Identification of novel biomarkers of hepatocellular carcinoma by high-definition mass spectrometry: Ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry and desorption electrospray ionization mass spectrometry imaging

Medium-chain and long-chain fatty acids are associated with diarrheal predominant irritable bowel syndrome revealed by DESI-MSI

BACKGROUND

Irritable bowel syndrome (IBS) is one of the most common functional bowel disorders, but its pathogenesis remains unknown. Its development may be linked to intestinal dysmetabolism, directly and indirectly. The present study aimed to screen the differentially expressed small molecular substances in the mucosa of the colon between IBS with diarrhea (IBS-D) patients and healthy subjects and explore the pathogenesis of IBS-D.

METHODS

In this pilot study, the metabolites of colonic mucosa in ten patients with IBS-D and six healthy controls (HC) were analyzed by DESI-MSI. We also mapped the spatial distribution of the screened differential metabolites from samples of the IBS-D group and HC group.

RESULTS

The results showed that 20 metabolites in the colonic mucosa of IBS-D were significantly more abundant, while the other 77 substances were significantly reduced. Enrichment analysis of 97 differential metabolites and KEGG pathway analysis revealed that six medium-chain and long-chain fatty acids were determined to be most overrepresented in the IBS-D group compared to the HC group. Four of these six fatty acids are all PUFAs. The DESI-MSI results suggested that these fatty acids were localized in the colonic mucosa and confirmed the differences in these fatty acids between IBS-D and HC.

CONCLUSIONS

Medium-chain and long-chain fatty acids localized in the colonic mucosa are likely to be potential indicators for the differentiation of IBS-D from healthy subjects which may have implications in the mechanisms and possible preventive measures against IBS.

CLINICAL TRIAL REGISTRY REGISTRATION NUMBER

ChiCTR2200060224.

The Role of Mass Spectrometry in Hepatocellular Carcinoma Biomarker Discovery

Hepatocellular carcinoma (HCC) is the main liver malignancy and has a high mortality rate. The discovery of novel biomarkers for early diagnosis, prognosis, and stratification purposes has the potential to alleviate its disease burden. Mass spectrometry (MS) is one of the principal technologies used in metabolomics, with different experimental methods and machine types for different phases of the biomarker discovery process. Here, we review why MS applications are useful for liver cancer, explain the MS technique, and briefly summarise recent findings from metabolomic MS studies on HCC. We also discuss the current challenges and the direction for future research.

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.

Nrf2 plays a critical role in the metabolic response during and after spaceflight

Space travel induces stresses that contribute to health problems, as well as inducing the expression of Nrf2 (NF-E2-related factor-2) target genes that mediate adaptive responses to oxidative and other stress responses. The volume of epididymal white adipose tissue (eWAT) in mice increases during spaceflight, a change that is attenuated by Nrf2 knockout. We conducted metabolome analyses of plasma from wild-type and Nrf2 knockout mice collected at pre-flight, in-flight and post-flight time points, as well as tissues collected post-flight to clarify the metabolic responses during and after spaceflight and the contribution of Nrf2 to these responses. Plasma glycerophospholipid and sphingolipid levels were elevated during spaceflight, whereas triacylglycerol levels were lower after spaceflight. In wild-type mouse eWAT, triacylglycerol levels were increased, but phosphatidylcholine levels were decreased, and these changes were attenuated in Nrf2 knockout mice. Transcriptome analyses revealed marked changes in the expression of lipid-related genes in the liver and eWAT after spaceflight and the effects of Nrf2 knockout on these changes. Based on these results, we concluded that space stress provokes significant responses in lipid metabolism during and after spaceflight; Nrf2 plays critical roles in these responses.

