Promising Diagnostic Biomarkers for Primary Biliary Cirrhosis Identified With Magnetic Beads and MALDI-TOF-MS

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.

Prevalence and impact of Sjögren's syndrome in primary biliary cholangitis: a systematic review and meta-analysis

INTRODUCTION AND OBJECTIVES

We performed a systematic review and meta-analysis to evaluate the prevalence of concomitant Sjögren's syndrome (SS) with primary biliary cholangitis (PBC) in adults and quantify the impact of SS on PBC.

METHODS

PubMed, Web of Science and Cochrane library were searched using subject terms and predefined inclusion and exclusion criteria.

RESULTS

Seventeen articles were included. The prevalence of SS in PBC patients ranged from 3.5 to 73% (35% pooled) (95% CI: 28-41%; p &lt; 0.01). Seven studies included various biochemical indicators, including alanine transaminase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), γ-glutamyltransferase (γ-GT), total bilirubin (TBiL), albumin (ALB) and platelet (PLT), and immunological indexes including IgG, IgM, antinuclear antibody (ANA), anti-mitochondrial antibody (AMA), AMA-M2 and anti-Ro/Sjögren's syndrome antigen A (SSA) antibodies. Meta-analysis showed that there were no significant differences in ALT, AST, ALP, γ-GT, TBiL and IgM levels between PBS and PBC with SS. Pooled analysis showed that ALB (MD=0.82; 95% CI: 0.08-1.56) and PLT (MD=30.41; 95% CI: 10.16-50.66) levels were lower, IgG levels (MD=-1.55; 95% CI: -2.39 to -0.72) were higher, and the positive ratios of ANA (RR=0.92; 95% CI: 0.87-0.98), AMA (RR=0.94; 95% CI: 0.89-0.98), AMA-M2 (RR=0.77; 95% CI: 0.70-0.85) and anti-Ro/SSA antibodies (RR=0.29; 95% CI: 0.08-1.01) were significantly higher in PBC patients with SS than in PBC patients.

CONCLUSIONS

Our study confirms that SS is common in PBC. Comorbid SS appears to influence the clinical phenotype of PBC and may therefore influence the management of PBC.

Establishing Classification Tree Models in Rheumatoid Arthritis Using Combination of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry and Magnetic Beads

Background: There is no simple method for early diagnosis and evaluation of rheumatoid arthritis (RA). This study aimed to determine potential biomarkers and establish diagnostic patterns for RA using proteomic fingerprint technology combined with magnetic beads.

Methods: The serum protein profiles of 97 RA patients and 76 healthy controls (HCs) were analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) with weak cationic exchange (WCX) magnetic beads. Samples were randomly divided into training (83 RA patients and 56 HCs) and test sets (14 RA patients and 20 HCs). Patients were classified according to their Disease Activity Score: in remission, n = 28; with low disease activity, n = 17; with moderate disease activity, n = 21; with high disease activity, n = 31. There are 44 RA patients alone, 22 RA patients with interstitial lung disease (RA-ILD), 18 RA patients with secondary Sjögren's syndrome (RA-sSS), 6 RA patients with osteonecrosis of the femoral head (RA-ONFH), and 7 RA patients with other complications. Eleven patients were treated with etanercept only for half a year, after which their serum protein profiles were detected. The proteomic pattern was identified by Biomarker Patterns Software, and the potential biomarkers for RA diagnosis were further identified and quantified by enzyme-linked immunosorbent assay.

Results: The diagnostic pattern with four potential protein biomarkers, mass-to-charge (m/z) 3,448.85, 4,716.71, 8,214.29, and 10,645.10, could accurately recognize RA patients from HCs (specificity, 91.57%; sensitivity, 92.86%). The test set were correctly classified by this model (sensitivity, 95%; specificity, 100%). The components containing the four biomarkers were preliminarily retrieved through the ExPasy database, including the C-C motif chemokine 24 (CCL24), putative metallothionein (MT1DP), sarcolipin (SLN), and C-X-C motif chemokine 11 (CCXL11). Only the CCL24 level was detected to have a significant decrease in the serum of RA patients as compared with HCs (p < 0.05). No significant difference was found in others, but a decreasing trend consistent with the down-regulation of the four biomarkers detected by MALDI-TOF-MS was observed. The diagnostic models could effectively discriminate between RA alone and RA with complications (RA-ILD: m/z 10,645.10 and 12,595.86; RA-sSS: m/z 6,635.62 and 33,897.72; RA-ONFH: m/z 2,071.689). The classification model, including m/z 1,130.776, 1,501.065, 2,091.198, and 11,381.87, could distinguish between RA patients with disease activity and those in remission. RA with low disease activity could be efficiently discriminated from other disease activity patients by specific protein biomarkers (m/z 2,032.31, 2,506.214, and Z9286.495). Two biomarkers (m/z 2,032.31 and 4,716.71) were applied to build the classification model for RA patients with moderate and high disease activities. Biological markers for etanercept (m/z 2,671.604064, 5,801.840579, 8,130.195641, and 9,286.49499) were observed between the responder (n = 7) and non-responder groups (n = 4) (p < 0.05).

