levels indicated that MS technology could facilitate the discovery of a novel and quantitative prognostic biomarker for gastric cancer with lymph node involvement.

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.

Screening for specific biomarkers in the serum of postmenopausal osteoporosis patients using proteomic fingerprint techniques

The aim of this study was to detect serum protein biomarkers and establish a diagnostic model for postmenopausal osteoporosis (PMOP) adopting matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) combined with weak cationic exchange (WCX) magnetic beads, and to study the clinical significance of the model in the early diagnosis of PMOP. Serum samples from 45 patients with PMOP, 30 patients with osteopenia and 40 healthy controls were prepared using WCX magnetic beads, and were then analyzed using a PBSII-C mass spectrometer reader. The protein spectra of the serum samples were normalized using the Ciphergen Proteinchip software. The peak labeling was performed using the Biomarker Wizard software. The specific protein biomarkers were screened using the Biomarker Pattern software to construct a diagnostic model for PMOP. A total of 138 discriminative mass-to-charge (m/z) ratios were found to be associated with PMOP. Of these, the m/z peaks at 3167.4, 4071.1, 7771.7 and 8140.5 were used to construct a diagnostic model in a training set. In a test set, the sensitivity and specificity of the model were 91.11 and 92.86%, respectively. Potential protein biomarkers for PMOP were detected in patient serum using MALDI-TOF MS combined with WCX magnetic beads. This model of multiple biomarkers provided a powerful and reliable diagnostic method for PMOP diagnosis with high sensitivity and specificity.

Identification of a new protein biomarker for colorectal cancer diagnosis

As one of the most common cancers, colorectal cancer (CRC) is a major public health issue worldwide. Thus, the identification of novel biomarkers to aid in the early diagnosis of CRC is crucial. The aim of the present study was to identify a novel protein biomarker for CRC, and to identify its structure. In this study, a total of 99 serum samples from 73 CRC patients and 26 healthy controls were collected and analyzed by SELDI-TOF-MS. The biomarkers were separated using HPLC and detected with MALDI-TOF-MS. The qualified peaks were ranked by p-value of non-parametric tests and the top 10 peaks displaying significant differences were selected. Among the 10 protein biomarkers, the concentration of a 3.9‑kDa protein in the serum of the CRC patients was much lower than that in the healthy controls. Therefore, the 3.9‑kDa protein was selected as a biomarker for CRC and its separation and purification were performed. The structure of the 3.9-kDa protein biomarker was determined by LC-MS/MS, and was confirmed to be a fragment of serine/theonine kinase 4 (MST1/STK4). The 3.9‑kDa protein biomarker had high sensitivity and specificity for CRC, and its potential clinical application warrants further investigation.

Diagnosis of gallbladder cancer using matrix-assisted laser desorption/ionization time-of-flight profiling

Proteomic fingerprint technology combining magnetic beads with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry was used to profile and compare the serum proteins from 45 patients with gallbladder cancer and 50 healthy blood donors. The proteomic patterns were identified; the tree model of biomarkers was constructed and evaluated using the Biomarker Patterns Software. The model tree was constructed based on the 3 biomarkers (5913 Da, 6181 Da and 13,752 Da), which generated excellent separation between the gallbladder cancer and control groups. The sensitivity was 86.7% and the specificity was 93.3%. The blind test data showed a sensitivity of 80% and a specificity of 90%. Taken together, our studies suggested that biomarkers for gallbladder cancer could be discovered in serum by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with the use of magnetic beads. The pattern of combined markers would provide a powerful and reliable diagnostic method for gallbladder cancer 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.

The role of microRNAs in the management of liver cancer

Four major components of epigenetic regulation are promoter methylation, histone modification, chromatin conformation changes, and altered expression of noncoding RNAs, especially microRNAs (miRNAs). MiRNAs are noncoding RNAs of single-stranded RNA molecules consisting of ∼22 nucleotides that regulate gene expression at the posttranscriptional level. MiRNAs are endogenous and potentially can regulate every aspect of cellular activity, including development and proliferation, differentiation, metabolism, viral infection, epigenetic modulation, apoptotic cell death, and tumor genesis. Recent studies provide evidence that miRNAs are abundant in the liver and affect a diverse spectrum of liver functions. MiRNA expression and deregulation of miRNAs may be a major pathogenetic factor in many liver diseases. Although global downregulation is a common trait in human malignancies, including viral hepatitis, hepatocellular carcinoma, and polycystic liver diseases, specific miRNAs are upregulated in cancer and offer new diagnostic and therapeutic strategies to manage liver diseases. Here, the current status of the role of miRNAs in liver cancer is discussed along with areas for future research.

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.

Proteomic profiling of hepatitis B virus-related hepatocellular carcinoma with magnetic bead-based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Proteomic techniques are promising strategies in the surveillance of hepatocellular carcinoma (HCC). This study aimed to investigate the serum profiling with magnetic bead (MB) and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) and to further identify the biomarkers for HCC. Serum samples from 80 chronic hepatitis B (CHB) patients, 94 HCC concomitant with HBV patients and 24 healthy subjects were examined by MALDI-TOF MS after peptide enrichment on MBs. Based on the genetic algorithm, diagnostic models for HCC were established between 30 HCC patients and 24 healthy subjects/30 CHB patients. Validations were done with the remaining cases. Markers in the models were identified through liquid chromatography (LC)/MS-MS. The three groups were well separated from each other and two discrimination models were established for HCC. The overall recognition capability of these two models was 96.25% and 93.33%, respectively. Validations showed the misdiagnosis ratio for HCC was 1.6% and 23.4%, respectively. The identified biomarkers for HCC included prothrombin precursor (fragment), calcium-dependent secretion activator 1, Baculoviral inhibitor of apoptosis  repeat-containing protein 6, etc. MB-based MALDI-TOF MS is applicable in identifying the serum biomarkers and can be used in the surveillance of HCC among HBV-infected patients.

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.