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

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.

Serum peptidomic screening identified circulating peptide biomarkers predictive for preeclampsia

Background

Reliable biomarkers are needed to improve preeclampsia (PE) prediction accuracy. With the investigational tool of peptidomics, we aimed to identify and validate potential serum peptide biomarkers in cohorts suspected for PE development in middle or late pregnancy.

Methods

Totally 195 serum samples were prospectively collected from pregnant women with PE-related syndromes who were followed up for PE development until delivery. Serum peptidomic analysis was performed in the discovery cohort of 115 samples using matrix-assisted laser desorption ionization-time of flight coupled with Linear Trap Quadropole Orbitrap mass spectrometry. The candidate biomarkers were further validated using an in-house developed liquid chromatography tandem mass spectrometry (LC-MS/MS) method in an independent validation cohort of 80 serum samples.

Results

We identified 8 peptides that were differentially expressed and originated from fibrinogen alpha chain (FGA), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4) and complement component 3. In the subsequent LC-MS/MS quantitation analysis, the levels of the three peptides (FGA-1033.4, ITIH4-2026.9, ITIH4-2051.1) exhibited a significant difference between the PE-positive and PE-negative groups. Further, the three-peptide panel yielded an area under the ROC curve (AUC) of 0.985 [95% confidence interval (CI) 0.965-1.000] and 0.923 (95% CI 0.845-1.000) in the discovery and validation cohorts respectively, with negative predictive values of 98.1-98.8% and positive predictive values of 73.1-85.3% that were much improved when compared with that of soluble fms-like tyrosine kinase-1/placental growth factor (sFlt-1/PlGF) ratio.

Conclusions

We have discovered and validated a novel three-peptide biomarker panel predictive for the occurrence PE in pregnant women.

Detection of Hepatocellular Carcinoma in a High-Risk Population by a Mass Spectrometry-Based Test

Simple Summary

Liver cancer is one of the most common causes of cancer worldwide, but unfortunately, current technology has a limited ability to detect it early in high-risk patients. This study investigates a machine learning algorithm based on protein levels in the blood that can be used to help with diagnosis. The test shows promising results, especially in patients with smaller tumors and compared to current blood detection tests. This research suggests an important role in the future for machine learning algorithm-based blood detection tests.

Abstract

Hepatocellular carcinoma (HCC) is one of the fastest growing causes of cancer-related death. Guidelines recommend obtaining a screening ultrasound with or without alpha-fetoprotein (AFP) every 6 months in at-risk adults. AFP as a screening biomarker is plagued by low sensitivity/specificity, prompting interest in discovering alternatives. Mass spectrometry-based techniques are promising in their ability to identify potential biomarkers. This study aimed to use machine learning utilizing spectral data and AFP to create a model for early detection. Serum samples were collected from three separate cohorts, and data were compiled to make Development, Internal Validation, and Independent Validation sets. AFP levels were measured, and Deep MALDI^® analysis was used to generate mass spectra. Spectral data were input into the VeriStrat^® classification algorithm. Machine learning techniques then classified each sample as “Cancer” or “No Cancer”. Sensitivity and specificity of the test were >80% to detect HCC. High specificity of the test was independent of cause and severity of underlying disease. When compared to AFP, there was improved cancer detection for all tumor sizes, especially small lesions. Overall, a machine learning algorithm incorporating mass spectral data and AFP values from serum samples offers a novel approach to diagnose HCC. Given the small sample size of the Independent Validation set, a further independent, prospective study is warranted.

Label-Free Electrochemical Immunosensor for Ultrasensitive Detection of Carbohydrate Antigen 125 Based on Antibody-Immobilized Biocompatible MOF-808/CNT

In this work, a nanocomposite of Zr-trimesic acid MOF (MOF-808) with carbon nanotube (CNT) was synthesized through an in situ formation of MOF-808 on the activated CNT. The synthesized materials were characterized by powder X-ray diffraction, transmission electron microscopy, X-ray photoluminescence spectroscopy, Brunauer-Emmett-Teller, Fourier transform infrared spectroscopy, and Raman spectroscopy. The protein compatible nature with high surface area and electrocatalytic ability of MOF-808 was utilized to construct an immunosensor for ultra low-level detection of the ovarian cancer biomarker, carbohydrate antigen 125 (CA 125). The mutual benefit of each constituent of the MOF-808/CNT composite was capable of producing highly enhanced electrochemical properties. A glassy carbon electrode modified with MOF-808/CNT was used as a platform to fabricate a label-free electrochemical immunosensor. The antibody binding sites of MOF-808/CNT were enriched by functionalization with streptavidin. The immunosensor exhibited two linear determination ranges of 0.001-0.1 and 0.1-30 ng·mL^-1, and the calculated limit of detection was 0.5 pg·mL^-1 (S/N 3). The immunosensor showed excellent reproducibility and selectivity. The patient serum sample analysis was cross-verified with the electrochemiluminescence method with a relative error of 105-110%.