Application of metabolomics in clinical and laboratory gastrointestinal oncology

Metabolites are versatile bioactive molecules. They are not only the substrates and/or the products of enzymatic reactions but also act as the regulators in the systemic metabolism. Metabolomics is a high-throughput analytical strategy to qualify or quantify as many metabolites as possible in the metabolomes. It is an indispensable part of systems biology. The leading techniques in this field are mainly based on mass spectrometry and nuclear magnetic resonance spectroscopy. The metabolomic analysis has gained wide use in bioscience fields. In the tumor research arena, metabolomics can be employed to identify biomarkers for prediction, diagnosis, and prognosis. Chemotherapeutic effect evaluation and personalized medicine decision-making can also benefit from metabolomic analysis of patient biofluid or biopsy samples. Many cell-level studies can help in disease exploration. In this review, the basic features and principles of varied metabolomic analysis are introduced. The value of metabolomics in clinical and laboratory gastrointestinal cancer studies is discussed, especially for mass spectrometry applications. Besides, combined use of metabolomics and other tools to solve problems in cancer practice is briefly illustrated. In summary, metabolomics paves a new way to explore cancerous diseases in the light of small molecules.

MiR-139-5p influences hepatocellular carcinoma cell invasion and proliferation capacities via decreasing SLITRK4 expression

The microRNA, miR-139-5p, has been proved to play important roles in regulating tumor progression, including prostate cancer, osteosarcoma, esophageal cancer, and so on, but its correlation of hepatocellular carcinoma (HCC) still remains unclear. Here we found that hsa-miR-139-5p (miR-139-5p) was decreased in HCC samples compared with normal liver tissues, and a lower expression of miR-139-5p was connected to a poorer prognosis. Mechanism study indicated that a decreased/increased miR-139-5p could increase/decrease HCC cells invasion and proliferation capacities via increasing SLITRK4 expression, what’s more, the reverse assays also confirmed the conclusion when we knocked down SLITRK4 in the miR-139-5p low-expression cells. Luciferase assay confirmed that miR-139-5p could directly bind to the 3′UTR of SLITRK4 mRNA to regulate its expression. Together, these findings show the importance of miR-139-5p/SLITRK4 pathway in HCC growth and progression and may provide new targets for us to better arrange the progression of HCC.

Real-time lipid patterns to classify viable and necrotic liver tumors

Real-time tissue classifiers based on molecular patterns are emerging tools for fast tumor diagnosis. Here, we used rapid evaporative ionization mass spectrometry (REIMS) and multivariate statistical analysis (principal component analysis-linear discriminant analysis) to classify tissues with subsequent comparison to gold standard histopathology. We explored whether REIMS lipid patterns can identify human liver tumors and improve the rapid characterization of their underlying metabolic features. REIMS-based classification of liver parenchyma (LP), hepatocellular carcinoma (HCC), and metastatic adenocarcinoma (MAC) reached an accuracy of 98.3%. Lipid patterns of LP were more similar to those of HCC than to those of MAC and allowed clear distinction between primary and metastatic liver tumors. HCC lipid patterns were more heterogeneous than those of MAC, which is consistent with the variation seen in the histopathological phenotype. A common ceramide pattern discriminated necrotic from viable tumor in MAC with 92.9% accuracy and in other human tumors. Targeted analysis of ceramide and related sphingolipid mass features in necrotic tissues may provide a new classification of tumor cell death based on metabolic shifts. Real-time lipid patterns may have a role in future clinical decision-making in cancer precision medicine.

Multimodal Imaging Mass Spectrometry: Next Generation Molecular Mapping in Biology and Medicine

Imaging mass spectrometry has become a mature molecular mapping technology that is used for molecular discovery in many medical and biological systems. While powerful by itself, imaging mass spectrometry can be complemented by the addition of other orthogonal, chemically informative imaging technologies to maximize the information gained from a single experiment and enable deeper understanding of biological processes. Within this review, we describe MALDI, SIMS, and DESI imaging mass spectrometric technologies and how these have been integrated with other analytical modalities such as microscopy, transcriptomics, spectroscopy, and electrochemistry in a field termed multimodal imaging. We explore the future of this field and discuss forthcoming developments that will bring new insights to help unravel the molecular complexities of biological systems, from single cells to functional tissue structures and organs.