Profiling of Serum Metabolites Using MALDI-TOF and Triple-TOF Mass Spectrometry to Develop a Screen for Ovarian Cancer

Applications of MALDI-MS/MS-Based Proteomics in Biomedical Research

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) is one of the most widely used techniques in proteomics to achieve structural identification and characterization of proteins and peptides, including their variety of proteoforms due to post-translational modifications (PTMs) or protein–protein interactions (PPIs). MALDI-MS and MALDI tandem mass spectrometry (MS/MS) have been developed as analytical techniques to study small and large molecules, offering picomole to femtomole sensitivity and enabling the direct analysis of biological samples, such as biofluids, solid tissues, tissue/cell homogenates, and cell culture lysates, with a minimized procedure of sample preparation. In the last decades, structural identification of peptides and proteins achieved by MALDI-MS/MS helped researchers and clinicians to decipher molecular function, biological process, cellular component, and related pathways of the gene products as well as their involvement in pathogenesis of diseases. In this review, we highlight the applications of MALDI ionization source and tandem approaches for MS for analyzing biomedical relevant peptides and proteins. Furthermore, one of the most relevant applications of MALDI-MS/MS is to provide “molecular pictures”, which offer in situ information about molecular weight proteins without labeling of potential targets. Histology-directed MALDI-mass spectrometry imaging (MSI) uses MALDI-ToF/ToF or other MALDI tandem mass spectrometers for accurate sequence analysis of peptide biomarkers and biological active compounds directly in tissues, to assure complementary and essential spatial data compared with those obtained by LC-ESI-MS/MS technique.

Circulating proteins as predictive and prognostic biomarkers in breast cancer

Breast cancer (BC) is the most common cancer and among the leading causes of cancer death in women. It is a heterogeneous group of tumours with numerous morphological and molecular subtypes, making predictions of disease evolution and patient outcomes difficult. Therefore, biomarkers are needed to help clinicians choose the best treatment for each patient. For the last years, studies have increasingly focused on biomarkers obtainable by liquid biopsy. Circulating proteins (from serum or plasma) can be used for inexpensive and minimally invasive determination of disease risk, early diagnosis, treatment adjusting, prognostication and disease progression monitoring. We provide here a review of the main published studies on serum proteins in breast cancer and elaborate on the potential of circulating proteins to be predictive and/or prognostic biomarkers in breast cancer.

Methylglyoxal Adducts Levels in Blood Measured on Dried Spot by Portable Near-Infrared Spectroscopy

The altered glucose metabolism characterising cancer cells determines an increased amount of methylglyoxal in their secretome. Previous studies have demonstrated that the methylglyoxal, in turn, modifies the protonation state (PS) of soluble proteins contained in the secretomes of cultivated circulating tumour cells (CTCs). In this study, we describe a method to assess the content of methylglyoxal adducts (MAs) in the secretome by near-infrared (NIR) portable handheld spectroscopy and the extreme learning machine (ELM) algorithm. By measuring the vibration absorption functional groups containing hydrogen, such as C-H, O-H and N-H, NIR generates specific spectra. These spectra reflect alterations of the energy frequency of a sample bringing information about its MAs concentration levels. The algorithm deciphers the information encoded in the spectra and yields a quantitative estimate of the concentration of MAs in the sample. This procedure was used for the comparative analysis of different biological fluids extracted from patients suspected of having cancer (secretome, plasma, serum, interstitial fluid and whole blood) measured directly on the solute left on a surface upon a sample-drop cast and evaporation, without any sample pretreatment. Qualitative and quantitative regression models were built and tested to characterise the different levels of MAs by ELM. The final model we selected was able to automatically segregate tumour from non-tumour patients. The method is simple, rapid and repeatable; moreover, it can be integrated in portable electronic devices for point-of-care and remote testing of patients.

Metabolomic Biomarkers in Gynecology: A Treasure Path or a False Path?

Omic-technologies (genomics, transcriptomics, proteomics and metabolomics) have become more important in current medical science. Among them, it is metabolomics that most accurately reflects the minor changes in body functioning, as it focuses on metabolome - the group of the metabolism products, both intermediate and end. Therefore, metabolomics is actively engaged in fundamental and clinical studies and search for potential biomarkers. The biomarker could be used in diagnostics, management and stratification of the patients, as well as in prognosing the outcomes. The good example is gynecology, since many gynecological diseases lack effective biomarkers. In the current review, we aimed to summarize the results of the studies, devoted to the search of potential metabolomic biomarkers for the most common gynecological diseases.