Recent advancements in fabrication of nanomaterial based biosensors for diagnosis of ovarian cancer: a comprehensive review

Ovarian cancer is commonly diagnosed via determination of biomarkers like CA125, Mucin 1, HE4, and prostasin that can be present in the blood. However, there is a substantial need for less expensive, simpler, and portable diagnostic tools, both for timely diagnosis and management of ovarian cancer. This review (with 101 refs.) discusses various kinds of nanomaterial-based biosensors for tumor markers. Following an introduction into the field, a first section covers different kinds of biomarkers for ovarian cancer including CA125 (MUC16), mucin 1 (MUC1), human epididymis protein 4 (HE4), and prostasin. This is followed by a short overview on conventional diagnostic approaches. A large section is then presented on biosensors for determination of ovarian cancer, with subsections on optical biosensors (fluorimetric, colorimetric, surface plasmon resonance, chemiluminescence, electrochemiluminescence), on electrochemical sensors, molecularly imprinted sensors, paper-based biosensors, microfluidic (lab-on-a-chip) assays, chemiresistive and field effect transistor-based sensors, and giant magnetoresistive sensors. Tables are presented that give an overview on the wealth of methods and materials. A concluding section summarizes the current status, addresses current challenges, and gives an outlook on potential future trends. Graphical abstract Schematic representation of the review covering the advancements in the fabrication of various nanomaterial based biosensors for diagnosis of ovarian cancer.

Performance of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in diagnosis of ovarian cancer: a systematic review and meta-analysis

BACKGROUND

To evaluate the diagnostic performance of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for ovarian cancer.

PATIENTS AND METHODS

A thorough research was conducted in PubMed, Web of Science and Embase (until November 2018) to identify studies evaluating the accuracy of MALDI-TOF-MS for ovarian cancer. Using Meta-Disc1.4, Review Manager 5.3 and Stata 15.1 software to analyze the pooled results: sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and 95% confidence intervals (CI). The summary receiver operating characteristic curves (SROC) and area under the curve (AUC) show the overall performance of MALDI-TOF-MS.

RESULTS

Eighteen studies were included in the meta-analysis. Methodological quality analysis of the included studies showed that these articles were at low risk of bias and applicability concerns in total. Summary estimates of the diagnostic parameters were as follows: sensitivity, 0.77 (95% CI: 0.73-0.80); specificity, 0.72 (95% CI: 0.70-0.74), PLR, 2.80 (95% CI: 2.41-3.24); NLR, 0.30 (95% CI: 0.22-0.40) and DOR, 10.71 (95% CI: 7.81-14.68). And the AUC was 0.8336. Egger's test showed no significant publication bias in this meta-analysis.

CONCLUSION

In conclusion, MALDI-TOF-MS shows a good ability for diagnosing ovarian cancer. Further evaluation and optimization of standardized procedures are necessary for complete relying on MALDI-TOF-MS to diagnose ovarian cancer.

Detection of Epithelial Ovarian Cancer using C8Magnetic Bead Separation and MALDI-TOF Plasma Proteome Profiling in Egyptian Females

Background:

Ovarian cancer is the seventh most common cancer in females with the highest mortality rate of all gynecological cancers due to its late discovery and ambiguous symptoms. Thus, there is a need for new promising strategies to diagnose ovarian cancer. We aimed at finding a characteristic plasma proteome pattern that could be used for the detection of epithelial ovarian cancer, in comparison with benign ovarian masses and healthy controls. We also aimed at differentiating between profiling of plasma proteins in early and advanced stages of ovarian cancer and between serous and non-serous histopathological types.

Methods:

The combination of MagSi-proteomics C8 beads, Ultraflextreme MALDI-TOF and ClinPro Tools software was used to compare the plasma protein spectra from 50 patients with epithelial ovarian cancer, 20 patients with benign ovarian masses and 50 age matched healthy females.

