Non-Targeted Serum Lipidomics Analysis and Potential Biomarkers of Laryngeal Cancer Based on UHPLC-QTOF-MS

Plasma metabolomics and lipidomics reveal potential novel biomarkers in early gastric cancer: An explorative study

BACKGROUND

Early identification and therapy can significantly improve the outcome for gastric cancer. However, there is still no perfect biomarker available for the detection of early gastric cancer. This study aimed to investigate the alterations in the plasma metabolites of early gastric cancer using metabolomics and lipidomics based on high-performance liquid chromatography-mass spectrometry (HPLC-MS), which detected potential biomarkers that could be used for clinical diagnosis.

METHODS

To investigate the changes in metabolomics and lipidomics, a total of 30 plasma samples were collected, consisting of 15 patients with early gastric cancer and 15 healthy controls. Extensive HPLC-MS-based untargeted metabolomic and lipidomic investigations were conducted. Differential metabolites and metabolic pathways were uncovered through the utilization of statistical analysis and bioinformatics analysis. Candidate biomarker screening was performed using support vector machine-based multivariate receiver operating characteristic analysis.

RESULTS

A disturbance was observed in a combined total of 19 metabolites and 67 lipids of the early gastric cancer patients. The analysis of KEGG pathways showed that the early gastric cancer patients experienced disruptions in the arginine biosynthesis pathway, the pathway for alanine, aspartate, and glutamate metabolism, as well as the pathway for glyoxylate and dicarboxylate metabolism. Plasma metabolomics and lipidomics have identified multiple biomarker panels that can effectively differentiate early gastric cancer patients from healthy controls, exhibiting an area under the curve exceeding 0.9.

CONCLUSION

These metabolites and lipids could potentially serve as biomarkers for the screening of early gastric cancer, thereby optimizing the strategy for the detection of early gastric cancer. The disrupted pathways implicated in early gastric cancer provide new clues for additional understanding of gastric cancer's pathogenesis. Nonetheless, large-scale clinical data are required to prove our findings.

Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

TiO2@COF-based solid-phase microextraction combined with UHPLC-MS/MS for the rapid determination of potential biomarkers of phosphatidylcholines and lysophosphatidylcholines in head and neck cancers

Phosphatidylcholine (PC) and lysophosphatidylcholine (LPC), two types of phospholipids (PLs), have been reported to be closely correlated with head and neck cancers of laryngeal cancer (LC) and thyroid cancer (TC), which make their analysis crucial. TiO2@COF-based solid-phase microextraction (SPME) coupled to UHPLC-MS/MS was developed for the rapid and accurate detection of seven potential PL biomarkers from small amounts of serum in this work. The combination of TiO2 and COF proves to be effective for the extraction of the target analytes. Under optimal conditions, the developed TiO2@COF-based SPME-UHPLC-MS/MS method revealed good linearity (R2 ≥ 0.997) with LODs ranging from 0.05 to 0.38 ng/mL for PLs, the extraction recoveries and matrix effects ranging from 83.09-112.03% and 85.38-113.67%, respectively. As a high-throughput pretreatment method, satisfactory probe-to-probe reproducibility rates of 2.7-10.1% were obtained. Finally, the TiO2@COF-based SPME-UHPLC-MS/MS method was applied to analyze LPC 14:0, LPC 16:0, LPC 18:0, LPC 18:1, LPC 19:0, PC 16:0/18:1, and PC 18:0 in serum samples from early LC patients (n = 15), early TC patients (n = 15), and healthy volunteers (n = 15). The results indicated that cancer patients could be effectively differentiated from healthy controls using orthogonal partial least squares discriminant analysis (OPLS-DA). In conclusion, the established TiO2@COF-based SPME-UHPLC-MS/MS method is reliable for the rapid determination of the seven PLs in serum samples, which is promising for early diagnosis of head and neck cancers.

Lipid metabolism reprogramming in head and neck cancer

Lipid metabolism reprogramming is one of the most prominent metabolic anomalies in cancer, wherein cancer cells undergo dysregulation of lipid metabolism to acquire adequate energy, cell membrane building blocks, as well as signaling molecules essential for cell proliferation, survival, invasion, and metastasis. These adaptations enable cancer cells to effectively respond to challenges posed by the tumor microenvironment, leading to cancer therapy resistance and poor cancer prognosis. Head and neck cancer, ranking as the seventh most prevalent cancer, exhibits numerous abnormalities in lipid metabolism. Nevertheless, the precise role of lipid metabolic rewiring in head and neck cancer remains unclear. In line with the LIPID MAPS Lipid Classification System and cancer risk factors, the present review delves into the dysregulated molecules and pathways participating in the process of lipid uptake, biosynthesis, transportation, and catabolism. We also present an overview of the latest advancements in understanding alterations in lipid metabolism and how they intersect with the carcinogenesis, development, treatment, and prognosis of head and neck cancer. By shedding light on the significance of metabolic therapy, we aspire to improve the overall prognosis and treatment outcomes of head and neck cancer patients.

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.