Proteomic and Glycomic Markers to Differentiate Lung Adenocarcinoma from COPD

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2019-2020

This review is the tenth update of the original article published in 1999 on the application of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2020. Also included are papers that describe methods appropriate to analysis by MALDI, such as sample preparation techniques, even though the ionization method is not MALDI. The review is basically divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of arrays. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other areas such as medicine, industrial processes and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. The reported work shows increasing use of incorporation of new techniques such as ion mobility and the enormous impact that MALDI imaging is having. MALDI, although invented nearly 40 years ago is still an ideal technique for carbohydrate analysis and advancements in the technique and range of applications show little sign of diminishing.

Plasma Proteomics Enable Differentiation of Lung Adenocarcinoma from Chronic Obstructive Pulmonary Disease (COPD)

Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass-spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel.

Glycosylation of immunoglobin G in tumors: Function, regulation and clinical implications

Immunoglobulin G (IgG) is the predominant component in humoral immunity and the major effector of neutralizing heterogeneous antigens. Glycosylation, as excessive posttranscriptional modification, can modulate IgG immune function. Glycosylated IgG has been reported to correlate with tumor progression, presenting several characteristic modifications, including the core fucose, galactose, sialic acid, and the bisect N-acetylglucosamine (GlcNAc). Meanwhile, IgG glycosylation regulates tumor immunity involved in tumor progression and is thus a potential target. Herein, we summarized the research progression to provide novel insight into the application of IgG glycosylation in tumor diagnosis and treatment.

Glycoprotein biomarkers and analysis in chronic obstructive pulmonary disease and lung cancer with special focus on serum immunoglobulin G

Chronic obstructive pulmonary disease (COPD) and lung cancer are two major diseases of the lung with high rate of mortality, mostly among tobacco smokers. The glycosylation patterns of various plasma proteins show significant changes in COPD and subsequent hypoxia, inflammation and lung cancer, providing promising opportunities for screening aberrant glycan structures contribute to early detection of both diseases. Glycoproteins associated with COPD and lung cancer consist of highly sialylated N-glycans, which play an important role in inflammation whereby hypoxia leads to accumulation of sialyl Lewis A and X glycans. Although COPD is an inflammatory disease, it is an independent risk factor for lung cancer. Marked decrease in galactosylation of plasma immunoglobulin G (IgG) together with increased presence of sialic acids and more complex highly branched N-glycan structures are characteristic for COPD and lung cancer. Numerous glycan biomarkers have been discovered, and analysis of glycovariants associated with COPD and lung cancer has been carried out. In this paper we review fundamental glycosylation changes in COPD and lung cancer glycoproteins, focusing on IgG to provide an opportunity to distinguish between the two diseases at the glycoprotein level with diagnostic value.