Exploring serum metabolic markers for the discrimination of ccRCC from renal angiomyolipoma by metabolomics

Aim: The discrimination of renal cell carcinoma from renal angiomyolipoma (RAML) is crucial for the effective treatment of each. Materials & methods: Serum samples were analyzed by nuclear magnetic resonance spectroscopy-based metabolomics and a number of metabolites were further quantified by HPLC-UV. Results: Clear-cell renal carcinoma (ccRCC) was characterized by drastic disruptions in energy, amino acids, creatinine and uric acid metabolic pathways. A logistic model for the differential diagnosis of RAML from ccRCC was established using the combination of serum levels of uric acid, the ratio of uric acid to hypoxanthine and the ratio of hypoxanthine to creatinine as variables with area under the curve of the receiver operating characteristic curve value of 0.907. Conclusion: Alterations in serum purine metabolites may be used as potential metabolic markers for the differential diagnosis of ccRCC and RAML.

Comparative cerebrospinal fluid metabolites profiling in glioma patients to predict malignant transformation and leptomeningeal metastasis with a potential for preventive personalized medicine

Glioma shows progression presenting as malignant transformation or leptomeningeal metastasis (LM). However, longitudinal biopsy of brain parenchyma is difficult due to its critical location, whereas cerebrospinal fluid (CSF) can be obtained serially with a little invasiveness of puncture. Thus, if we could find a biomarker for glioma progression, we could predict such event and determine therapeutic interventions as early as possible. In this study, we examined whether cerebrospinal fluid (CSF) metabolome profiles can reflect glioma grade, difference with non-glial tumor, and LM status. We selected 32 CSF samples from glioma patients, and compared them with 10 non-tumor control and seven non-glial brain tumor (medulloblastoma) samples. A total of 10,408 low-mass ions (LMIs) were detected as a candidate of metabolites using mass spectrometry, and representative LMIs were identified via the Human Metabolome Database. Grade IV gliomas showed eight LMIs, including acetic acid, of higher levels (summed sensitivity and specificity > 180%) than grade III gliomas. Grade IV gliomas demonstrated more abundant 30 LMIs, including glycerophosphate, compared with medulloblastoma, but none was mutually exclusive. Phospholipid derivatives were significantly more abundant in LM (-) than LM (+) gliomas regardless of glioma grade. LMIs representative of LM (+) gliomas were derivatives of glycolysis. We also verified discriminative LMIs based on mean expression level of each LMI (Student t test, p < 0.05) and evaluated the differences of the above analyses. Over 90% of metabolite pathways indicated from two analytical models were common to each other. Non-targeted mass spectrometry of CSF metabolites revealed significantly different profiles across gliomas that possibly permitted differentiation between glioma grades, LM, and non-glial brain tumors.

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.

Identification of New Peptide Biomarkers for Bacterial Bloodstream Infection

PURPOSE

Due to a lack of effective early diagnostic measures, new diagnostic methods for bacterial bloodstream infections (BSIs) are urgently needed. A protein-peptide profiling approach can be used to identify novel diagnostic biomarkers of BSIs.

EXPERIMENTAL DESIGN

In this study, MALDI-TOF MS and nano-LC/ESI-MS/MS are used to analyze serum peptides. In addition, GO and network analyses are conducted as a means of analyzing these potential protein markers. Finally, the potential biomarkers are verified in independent clinical samples via ELISA.

RESULTS

m/z 1533.8, 2794.3, 3597.3, 5007.3, and 7816.7 reveal an identical trend; the intensity of m/z 1533.8, 2794.3, and 3597.3 are higher in the infection group relative to controls, whereas the intensity of m/z 5007.3 and 7816.7 are lower in the infection group. Four peaks are successfully identified including ITIH4, KNG1, SAA2, and C3. GO and network analyses find these proteins to form an interaction network, which may be correlated with BSI. ELISA results indicate that ITIH4, KNG1, and SAA2 are effective in differentiating infected from normal control group and the febrile group.

CONCLUSIONS AND CLINICAL RELEVANCE

These biomarkers have the potential to offer new insights into the signaling networks underlying the development and progression of BSI.