Results:

A plasma proteome profile of 21 peaks differentiated patients with epithelial ovarian cancer from healthy controls with a sensitivity of 73 % and a specificity of 82.8% upon external validation, while a 5-peak profile differentiated patients with epithelial ovarian cancer from patients with benign ovarian masses with a sensitivity of 81% and a specificity of 73.7%. A 20 peak profile was generated to discriminate between early and late stages of the disease with 88.3% recognition capability and 70% cross validation.

Conclusion:

MALDI-TOF proteomic profiling represents a promising potential tool for diagnosing epithelial ovarian cancer, discriminating between early and advanced stages and between serous and non- serous types.

Mass spectrometry-based proteomics techniques and their application in ovarian cancer research

Ovarian cancer has emerged as one of the leading cause of gynecological malignancies. So far, the measurement of CA125 and HE4 concentrations in blood and transvaginal ultrasound examination are essential ovarian cancer diagnostic methods. However, their sensitivity and specificity are still not sufficient to detect disease at the early stage. Moreover, applied treatment may appear to be ineffective due to drug-resistance. Because of a high mortality rate of ovarian cancer, there is a pressing need to develop innovative strategies leading to a full understanding of complicated molecular pathways related to cancerogenesis. Recent studies have shown the great potential of clinical proteomics in the characterization of many diseases, including ovarian cancer. Therefore, in this review, we summarized achievements of proteomics in ovarian cancer management. Since the development of mass spectrometry has caused a breakthrough in systems biology, we decided to focus on studies based on this technique. According to PubMed engine, in the years 2008-2010 the number of studies concerning OC proteomics was increasing, and since 2010 it has reached a plateau. Proteomics as a rapidly evolving branch of science may be essential in novel biomarkers discovery, therapy decisions, progression predication, monitoring of drug response or resistance. Despite the fact that proteomics has many to offer, we also discussed some limitations occur in ovarian cancer studies. Main difficulties concern both complexity and heterogeneity of ovarian cancer and drawbacks of the mass spectrometry strategies. This review summarizes challenges, capabilities, and promises of the mass spectrometry-based proteomics techniques in ovarian cancer management.

Ultrasensitive flexible FET-type aptasensor for CA 125 cancer marker detection based on carboxylated multiwalled carbon nanotubes immobilized onto reduced graphene oxide film

The development of a novel flexible and ultrasensitive aptasensor based on carboxylated multiwalled carbon nanotubes (MWCNTs)/ reduced graphene oxide-based field effect transistor (FET) has been reported for label-free detection of the ovarian cancer antigen (CA125). The fabricated sensor has a straightforward design based on the noncovalent attachment of MWCNTs/aptamer conjugated onto few layers reduced graphene oxide nanosheets and its integration with poly-methyl methacrylate (PMMA) as a suitable platform for designing flexible field-effect transistors. The surface properties of the aptasensor were characterized using scanning electron microscopy (SEM), transmission electron microscopy (TEM) and atomic force microscopy (AFM). Under optimal conditions, the proposed aptasensor exhibited a wide linear dynamic range for CA125 (1.0 × 10^-9-1.0 U/mL) with a low detection limit of 5.0 × 10^-10 U/mL. The proposed aptasensor was also successfully applied to detect CA125 in real human serum samples. Furthermore, sensor flexibility is also a challenging area in chemical and biological sensors, especially for portable, wearable, or even implantable sensors, so, the reduced graphene oxide-based FET-type aptasensor showed bendable flexibility on the PMMA substrate. In addition, the aptasensor exhibited high sensitivity, selectivity, stability and reproducibility which offers great promise as a high performance and flexible FET-type aptasensor to detect CA125 and other cancer biomarkers in clinical samples and biological fluids.

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.

