Glycoproteomic analysis of prostate cancer tissues by SWATH mass spectrometry discovers N-acylethanolamine acid amidase and protein tyrosine kinase 7 as signatures for tumor aggressiveness

Abstract

The identification of biomarkers indicating the level of aggressiveness of prostate cancer (PCa) will address the urgent clinical need to minimize the general overtreatment of patients with non-aggressive PCa, who account for the majority of PCa cases. Here, we isolated formerly N-linked glycopeptides from normal prostate (n = 10) and from non-aggressive (n = 24), aggressive (n = 16), and metastatic (n = 25) PCa tumor tissues and analyzed the samples using SWATH mass spectrometry, an emerging data-independent acquisition method that generates a single file containing fragment ion spectra of all ionized species of a sample. The resulting datasets were searched using a targeted data analysis strategy in which an a priori spectral reference library representing known N-glycosites of the human proteome was used to identify groups of signals in the SWATH mass spectrometry data. On average we identified 1430 N-glycosites from each sample. Out of those, 220 glycoproteins showed significant quantitative changes associated with diverse biological processes involved in PCa aggressiveness and metastasis and indicated functional relationships. Two glycoproteins, N-acylethanolamine acid amidase and protein tyrosine kinase 7, that were significantly associated with aggressive PCa in the initial sample cohort were further validated in an independent set of patient tissues using tissue microarray analysis. The results suggest that N-acylethanolamine acid amidase and protein tyrosine kinase 7 may be used as potential tissue biomarkers to avoid overtreatment of non-aggressive PCa.

Fig. 1.

Experimental design. Formerly N-glycosylated peptides of well-characterized PCa tissue samples were isolated via solid phase extraction of glycopeptides (SPEG) and subjected to SWATH-MS. A spectral library for the targeted identification and quantification of specific N-glycosites from the SWATH maps was generated via shotgun sequencing of the de-N-glycopeptides from clinical samples and synthetic reference peptides. The in-house-developed OpenSWATH software was used to identify and quantify peaks in SWATH maps, and quantification was followed by bioinformatic analysis and immunohistochemistry (IHC)-based tissue microarray validation.

Fig. 2.

Quantitative profiling of tissue N-glyoproteome between different PCa groups. A, numbers of N-glycosites identified from SWATH maps. Note that identification of de-N-glycopeptides using the SWATHatlas library from synthetic reference peptides increased the number of identified peptides by approximately 30%. B, hierarchical clustering analysis of 1057 N-glycosites quantified among >80% of the samples. C, the accuracy of SWATH-MS quantification was revealed by the example of PSA that was also measured by the tissue ELISA. D, principle component analysis of NAG and AG cases tested.

Fig. 3.

Functional annotation of significantly regulated glycoproteins (n = 220) between PCa groups. A, the GO cellular component distribution. B, the pathways and biological processes enriched in the 220-protein list, according to DAVID functional annotation.

Fig. 4.

An RFIN subnetwork of functional interactions between regulated glycoproteins (AG versus NAG) and genes commonly mutated in PCa. Red diamonds denote proteins up-regulated in NAG, and blue diamonds denote glycoproteins up-regulated in AG. Altered genes are shown by yellow circles. Edges denote functional relationships between the nodes they connect. Regulated glycoproteins and altered genes not connected to each other are not shown in the network. Relative to randomized RFIN networks of the same size and same connectivity, the altered PCa genes and our regulated glycoproteins are significantly interconnected to each other (p = 0.021).

Fig. 5.

TMA analysis for NAAA and PTK7. A, B, representative NAAA staining showing strong staining in Gleason score 3 tumors and faint staining in Gleason score 4 tumors. Faint or no staining was observed in adjacent normal tissues. C, D, representative PTK7 staining showing strong staining in Gleason score 4 tumors and less staining in Gleason score 3 tumors. Faint staining was observed in adjacent normal tissues. E, representative NAAA and PTK7 staining in TMA with corresponding H&E staining. F, histogram of the difference in IHC score between tumor and its matched adjacent normal tissue for NAAA. G, histogram of the difference in IHC score between tumor and its matched adjacent normal tissue for PKT7. Wilcoxon signal rank order test (paired, two-sided) was performed for NAAA and PTK7 between tumors and matched adjacent normal tissues (p < 0.0001). H, a box-plot was generated for NAAA and PTK7 between tumors with a Gleason score less than or equal to 3 + 4 tumor and tumors with a Gleason score greater than or equal to 4 + 3 tumor. *p < 0.05; **p < 0.01. I, ROC analysis of NAAA and PTK7 based on their IHC staining intensities in the tumor specimens.