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

MALDI-TOF MS: A Promising Analytical Approach to Cancer Diagnostics and Monitoring

Cancer remains the second most common cause of death after cardiovascular diseases, accounting for nearly 10 million deaths in 2020. Although the incidence of cancer increases considerably with age, the cancer burden can also be reduced and have a high chance of cure through early detection, appropriate treatment, and care of patients. The development of high-throughput analytical approaches, like matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), contributes to identifying a pool of proteins/peptides as putative biomarkers for the early detection, diagnosis, and tumor progression. The purpose of the current review is to present an updated outline of recent proteome/peptidome research to establish putative cancer biomarkers using MALDI-TOF MS and highlight the applicability of statistical analysis in the oncology field. The pros and cons of MALDI-TOF MS application on cancer diagnostics and monitoring will be discussed, as well as compared with tandem mass spectrometry (MS/MS)-based proteomics (e.g., liquid chromatography–tandem mass spectrometry). In addition, pre-analytical (e.g., sample quality control) and analytical (e.g., sample pre-treatment, instrumental analytical conditions) properties that influence the robustness of MALDI-TOF MS data will be also discussed.

Probe electrospray ionization mass spectrometry-based rapid diagnosis of liver tumors

BACKGROUND AND AIM

Prompt differential diagnosis of liver tumors is clinically important and sometimes difficult. A new diagnostic device that combines probe electrospray ionization-mass spectrometry (PESI-MS) and machine learning may help provide the differential diagnosis of liver tumors.

METHODS

We evaluated the diagnostic accuracy of this new PESI-MS device using tissues obtained and stored from previous surgically resected specimens. The following cancer tissues (with collection dates): hepatocellular carcinoma (HCC, 2016-2019), intrahepatic cholangiocellular carcinoma (ICC, 2014-2019), and colorectal liver metastasis (CRLM, 2014-2019) from patients who underwent hepatic resection were considered for use in this study. Non-cancerous liver tissues (NL) taken from CRLM cases were also incorporated into the analysis. Each mass spectrum provided by PESI-MS was tested using support vector machine, a type of machine learning, to evaluate the discriminatory ability of the device.

RESULTS

In this study, we used samples from 91 of 139 patients with HCC, all 24 ICC samples, and 103 of 202 CRLM samples; 80 NL from CRLM cases were also used. Each mass spectrum was obtained by PESI-MS in a few minutes and was evaluated by machine learning. The sensitivity, specificity, and diagnostic accuracy of the PESI-MS device for discriminating HCC, ICC, and CRLM from among a mix of all three tumors and from NL were 98.9%, 98.1%, and 98.3%; 87.5%, 93.1%, and 92.6%; and 99.0%, 97.9%, and 98.3%, respectively.

CONCLUSION

This study demonstrated that PESI-MS and machine learning could discriminate liver tumors accurately and rapidly.

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.

ITIH4 is a novel serum biomarker for early gastric cancer diagnosis

BACKGROUND

Gastric cancer (GC) is one of the most lethal forms of cancer due to the absence of tools for its early detection. Here, we explored critical biomarkers for early diagnosis.

MATERIALS AND METHODS

Key biomarkers in serum from patients with early gastric cancer (EGC) and healthy controls (HCs) were identified via mass spectrometry and the expression of inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4) was evaluated using several methods. Furthermore, ITIH4 expression in sera and exosomes from patients with EGC, advanced GC (AGC), low grade intraepithelial neoplasia (LGN), chronic superficial gastritis with Helicobacter pylori infection (Hpi), other systemic malignant tumors (OSTs), and healthy controls was also evaluated.

RESULTS

ITIH4 was identified as a key biomarker in patients with EGC. Its expression level in serum from the EGC group, which showed the highest specificity (94.44%), was significantly higher than those in sera from other GC groups as well as the control. Western blot analysis, immunohistochemical staining, and exosome analysis also confirmed ITIH4 expression in sera from patients with GC, but not in those from healthy individual.

CONCLUSION

ITIH4 is a key biomarker in serum from patients with EGC and has potential as a high value diagnostic marker for EGC.

Pathophysiological Implications of Urinary Peptides in Hepatocellular Carcinoma

Simple Summary

In this study, the application of capillary electrophoresis mass spectrometry enabled identification of 31 urinary peptides significantly associated with hepatocellular carcinoma diagnosis and prognosis. Further assessment of these peptides lead to prediction of cellular proteases involved in their development namely Meprin A subunit α and Kallikrein-6. Subsequent identification of the proteases was verified by immunohistochemistry in normal liver, cirrhosis and hepatocellular carcinoma. Histopathological assessment of the proteases revealed numerical gradient staining signifying their involvement in liver fibrosis and hepatocellular carcinoma formation. The discovered urinary peptides offered a potential noninvasive tool for diagnosis and prognosis of hepatocellular carcinoma.

