Early and accurate diagnosis of gastric cancer is vital for effective and targeted treatment. It is known that glycosylation profiles differ in the cancer tissue development process. This study aimed to profile the N-glycans in gastric cancer tissues to predict gastric cancer using machine learning algorithms. The (glyco-) proteins of formalin-fixed parafilm embedded (FFPE) gastric cancer and adjacent control tissues were extracted by chloroform/methanol extraction after the conventional deparaffinization step. The N-glycans were released and labeled with a 2-amino benzoic (2-AA) tag. The MALDI-MS analysis of the 2-AA labeled N-glycans was performed in negative ionization mode, and fifty-nine N-glycan structures were determined. The relative and analyte areas of the detected N-glycans were extracted from the obtained data. Statistical analyses identified significant expression levels of 14 different N-glycans in gastric cancer tissues. The data were separated based on the physical characteristics of N-glycans and used to test in machine-learning models. It was determined that the multilayer perceptron (MLP) was the most appropriate model with the highest sensitivity, specificity, accuracy, Matthews correlation coefficient, and f1 scores for each dataset. The highest accuracy score (96.0 ± 1.3) was obtained from the whole N-glycans relative area dataset, and the AUC value was determined as 0.98. It was concluded that gastric cancer tissues could be distinguished from adjacent control tissues with high accuracy using mass spectrometry-based N-glycomic data.

Invasive ductal carcinoma (IDC) is the most common type of breast cancer. As dynamic changes of the glycome are closely associated with complex diseases, they have become a focal point of cancer research involving predictive and prognostic markers. Formalin-fixed paraffin-embedded (FFPE) clinical specimens are representative of the tumor environment and are thus utilized in studies on cancer related research and biomarker discovery. Further studies on differential N-glycosylation profiling of IDC cancer tissues are necessary in order to understand the biological role of glycans in cancer and to evaluate their predictive ability. In this study, matrix assisted laser desorption ionization-mass spectrometry (MALDI-MS)-based analyses were conducted for determining differential N-glycosylation patterns of IDC. Two different derivatization methods, namely, 2-aminobenzoic acid (2-AA) labeling and linkage-specific sialic acid esterification, were used for the analysis of N-glycans. Forty-seven 2-AA labeled and fifty ethyl esterified N-glycans were identified by MALDI-MS. In statistical analyses conducted for 2-AA-labeled N-glycans, the relative amounts of 32 N-glycans and prevalence of 15 N-glycan traits showed significant (p < 0.05) differences between cancer and normal tissues; and in such analyses for the ethyl-esterified N-glycans, the relative amounts of 27 N-glycans and prevalence of 17 N-glycan traits showed significant (p < 0.05) differences between them. It was found that mainly high mannose N-glycans, including H5N2, H6N2, and H7N2, and two fucosylated compositions (H3N3F1 and H5N5F1) showed strong discrimination between IDC and controls. In addition, compared with the controls, high mannose N-glycans were observed to be up-regulated in IDC whereas bisecting N-glycans were down-regulated.

Advances in food production systems and customer acceptance have led to the commercial launch of dietary proteins produced via modern biotechnological approaches as alternatives to traditional agricultural sources. At the same time, a deeper understanding of how dietary components interact with the gut microbiome has highlighted the importance of understanding the nuances underpinning diet-microbiome interactions. Novel food proteins with distinct post-translational modifications resulting from their respective production systems have not been characterized, nor how they may differ from their traditionally produced counterparts. Here, we have characterized the protein composition and N -glycome of a yeast-synthesized and commercially available whey protein ingredient and compared this novel ingredient to whey protein isolate powder derived from bovine milk. Despite strong similarities in protein composition, we found that the N -glycome significantly differs between the two protein sources, reflecting the biosynthetic machinery of the production systems. Furthermore, the diversity of proteins found in yeast-synthesized whey protein were lower relative to bovine whey protein, despite both being predominantly β-lactoglobulin. Finally, to understand whether these differences in N- glycome profiles may affect the human gut microbiome, we compared these proteins in an in vitro fecal fermentation model. The two whey protein sources generated significant differences among three representative gut microbiomes in vitro , most likely due to differences in N -glycan composition and degradation by these representative microbial communities. This work highlights the need to understand how differences in novel biotechnological systems affect the bioactivity of synthesized proteins and how these differences impact the human gut microbiome. IMPORTANCE: Recent advances in food technology have led to the production of animal-free products from yeast that are traditionally derived from animals, such as milk proteins. These new processes raise important questions about the use of synthetic proteins as a replacement for traditionally sourced protein, especially in the context of the gut microbiome. Importantly, yeast produce N -glycans comprised primarily of mannose, while animals synthesize structurally and compositionally complex N -glycan structures. Given these differences, we characterized a new, yeast-derived whey protein ingredient and compared it to bovine whey protein. We found that yeast-derived whey protein differs in its impact on human gut microbiomes because of differences in N -glycan structures, despite similarity in protein composition. These findings raise important questions as to whether these differences in synthetic proteins lead to significant changes to the gut microbiome in vivo , and whether this may impact the utility of these novel ingredients.