Conclusion: We successfully established a series of diagnostic models involving RA and RA with complications as well as assessed disease activity. Furthermore, we found that CCL24 may be a valuable auxiliary diagnostic indicator for RA. These results provide reference values for clinical practice in the future.

Novel approaches to liver disease diagnosis and modeling

Lack of a prompt and accurate diagnosis remains on top of the list of challenges faced by patients with rare liver diseases. Although rare liver diseases affect a significant percentage of the population as a group, when taken singularly they represent unique diseases and the approaches used for diagnosis of common liver diseases are insufficient. However, the development of new methods for the acquisition of molecular and clinical data (i.e., genomic, proteomics, metabolomics) and computational tools for their analysis and integration, together with advances in modeling diseases using stem cell-based technology [i.e., induced pluripotent stem cells (iPSCs) and tissue organoids] represent a promising and powerful tool to improve the clinical management of these patients. This is the goal of precision medicine, a novel approach of modern medicine that aims at delivering a specific treatment based on disease-specific biological insights and individual profile. This review will discuss the application and advances of these technologies and how they represent a new opportunity in hepatology.

Biomarkers for primary biliary cholangitis: current perspectives

Primary biliary cholangitis (PBC) is a chronic progressive cholestatic disease characterized by destruction of small- and medium-sized intrahepatic bile ducts. It is no longer a rare disease, since many new asymptomatic cases are incidentally identified. Liver biopsy is diagnostically critical but not always feasible or practical to be performed. Many potential, noninvasive, markers have been proposed to replace liver biopsy and further provide the assessment of disease severity and ultimate prognosis. In this review, we evaluated serum biomarkers proposed for diagnosis, extent of fibrosis, disease prognosis and attempts for early prediction of treatment response. Older biochemical and immunological markers are presented along with recent reports including the role of microRNAs and promising results based on proteomics and metabolomics.

Establishing serological classification tree model in rheumatoid arthritis using combination of MALDI-TOF-MS and magnetic beads

To establish a serological classification tree model for rheumatoid arthritis (RA), protein/peptide profiles of serum were detected by matrix-assisted laser desorption-ionization time-of-flight mass spectrometry (MALDI-TOF-MS) combined with weak cationic exchange (WCX) from Cohort 1, including 65 patients with RA and 41 healthy controls (HC). The samples were randomly divided into a training set and a test set. Twenty-four differentially expressed peaks (P < 0.05) were identified in the training set and 4 of them, namely m/z 3,939, 5,906, 8,146, and 8,569 were chosen to set up our model. This model exhibited a sensitivity of 100.0% and a specificity of 96.0% for differentiating RA patients from HC. The test set reproduced these high levels of sensitivity and specificity, which were 100.0 and 81.2%, respectively. Cohort 2, which include 228 RA patients, was used to further verify the classification efficiency of this model. It came out that 97.4% of them were classified as RA by this model. In conclusion, MALDI-TOF-MS combined with WCX magnetic beads was a powerful method for constructing a classification tree model for RA, and the model we established was useful in recognizing RA.

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.

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.

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.

Genomics and proteomics in liver fibrosis and cirrhosis

Genomics and proteomics have become increasingly important in biomedical science in the past decade, as they provide an opportunity for hypothesis-free experiments that can yield major insights not previously foreseen when scientific and clinical questions are based only on hypothesis-driven approaches. Use of these tools, therefore, opens new avenues for uncovering physiological and pathological pathways. Liver fibrosis is a complex disease provoked by a range of chronic injuries to the liver, among which are viral hepatitis, (non-) alcoholic steatohepatitis and autoimmune disorders. Some chronic liver patients will never develop fibrosis or cirrhosis, whereas others rapidly progress towards cirrhosis in a few years. This variety can be caused by disease-related factors (for example, viral genotype) or host-factors (genetic/epigenetic). It is vital to establish accurate tools to identify those patients at highest risk for disease severity or progression in order to determine who are in need of immediate therapies. Moreover, there is an urgent imperative to identify non-invasive markers that can accurately distinguish mild and intermediate stages of fibrosis. Ideally, biomarkers can be used to predict disease progression and treatment response, but these studies will take many years due to the requirement for lengthy follow-up periods to assess outcomes. Current genomic and proteomic research provides many candidate biomarkers, but independent validation of these biomarkers is lacking, and reproducibility is still a key concern. Thus, great opportunities and challenges lie ahead in the field of genomics and proteomics, which, if successful, could transform the diagnosis and treatment of chronic fibrosing liver diseases.

