Is Lipidomic the Answer to the Search of a Biomarker for Organ Preservation Protocol in Head and Neck Squamous Cell Carcinoma?

Metabolomics of head and neck cancer in biofluids: an integrative systematic review

INTRODUCTION

Head and neck cancer (HNC) is the fifth most common cancer globally. Diagnosis at early stages are critical to reduce mortality and improve functional and esthetic outcomes associated with HNC. Metabolomics is a promising approach for discovery of biomarkers and metabolic pathways for risk assessment and early detection of HNC.

OBJECTIVES

To summarize and consolidate the available evidence on metabolomics and HNC in plasma/serum, saliva, and urine.

METHODS

A systematic search of experimental research was executed using PubMed and Web of Science. Available data on areas under the curve was extracted. Metabolic pathway enrichment analysis were performed to identify metabolic pathways altered in HNC. Fifty-four studies were eligible for data extraction (33 performed in plasma/serum, 15 in saliva and 6 in urine).

RESULTS

Metabolites with high discriminatory performance for detection of HNC included single metabolites and combination panels of several lysoPCs, pyroglutamate, glutamic acid, glucose, tartronic acid, arachidonic acid, norvaline, linoleic acid, propionate, acetone, acetate, choline, glutamate and others. The glucose-alanine cycle and the urea cycle were the most altered pathways in HNC, among other pathways (i.e. gluconeogenesis, glycine and serine metabolism, alanine metabolism, etc.). Specific metabolites that can potentially serve as complementary less- or non-invasive biomarkers, as well as metabolic pathways integrating the data from the available studies, are presented.

CONCLUSION

The present work highlights utility of metabolite-based biomarkers for risk assessment, early detection, and prognostication of HNC, as well as facilitates incorporation of available metabolomics studies into multi-omics data integration and big data analytics for personalized health.

MALDI-TOF/MS Analysis of Non-Invasive Human Urine and Saliva Samples for the Identification of New Cancer Biomarkers

Cancer represents a group of heterogeneous diseases that are a leading global cause of death. Even though mortality has decreased in the past thirty years for different reasons, most patients are still diagnosed at the advanced stage, with limited therapeutic choices and poor outcomes. Moreover, the majority of cancers are detected using invasive painful methods, such as endoscopic biopsy, making the development of non-invasive or minimally invasive methods for the discovery and fast detection of specific biomarkers a crucial need. Among body fluids, a valuable non-invasive alternative to tissue biopsy, the most accessible and least invasive are undoubtedly urine and saliva. They are easily retrievable complex fluids containing a large variety of endogenous compounds that may provide information on the physiological condition of the body. The combined analysis of these fluids with matrix-assisted laser desorption ionization–time-of-flight mass spectrometry (MALDI-TOF/MS), a reliable and easy-to-use instrumentation that provides information with relatively simple sample pretreatments, could represent the ideal option to rapidly achieve fast early stage diagnosis of tumors and their real-time monitoring. On this basis, the present review summarizes the recently reported applications relevant to the MALDI analysis of human urine and saliva samples.

A Study of the Lipidomic Profiles of the CAL-27 and HOK Cell Lines Using EMS Spectra

Objective

The aim of this study was to explore the lipidomic profiles of the CAL-27 human tongue cancer cell line and the human oral keratinocyte (HOK) cell line.

Methods

The lipidomic differences between the CAL-27 and the HOK cell lines were investigated using non-targeted high-performance liquid chromatography–mass spectrometry lipidomic analysis. The resulting data were then further mined via bioinformatics analysis technology and metabolic pathway analysis was conducted in order to map the most affected metabolites and pathways in the two cell lines.

Results

A total of 711 lipids were identified, including 403 glycerophospholipids (GPs), 147 glycerolipids, and 161 sphingolipids. Comparison of the enhanced MS (EMS) spectra of the two cell lines in positive and negative ionization modes showed the lipid compositions of HOK and CAL-27 cells to be similar. The expressions of most GP species in CAL-27 cells showed an increasing trend as compared with HOK, whereas a significant increase in phosphatidylcholine was observed (p < 0.05). Significant differences in the lipid composition between CAL-27 and HOK cells were shown as a heatmap. Through principal component analysis and orthogonal partial least squares discriminant analysis, noticeably clear separation trends and satisfactory clustering trends between groups of HOK and CAL-27 cells were identified. The numbers of specific lipid metabolites that could distinguish CAL-27 from HOK in positive and negative modes were 100 and 248, respectively. GP metabolism was the most significantly altered lipid metabolic pathway, with 4 metabolites differentially expressed in 39 hit products.

Conclusion

This study demonstrated the potential of using untargeted mass spectra and bioinformatics analysis to describe the lipid profiles of HOK and CAL-27 cells.

Analytical Strategies in Lipidomics for Discovery of Functional Biomarkers from Human Saliva

Human saliva is increasingly being used and validated as a biofluid for diagnosing, monitoring systemic disease status, and predicting disease progression. The discovery of biomarkers in saliva biofluid offers unique opportunities to bypass the invasive procedure of blood sampling by using oral fluids to evaluate the health condition of a patient. Saliva biofluid is clinically relevant since its components can be found in plasma. As salivary lipids are among the most essential cellular components of human saliva, there is great potential for their use as biomarkers. Lipid composition in cells and tissues change in response to physiological changes and normal tissues have a different lipid composition than tissues affected by diseases. Lipid imbalance is closely associated with a number of human lifestyle-related diseases, such as atherosclerosis, diabetes, metabolic syndromes, systemic cancers, neurodegenerative diseases, and infectious diseases. Thus, identification of lipidomic biomarkers or key lipids in different diseases can be used to diagnose diseases and disease state and evaluate response to treatments. However, further research is needed to determine if saliva can be used as a surrogate to serum lipid profiles, given that highly sensitive methods with low limits of detection are needed to discover salivary biomarkers in order to develop reliable diagnostic and disease monitoring salivary tests. Lipidomic methods have greatly advanced in recent years with a constant advance in mass spectrometry (MS) and development of MS detectors with high accuracy and high resolution that are able to determine the elemental composition of many lipids.