Weak cation exchange magnetic beads coupled with matrix-assisted laser desorption ionization-time of flight-mass spectrometry in screening serum protein markers in osteopenia

The present study aimed at investigating the weak cation magnetic separation technology and matrix-assisted laser desorption ionization-time of flight-mass spectrometry (MALDI-TOF-MS) in screening serum protein markers of osteopenia from ten postmenopausal women and ten postmenopausal women without osteopenia as control group, to find a new method for screening biomarkers and establishing a diagnostic model for primary type I osteoporosis. Serum samples were collected from postmenopausal women with osteopenia and postmenopausal women with normal bone mass. Proteins were extracted from serum samples by weak cation exchange magnetic beads technology, and mass spectra acquisition was done by MALDI-TOF-MS. The visualization and comparison of data sets, statistical peak evaluation, model recognition, and discovery of biomarker candidates were handled by the proteinchip data analysis system software(ZJU-PDAS). The diagnostic models were established using genetic arithmetic based support vector machine (SVM). The SVM result with the highest Youden Index was selected as the model. Combinatorial Peaks having the highest accuracy in distinguishing different samples were selected as potential biomarker. From the two group serum samples, a total of 133 differential features were selected. Ten features with significant intensity differences were screened. In the pair-wise comparisons, processing of MALDI-TOF spectra resulted in the identification of ten differential features between postmenopausal women with osteopenia and postmenopausal women with normal bone mass. The difference of features by Youden index showed that the highest features had a mass to charge ratio of 1699 and 3038 Da. A diagnosis model was established with these two peaks as the candidate marker, and the specificity of the model is 100 %, the sensitivity was 90 % by leave-one-out cross validation test. The two groups of specimens in SVM results on the scatter plot could be clearly distinguished. The peak with m/z 3038 in the SVM model was suggested as Secretin by TagIdent tool. To provide further validation, the secretin levels in serum were analyzed using enzyme-linked immunosorbent assays that is a competitive inhibition enzyme immunoassay technique for the in vitro quantitative measurement of secretin in human serum.

Applicability of 2D gel electrophoresis and liquid chromatography in proteomic analysis of urine using mass spectrometry MALDI-TOF

Proteomics including the studies of the structure, function and dependences between proteins is more and more extensively applied in human medicine and veterinary medicine. The analysis of protein profiles of tissues and body fluid from healthy and ill individuals allows to identify diagnostic, prognostic and predictive markers in various pathological states in people and animals. This paper presents preparation of urine samples for analysis in the mass spectrometer MALDI-TOF (Ultraflextreme, Bruker, Bremen, Germany) by means of two methods: liquid chromatography based on the system Nano-LC (PROTEINER FC II, Bruker Daltonics, Bremen Germany). and two-direction electrophoresis 2DE (GE Healthcare, United Kingdom). Both methods enable separation of the mixture under consideration into individual fractions of high purity indispensable for obtaining readable mass spectra. The purpose of this paper is to determine applicability of these methods in analysis of protein composition of urine samples.

A novel magnet focusing plate for matrix-assisted laser desorption/ionization analysis of magnetic bead-bound analytes

RATIONALE

Magnetic beads are often used for serum profiling of peptide and protein biomarkers. In these assays, the bead-bound analytes are eluted from the beads prior to mass spectrometric analysis. This study describes a novel matrix-assisted laser desorption/ionization (MALDI) technique for direct application and focusing of magnetic beads to MALDI plates by means of dedicated micro-magnets as sample spots.

METHODS

Custom-made MALDI plates with magnetic focusing spots were made using small nickel-coated neodymium micro-magnets integrated into a stainless steel plate in a 16 × 24 (384) pattern. For demonstrating the proof-of-concept, commercial C-18 magnetic beads were used for the extraction of a test compound (reserpine) from aqueous solution. Experiments were conducted to study focusing abilities, the required laser energies, the influence of a matrix compound, dispensing techniques, solvent choice and the amount of magnetic beads.

RESULTS

Dispensing the magnetic beads onto the micro-magnet sample spots resulted in immediate and strong binding to the magnetic surface. Light microscope images illustrated the homogeneous distribution of beads across the surfaces of the magnets, when the entire sample volume containing the beads was pipetted onto the surface. Subsequent MALDI analysis of the bead-bound analyte demonstrated excellent and reproducible ionization yields. The surface-assisted laser desorption/ionization (SALDI) properties of the strongly light-absorbing γ-Fe2O3-based beads resulted in similar ionization efficiencies to those obtained from experiments with an additional MALDI matrix compound.

CONCLUSIONS

This feasibility study successfully demonstrated the magnetic focusing abilities for magnetic bead-bound analytes on a novel MALDI plate containing small micro-magnets as sample spots. One of the key advantages of this integrated approach is that no elution steps from magnetic beads were required during analyses compared with conventional bead experiments.

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.