Primary biliary cirrhosis and Sjögren's syndrome: autoimmune epithelitis

Primary biliary cirrhosis (PBC) has been often coined a model autoimmune disease based on the homogeneity amongst patients, the frequency and similarity of antimitochondrial antibodies, including the highly directed immune response to pyruvate dehydrogenase (PDC-E2). A significant number of patients with PBC suffer from sicca and amongst these, there are patients who also have classic Sjögren's syndrome. Indeed, both PBC and Sjögren's syndrome are characterized by inflammation of target epithelial elements. Both diseases can be considered on the basis of a number of other related clinical aspects, including proposed unique apoptotic features of the target tissue, the role of secretory IgA, and the frequency with which both diseases overlap with each other. Indeed, PBC may be considered a Sjögren's syndrome of the liver, whereas Sjögren's syndrome can be equally discussed as PBC of the salivary glands. Dissection of the genetic predispositions for both diseases and especially the molecular basis of effector mechanisms, will become critical elements in developing new therapies.

Discovery of serum protein biomarkers in rheumatoid arthritis using MALDI-TOF-MS combined with magnetic beads

The aim of this study was to discover potential biomarkers for rheumatoid arthritis (RA) using Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with magnetic beads. Proteomic fingerprint technology combining magnetic beads with MALDI-TOF-MS was used to profile and compare the proteomes in serum samples from 60 patients with RA, 35 patients with osteoarthritis and 36 healthy controls. The proteomic pattern associated with RA was identified by Biomarker Patterns Software. Model of biomarkers was constructed and evaluated through the Biomarker Patterns Software. A total of 33 discriminative peaks were identified to be related with RA, in which the 5 peaks with the mass-charge ratio (m/z) peaks at 15,715.5, 7,771.4, 8,959.4, 8,469.8 and 8,710.8 Da were used to construct a model for the diagnosis of RA by pattern recognition software. The blind testing data indicated a sensitivity of 86.7% and a specificity of 90.0% in RA diagnosis. These results demonstrated that potential protein biomarkers for RA could be discovered in serum by MALDI-TOF-MS combined with WCX magnetic beads. The diagnosis mode tree based on the five candidate biomarkers could provide a powerful and reliable diagnostic method for RA with high sensitivity and specificity.

Establishing a serologic decision tree model of extrapulmonary tuberculosis by MALDI-TOF MS analysis

Matrix-assisted laser desorption-ionization time of flight mass spectrometry (MALDI-TOF MS) combined with weak cationic exchange (WCX) magnetic beads was used to establish a decision tree model that distinguished extrapulmonary tuberculosis (EPTB) from non-EPTB individuals. Eight-one patients with EPTB and 112 non-EPTB individuals (72 disease controls and 40 healthy controls) were involved in this study. The model was set up by 5 of 19 differentially expressed peaks (P < 0.05), m/z 4100, 4310, 6093, 8605, and 14,019. This model can discriminate patients with EPTB from non-EPTB with a sensitivity of 97.7% and a specificity of 84.1%. The test set verified that this model had good sensitivity and specificity: 94.4% and 83.6%, respectively. In conclusion, MALDI-TOF MS combined with WCX magnetic beads is a powerful technology for constructing a decision tree model and the model we built could serve as a potential diagnostic tool for EPTB.

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.

Identify biomarkers of neuropsychiatric systemic lupus erythematosus by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with weak cation magnetic beads

OBJECTIVE

To identify proteomic biomarkers in cerebrospinal fluid (CSF) and develop a diagnostic proteomic model for neuropsychiatric systemic lupus erythematosus (NPSLE).

METHODS

CSF proteomic spectra were generated by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) combined with weak cation exchange (WCX) magnetic beads. The spectra were taken from 27 patients with NPSLE before and after treatment, and 27 controls including 17 patients with scoliosis and 10 patients with SLE but without neuropsychiatric manifestation. Discriminating peaks were processed by Biomarker Patterns Software to build a decision tree model for NPSLE classification. In addition, CSF samples of 12 patients with NPSLE, 12 patients with lumbar disc herniation, and 9 patients with other neurological conditions were used as a blind test group to verify the accuracy of the model.

RESULTS

Twelve discriminating mass-to-charge (m/z) peaks were identified between NPSLE and controls: m/z peaks 7740, 11962, 8065, 7661, 6637, 5978, 11384, 11744, 8595, 10848, 7170, and 5806. The diagnostic decision tree model, built with a panel of m/z peaks 8595, 7170, 7661, 7740, and 5806, recognized NPSLE with both sensitivity and specificity of 92.6%, based on training group samples, and sensitivity and specificity of 91.7% and 85.7%, respectively, based on the blind test group. In addition, the root node m/z peak 8595 protein, which was downregulated in the CSF of patients with NPSLE after treatment, was identified and confirmed as ubiquitin by immunoprecipitation and ELISA.

CONCLUSION

Potential CSF biomarkers for NPSLE are identified by MALDI-TOF-MS combined with WCX magnetic beads. The novel diagnostic proteomic model with m/z peaks 8595, 7170, 7661, 7740, and 5806 is highly sensitive and relatively specific for NPSLE diagnosis. The level of ubiquitin in CSF is a promising biomarker for active NPSLE.