Abstract

Hepatocellular carcinoma (HCC) is known to be associated with protein alterations and extracellular fibrous deposition. We investigated the urinary proteomic profiles of HCC patients in this prospective cross sectional multicentre study. 195 patients were recruited from the UK (Coventry) and Germany (Hannover) between 1 January 2013 and 30 June 2019. Out of these, 57 were HCC patients with a background of liver cirrhosis (LC) and 138 were non-HCC controls; 72 patients with LC, 57 with non-cirrhotic liver disease and 9 with normal liver function. Analysis of the urine samples was performed by capillary electrophoresis (CE) coupled to mass spectrometry (MS). Peptide sequences were obtained and 31 specific peptide markers for HCC were identified and further integrated into a multivariate classification model. The peptide model demonstrated 79.5% sensitivity and 85.1% specificity (95% CI: 0.81–0.93, p < 0.0001) for HCC and 4.1-fold increased risk of death (95% CI: 1.7–9.8, p = 0.0005). Proteases potentially involved in HCC progression were mapped to the N- and C-terminal sequence motifs of the CE-MS peptide markers. In silico protease prediction revealed that kallikrein-6 (KLK6) elicits increased activity, whilst Meprin A subunit α (MEP1A) has reduced activity in HCC compared to the controls. Tissue expression of KLK6 and MEP1A was subsequently verified by immunohistochemistry.

A Fibrinogen Alpha Fragment Mitigates Chemotherapy-Induced MLL Rearrangements

Rearrangements in the Mixed Lineage Leukemia breakpoint cluster region (MLLbcr) are frequently involved in therapy-induced leukemia, a severe side effect of anti-cancer therapies. Previous work unraveled Endonuclease G as the critical nuclease causing initial breakage in the MLLbcr in response to different types of chemotherapeutic treatment. To identify peptides protecting against therapy-induced leukemia, we screened a hemofiltrate-derived peptide library by use of an enhanced green fluorescent protein (EGFP)-based chromosomal reporter of MLLbcr rearrangements. Chromatographic purification of one active fraction and subsequent mass spectrometry allowed to isolate a C-terminal 27-mer of fibrinogen α encompassing amino acids 603 to 629. The chemically synthesized peptide, termed Fα27, inhibited MLLbcr rearrangements in immortalized hematopoietic cells following treatment with the cytostatics etoposide or doxorubicin. We also provide evidence for protection of primary human hematopoietic stem and progenitor cells from therapy-induced MLLbcr breakage. Of note, fibrinogen has been described to activate toll-like receptor 4 (TLR4). Dissecting the Fα27 mode-of action revealed association of the peptide with TLR4 in an antagonistic fashion affecting downstream NFκB signaling and pro-inflammatory cytokine production. In conclusion, we identified a hemofiltrate-derived peptide inhibitor of the genome destabilizing events causing secondary leukemia in patients undergoing chemotherapy.

Serum proteomic predicts effectiveness and reveals potential biomarkers for complications in liver transplant patients

Sophisticated postoperative complications limit the long-term clinical success of liver transplantation. Hence, early identification of biomarkers is essential for graft and patient survival. High-throughput serum proteomics technologies provide an opportunity to identify diagnostic and prognostic biomarkers. This study is aimed to identify serum diagnosis biomarkers for complications and monitor effectiveness. Serum samples from 10 paired pre- and post-liver transplant patients, 10 acute rejection (AR) patients, 9 ischemic-type biliary lesion (ITBL) patients, and 10 healthy controls were screened using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to explore divergence in polypeptide. Then, we used ELISA and western blot analysis to validate the expression of these potential biomarkers, and studied the correlation of proteomic profiles with clinical parameters. ACLY, FGA, and APOA1 were significantly lower in pre-operative patients compared with healthy controls, and these patients had modest recovery after transplantation. Downregulation of both, ACLY and FGA, was also observed in AR and ITBL patients. Furthermore, bioinformatics analysis was performed and the results suggested that the identified proteins were involved in glucolipid metabolism and the clotting cascade. Together, these findings suggest that ACLY, FGA, and APOA1 could be novel non-invasive and early biomarkers to detect complications and predict effectiveness of liver transplantation.

Learning a single-hidden layer feedforward neural network using a rank correlation-based strategy with application to high dimensional gene expression and proteomic spectra datasets in cancer detection

Methods based on microarrays (MA), mass spectrometry (MS), and machine learning (ML) algorithms have evolved rapidly in recent years, allowing for early detection of several types of cancer. A pitfall of these approaches, however, is the overfitting of data due to large number of attributes and small number of instances -- a phenomenon known as the 'curse of dimensionality'. A potentially fruitful idea to avoid this drawback is to develop algorithms that combine fast computation with a filtering module for the attributes. The goal of this paper is to propose a statistical strategy to initiate the hidden nodes of a single-hidden layer feedforward neural network (SLFN) by using both the knowledge embedded in data and a filtering mechanism for attribute relevance. In order to attest its feasibility, the proposed model has been tested on five publicly available high-dimensional datasets: breast, lung, colon, and ovarian cancer regarding gene expression and proteomic spectra provided by cDNA arrays, DNA microarray, and MS. The novel algorithm, called adaptive SLFN (aSLFN), has been compared with four major classification algorithms: traditional ELM, radial basis function network (RBF), single-hidden layer feedforward neural network trained by backpropagation algorithm (BP-SLFN), and support vector-machine (SVM). Experimental results showed that the classification performance of aSLFN is competitive with the comparison models.

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.

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.