Label-Free Glycopeptide Quantification for Biomarker Discovery in Human Sera

Glycan moieties of glycoproteins modulate many biological processes in mammals, such as immune response, inflammation, and cell signaling. Numerous studies show that many human diseases are correlated with quantitative alteration of protein glycosylation. In some cases, these changes can occur for certain types of glycans over specific sites in a glycoprotein rather than on the global abundance of the glycoprotein. Conventional analytical techniques that analyze the abundance of glycans cleaved from glycoproteins cannot reveal these subtle effects. Here we present a novel statistical method to quantify the site-specific glycosylation of glycoproteins in complex samples using label-free mass spectrometric techniques. Abundance variations between sites of a glycoprotein as well as different glycoforms, that is, glycopeptides with different glycans attached to the same site, can be detected using these techniques. We applied our method to an esophageal cancer study based on blood serum samples from cancer patients in an attempt to detect potential biomarkers of site-specific N-linked glycosylation. A few glycoproteins, including vitronectin, showed significantly different site-specific glycosylations within cancer/control samples, indicating that our method is ready to be used for the discovery of glycosylated biomarkers

Comparison of sensitivity, specificity and AUROC values from different PLS-DA models using differentiating metabolites detected individually by NMR or MS and their combination.

<p>aTrending markers that progressively change in their levels between EAC, high risk (BE and HGD) and healthy controls (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030181#pone-0030181-g003" target="_blank">Figure 3</a>).</p

Box-and-whisker plots illustrating progressive changes of the metabolite levels in high-risk patients (BE and HGD) and esophageal adenocarcinoma (EAC) patients relative to healthy controls.

<p>Horizontal line in the middle portion of the box, median; bottom and top boundaries of boxes, lower and upper quartile; whiskers, 5th and 95th percentiles; open circles, outliers. The first eight markers were detected by LC-MS, and the remaining four were detected by NMR.</p

Performance comparison of metabolic profiles between EAC patients and healthy controls.

<p>(A) Left, result of the PLS-DA model using 12 metabolite markers from LC-MS analyses; middle, ROC curve using the cross-validated predicted class values (AUROC = 0.82); right, PLS-DA prediction for the BE and HGD samples from the LC-MS model comparing EAC and healthy controls. (B) Same as (A) except using 8 metabolite markers from NMR analyses, (AUROC = 0.86); (C) Same as (A) except using the combination of LC-MS and NMR detected metabolite markers, (AUROC = 0.95).</p

Altered metabolism pathways for the most relevant metabolic differences between patients with EAC and control subjects.

<p>Blue boxes indicate metabolites that are up-regulated in EAC patient sera, while red boxes indicate metabolites that are down-regulated. Metabolites in bold showed mean levels that changed progressively from control to high-risk esophagus diseases (BE and HGD) and ultimately EAC.</p

Performance comparison of metabolic profiles between EAC patients and those with high-risk esophageal diseases (BE and HGD).

<p>(A) Left, result of the PLS-DA model using 7 metabolite markers from LC-MS analyses; middle, ROC curve using the cross-validated predicted class values (AUROC = 0.87); right, PLS-DA prediction for the healthy controls using the model developed using LC-MS metabolites comparing EAC and high-risk patients. (B) Same as (A) except using the 8 metabolite markers from NMR analyses, (AUROC = 0.72). (C) Same as (A) except using the combination of LC-MS and NMR detected metabolite markers, (AUROC = 0.82).</p

Differentiating metabolites (<i>p</i>-value <0.05) among EAC, high-risk (BE and HGD) and control groups.

<p>a<i>p</i>-value determined from Student's <i>t</i>-test, only <i>p</i>-values <0.05 are displayed;</p><p>bFC: fold change between esophageal adenocarcinoma (EAC) and healthy controls. Positive sign indicates a higher level in EAC and a negative value indicates a lower level;</p><p>cThe structural isomers of leucine and isoleucine could not be separated with the current LC method.</p

PLS-DA models comparing two patient groups, their coresponding ROC curves, and the prediction of the models for the other (third) patient group using the 12 trending markers of <b>Figure 3</b>.

<p>(A) Performance comparison of metabolic profiles between EAC patients and healthy controls, AUROC = 0.92. (B) Performance comparison of metabolic profiles between EAC and BE/HGD patients, AUROC = 0.78.</p