Glycosphingolipids (GSL) are a highly heterogeneous class of lipids representing the majority of the sphingolipid category. GSL are fundamental constituents of cellular membranes that have key roles in various biological processes, such as cellular signaling, recognition, and adhesion. Understanding the structural complexity of GSL is pivotal for unraveling their functional significance in a biological context, specifically their crucial role in the pathophysiology of various diseases. Mass spectrometry (MS) has emerged as a versatile and indispensable tool for the structural elucidation of GSL enabling a deeper understanding of their complex molecular structures and their key roles in cellular dynamics and patholophysiology. Here, we provide a thorough overview of MS techniques tailored for the analysis of GSL, emphasizing their utility in probing GSL intricate structures to advance our understanding of the functional relevance of GSL in health and disease. The application of tandem MS using diverse fragmentation techniques, including novel ion activation methodologies, in studying glycan sequences, linkage positions, and fatty acid composition is extensively discussed. Finally, we address current challenges, such as the detection of low-abundance species and the interpretation of complex spectra, and offer insights into potential solutions and future directions by improving MS instrumentation for enhanced sensitivity and resolution, developing novel ionization techniques, or integrating MS with other analytical approaches for comprehensive GSL characterization.

• Keywords
• Derivatization, Fragmentation, Glycosphingolipids, Liquid chromatography, Mass spectrometry, Structural elucidation,
• MeSH
• Glycosphingolipids * chemistry   analysis   MeSH
• Mass Spectrometry * methods   MeSH
• Humans MeSH
• Tandem Mass Spectrometry methods   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Review MeSH
• Names of Substances
• Glycosphingolipids * MeSH

Pancreatic ductal adenocarcinoma (PDAC) is one of the most common causes of cancer-related deaths worldwide, accounting for 90% of primary pancreatic tumors with an average 5-year survival rate of less than 10%. PDAC exhibits aggressive biology, which, together with late detection, results in most PDAC patients presenting with unresectable, locally advanced, or metastatic disease. In-depth lipid profiling and screening of potential biomarkers currently appear to be a promising approach for early detection of PDAC or other cancers. Here, we isolated and characterized complex glycosphingolipids (GSL) from normal and tumor pancreatic tissues of patients with PDAC using a combination of TLC, chemical staining, carbohydrate-recognized ligand-binding assay, and LC/ESI-MS2. The major neutral GSL identified were GSL with the terminal blood groups A, B, H, Lea, Leb, Lex, Ley, P1, and PX2 determinants together with globo- (Gb3 and Gb4) and neolacto-series GSL (nLc4 and nLc6). We also revealed that the neutral GSL profiles and their relative amounts differ between normal and tumor tissues. Additionally, the normal and tumor pancreatic tissues differ in type 1/2 core chains. Sulfatides and GM3 gangliosides were the predominant acidic GSL along with the minor sialyl-nLc4/nLc6 and sialyl-Lea/Lex. The comprehensive analysis of GSL in human PDAC tissues extends the GSL coverage and provides an important platform for further studies of GSL alterations; therefore, it could contribute to the development of new biomarkers and therapeutic approaches.

• Keywords
• Lipidomics, Tandem mass spectrometry, chromatogram binding assay, glycolipids, lipids, liquid chromatography, monoclonal antibodies, pancreatic cancer, sphingolipids, thin-layer chromatography,
• MeSH
• Chromatography, Liquid MeSH
• Chromatography, Thin Layer MeSH
• Carcinoma, Pancreatic Ductal diagnosis   physiopathology   MeSH
• Gangliosides chemistry   MeSH
• Glycosphingolipids * analysis   chemistry   MeSH
• Humans MeSH
• Biomarkers, Tumor metabolism   MeSH
• Pancreatic Neoplasms * diagnosis   physiopathology   MeSH
• Sulfoglycosphingolipids chemistry   MeSH
• Tandem Mass Spectrometry MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Gangliosides MeSH
• Glycosphingolipids * MeSH
• Biomarkers, Tumor MeSH
• Sulfoglycosphingolipids MeSH

PURPOSE: RCC, the most common type of kidney cancer, is associated with high mortality. A non-invasive diagnostic test remains unavailable due to the lack of RCC-specific biomarkers in body fluids. We have previously described a significantly altered profile of sulfatides in RCC tumor tissues, motivating us to investigate whether these alterations are reflected in collectible body fluids and whether they can enable RCC detection. METHODS: We collected and further analyzed 143 plasma, 100 urine, and 154 tissue samples from 155 kidney cancer patients, together with 207 plasma and 70 urine samples from 214 healthy controls. RESULTS: For the first time, we show elevated concentrations of lactosylsulfatides and decreased levels of sulfatides with hydroxylated fatty acyls in body fluids of RCC patients compared to controls. These alterations are emphasized in patients with the advanced tumor stage. Classification models are able to distinguish between controls and patients with RCC. In the case of all plasma samples, the AUC for the testing set was 0.903 (0.844-0.954), while for urine samples it was 0.867 (0.763-0.953). The models are able to efficiently detect patients with early- and late-stage RCC based on plasma samples as well. The test set sensitivities were 80.6% and 90%, and AUC values were 0.899 (0.832-0.952) and 0.981 (0.956-0.998), respectively. CONCLUSION: Similar trends in body fluids and tissues indicate that RCC influences lipid metabolism, and highlight the potential of the studied lipids for minimally-invasive cancer detection, including patients with early tumor stages, as demonstrated by the predictive ability of the applied classification models.

• Keywords
• cancer biomarkers, classification models, early detection, lipidomics, renal cell carcinoma, sphingomyelins, sulfatide,
• Publication type
• Journal Article MeSH

Pancreatic cancer has the worst prognosis among all cancers. Cancer screening of body fluids may improve the survival time prognosis of patients, who are often diagnosed too late at an incurable stage. Several studies report the dysregulation of lipid metabolism in tumor cells, suggesting that changes in the blood lipidome may accompany tumor growth. Here we show that the comprehensive mass spectrometric determination of a wide range of serum lipids reveals statistically significant differences between pancreatic cancer patients and healthy controls, as visualized by multivariate data analysis. Three phases of biomarker discovery research (discovery, qualification, and verification) are applied for 830 samples in total, which shows the dysregulation of some very long chain sphingomyelins, ceramides, and (lyso)phosphatidylcholines. The sensitivity and specificity to diagnose pancreatic cancer are over 90%, which outperforms CA 19-9, especially at an early stage, and is comparable to established diagnostic imaging methods. Furthermore, selected lipid species indicate a potential as prognostic biomarkers.

• MeSH
• CA-19-9 Antigen blood   MeSH
• Ceramides blood   MeSH
• Humans MeSH
• Lipidomics methods   MeSH
• Lysophosphatidylcholines blood   MeSH
• Lipid Metabolism genetics   MeSH
• Multivariate Analysis MeSH
• Biomarkers, Tumor blood   genetics   MeSH
• Pancreatic Neoplasms blood   diagnosis   mortality   pathology   MeSH
• Proportional Hazards Models MeSH
• Sensitivity and Specificity MeSH
• Sphingomyelins blood   MeSH
• Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization MeSH
• Case-Control Studies MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• CA-19-9 Antigen MeSH
• Ceramides MeSH
• Lysophosphatidylcholines MeSH
• Biomarkers, Tumor MeSH
• Sphingomyelins MeSH