Machine Learning Based Analysis of Human Serum N-glycome Alterations to Follow up Lung Tumor Surgery.

The human serum N-glycome is a valuable source of biomarkers for malignant diseases, already utilized in multiple studies. In this paper, the N-glycosylation changes in human serum proteins were analyzed after surgical lung tumor resection. Seventeen lung cancer patients were involved in this study and the N-glycosylation pattern of their serum samples was analyzed before and after the surgery using capillary electrophoresis separation with laser-induced fluorescent detection. The relative peak areas of 21 N-glycans were evaluated from the acquired electropherograms using machine learning-based data analysis. Individual glycans as well as their subclasses were taken into account during the course of evaluation. For the data analysis, both discrete (e.g., smoker or not) and continuous (e.g., age of the patient) clinical parameters were compared against the alterations in these 21 N-linked carbohydrate structures. The classification tree analysis resulted in a panel of N-glycans, which could be used to follow up on the effects of lung tumor surgical resection.

N-Glycosylation Alteration of Serum and Salivary Immunoglobulin a Is a Possible Biomarker in Oral Mucositis.

BACKGROUND: Oral and enteral mucositis due to high-dose cytostatic treatment administered during autologous and allogeneic stem-cell transplantation increases mortality. Salivary secretory immunoglobulin A (sIgA) is a basic pillar of local immunity in the first line of defense. Altered salivary sialoglycoprotein carbohydrates are important in the pathologies in the oral cavity including inflammation, infection and neoplasia. Therefore, we assessed whether changes in the salivary and serum IgA glycosylation correlated with development and severity of oral mucositis. METHODS: Using capillary electrophoresis, comparative analysis of serum and salivary IgA total N-glycans was conducted in 8 patients with autologous peripheral stem-cell transplantation (APSCT) at four different stages of transplantation (day -3/-7, 0, +7, +14) and in 10 healthy controls. RESULTS: Fourteen out of the 31 structures identified in serum and 6 out of 38 in saliva showed significant changes upon transplantation compared with the control group. Only serum core fucosylated, sialylated bisecting biantennary glycan (FA2BG2S2) showed significant differences between any two stages of transplantation (day -3/-7 and day +14; p = 0.0279). CONCLUSION: Our results suggest that changes in the serum IgA total N-glycan profile could serve as a disease-specific biomarker in patients undergoing APSCT, while analysis of salivary IgA N-glycan reflects the effect of APSCT on local immunity.

Modeling of the Desialylated Human Serum N-glycome for Molecular Diagnostic Applications in Inflammatory and Malignant Lung Diseases.

BACKGROUND: Immunoglobulin G and A, transferrin, haptoglobin and alpha-1- antitrypsin represent approximately 85% of the human serum glycoproteome and their N-glycosylation analysis may lead to the discovery of important molecular disease markers. However, due to the labile nature of the sialic acid residues, the desialylated subset of the serum N-glycoproteome has been traditionally utilized for diagnostic applications. OBJECTIVE: Creating a five-protein model to deconstruct the overall N-glycosylation fingerprints in inflammatory and malignant lung diseases. METHODS: The N-glycan pool of human serum and the five high abundant serum glycoproteins were analyzed. Simultaneous endoglycosidase/sialidase digestion was followed by fluorophore labeling and separation by CE-LIF to establish the model. Pooled serum samples from patients with COPD, lung cancer (LC) and their comorbidity were all analyzed. RESULTS: Nine significant (>1%) asialo-N-glycan structures were identified both in human serum and the standard protein mixture. The core-fucosylated-agalacto-biantennary glycan differentiated COPD and LC and both from the control and the comorbidity groups. Decrease in the core-fucosylated-agalacto-biantennary-bisecting, monogalacto and bigalacto structures differentiated all disease groups from the control. The significant increase of the fucosylated-galactosylated-triantennary structure was highly specific for LC, to a medium extent for COPD and a lesser extent for comorbidity. Also, some increase in the afucosylated-galactosylated-biantennary structure in all three disease types and afucosylated-galactosylated-triantennary structures in COPD and LC were observed in comparison to the control group. CONCLUSION: Our results suggested that changes in the desialylated human serum Nglycome hold glycoprotein specific molecular diagnostic potential for malignant and inflammatory lung diseases, which can be modeled with the five-protein mixture.

N-glycomic Analysis of Z(IgA1) Partitioned Serum and Salivary Immunoglobulin A by Capillary Electrophoresis.

AIMS: Application of capillary electrophoresis with laser induced fluorescence detection (CE-LIF) to identify the N-glycosylation structures of serum and saliva IgA from healthy controls and patients with malignant hematological diseases having cytostatic treatment induced mild oral mucosal lesions. BACKGROUND: Altered N-glycosylation of body fluid glycoproteins can be an effective indicator of most inflammatory processes. Immunoglobulin A (IgA) is the second highest abundant immunoglobulin and has a major role in the immune-defense against potential pathogen attacks. While IgA is abundant in serum, secretory immunoglobulin A (sIgA) is one of the most prevalent proteins in mucosal surfaces, such as in saliva. OBJECTIVE: Our aim was to investigate the changes of IgA glycosylation in serum and saliva as a response to an administered cytostatic treatment in patients with malignant hematological disorders. METHODS: Capillary electrophoresis with laser induced fluorescent detection (CE-LIF) was used to analyze the N-glycosylation profiles of Z(IgA1) partitioned immunoglobulin A in pooled serum and saliva of 10 control subjects and 8 patients with malignant hematological diseases having cytostatic treatment induced mild oral mucosal lesions. RESULTS: Eight of 31 and four of 38 N-glycans in serum and saliva, respectively, showed significant (p<0.05) differences upon comparison to the control group. Thirteen glycans were present in the saliva but not in the serum, on the other hand, six structures were found in the serum samples not present in the saliva. CONCLUSION: The developed Z(IgA1) partitioning and the high resolution CE-LIF based glyocoanalytical methods provided an efficient and sensitive workflow to detect and monitor IgA glycosylation alterations in serum and saliva with the scope for widespread molecular medicinal use.

Comparative analysis of the human serum N-glycome in lung cancer, COPD and their comorbidity using capillary electrophoresis.

Lung cancer (LC) and chronic obstructive pulmonary disease (COPD) are prevalent ailments with a great challenge to distinguish them based on symptoms only. Since they require different treatments, it is important to find non-invasive methods capable to readily diagnose them. Moreover, COPD increases the risk of lung cancer development, leading to their comorbidity. In this pilot study the N-glycosylation profile of pooled human serum samples (90 patients each) from lung cancer, COPD and comorbidity (LC with COPD) patients were investigated in comparison to healthy individuals (control) by capillary gel electrophoresis with high sensitivity laser-induced fluorescence detection. Sample preparation was optimized for human serum samples introducing a new temperature adjusted denaturation protocol to prevent precipitation and increased endoglycosidase digestion time to assure complete removal of the N-linked carbohydrates. The reproducibility of the optimized method was <3.5%. Sixty-one N-glycan structures were identified in the pooled control human serum sample and the profile was compared to pooled lung cancer, COPD and comorbidity of COPD with lung cancer patient samples. One important finding was that no other sugar structures were detected in any of the patient groups, only quantitative differences were observed. Based on this comparative exercise, a panel of 13 N-glycan structures were identified as potential glycobiomarkers to reveal significant changes (>33% in relative peak areas) between the pathological and control samples. In addition to N-glycan profile changes, alterations in the individual N-glycan subclasses, such as total fucosylation, degree of sialylation and branching may also hold important glycobiomarker values.