Integrated Proteomic and Glycoproteomic Characterization of Human High-Grade Serous Ovarian Carcinoma

Abstract

Many gene products exhibit great structural heterogeneity because of an array of modifications. These modifications are not directly encoded in the genomic template but often affect the functionality of proteins. Protein glycosylation plays a vital role in proper protein functions. However, the analysis of glycoproteins has been challenging compared with other protein modifications, such as phosphorylation. Here, we perform an integrated proteomic and glycoproteomic analysis of 83 prospectively collected high-grade serous ovarian carcinoma (HGSC) and 23 non-tumor tissues. Integration of the expression data from global proteomics and glycoproteomics reveals tumor-specific glycosylation, uncovers different glycosylation associated with three tumor clusters, and identifies glycosylation enzymes that were correlated with the altered glycosylation. In addition to providing a valuable resource, these results provide insights into the potential roles of glycosylation in the pathogenesis of HGSC, with the possibility of distinguishing pathological outcomes of ovarian tumors from non-tumors, as well as classifying tumor clusters.

Keywords: CPTAC; HGSC; glycoproteomics; glycosylation; high-grade serous ovarian carcinoma; mass spectrometry; proteomics; tumor clusters.

Figure 1.. The Workflow of the Integrated Glycoproteomic Strategy to Analyze HGSCs and Non-tumor Tissues

(A) Proteins from 83 HGSC tumor tissues, 23 non-tumor tissues, pooled reference sample, and technical replicates of quality control sample were digested by trypsin to peptides, which were labeled by TMT and analyzed by global proteomic analysis (GLOBAL), as well as glycosite-containing peptides (SPEG), and intact glycopeptides (IGPs) analysis using LC-MS/MS. (B) The clinical phenotypes and data profiling of proteomic (GLOBAL) and glycoproteomic (SPEG and IGP) data of 83 tumor and 23 non-tumor tissues. See also Tables S1, S2, S3, and S4 and Figure S1.

Figure 2.. The Proteomic and Glycoproteomic Investigation in Tumor Clusters

(A) Hierarchical clustering of tumor samples based on their Z score transformed abundance of IGPs from the IGP dataset. The clinical phenotypes of all 83 tumor samples, including tumor cellularity, tumor grade, tumor stage, participant race, participant age, anatomic site, origin site, and the labeled tumor clusters classified from GLOBAL, SPEG, and IGP datasets, were shown in the top rows of the clustered heatmap. The left columns showed the overrepresented pathways in the three IGP groups (IGs) and the associated glycan types on the IGPs. HM, high-mannose glycans; Fuc, fucosylated glycans; Sia, sialylated glycans. (B) The pairwise correlation values between the three tumor clusters based on GLOBAL and IGP datasets, respectively. (C) The pairwise correlation values between the three tumor clusters based on SPEG and IGP datasets, respectively. (D) The pairwise correlation values between the three clinical phenotypes (tumor cellularity, anatomic site, and origin site) and the three tumor clusters in the IGP dataset. (E) The abundance comparison of three IGP groups (IGs) grouped by three tumor clusters in the IGP dataset. Fuc, fucose; HM, high mannose; Sia, Sialic acid. See also Table S5 and Figure S2.

Figure 3.. Proteomic and Glycoproteomic Analyses of 83 Ovarian Tumors and 23 Non-tumors Revealed Alterations of Proteins and Glycoproteins in Ovarian Tumors

(A) Principal-component analysis (PCA) based on the abundance of IGPs from the IGP dataset to reveal the difference between 83 tumor and 23 non-tumor samples. (B) Volcano plot of IGPs of 83 tumor and 23 non-tumor samples to reveal the significantly upregulated and downregulated IGPs. (C) Receiver operating characteristic (ROC) curves of selected IGPs: HYOU1_931_N2H8 (AEPPLNASASDQGEK), FKBP10 _70_N2H8 (YHYNGTFEDGK), PSAP_80_ N2H3F1S0G0 (DNATEEEILVYLEK), and PPT1_212_N2H7 (GINESYK).The format is GeneName_Glycosite_GlycanComposition (PeptideSequence). In the glycan composition, N = HexNAc and H = Hex. (D) Overrepresentation analysis (ORA) of significantly upregulated and downregulated IGPs using DAVID 6.8 referring to the KEGG pathway database. (E) The relative abundances of IGPs in tumor and non-tumor samples. (F) The enriched pathways from the gene sets obtained from the identified IGPs under three different glycosylation types (HM, Fuc, and Sia). See also Table S6 and Figure S3.

Figure 4.. Proteomic and Glycoproteomic Analyses of 83 Ovarian Tumors and 23 Non-tumors Reveal Alterations of Proteins and Glycoproteins in Ovarian Tumors

(A) A comparative analysis of the differential abundance changes of glycosite-containing peptides and their corresponding proteins in tumors comparing with non-tumor samples from SPEG glycoproteomic data and GLOBAL proteomic data, respectively. (B) A comparative analysis of the differential abundance changes of IGPs and glycosite-containing peptides in tumors comparing with non-tumors from intact glycoproteomic data and SPEG glycoproteomic data, respectively. The attached glycans were classified and highlighted by three groups (HM, Fuc, and Sia) according to their identified glycan compositions. (C) The abundance changes of global protein expression of CA125 (MUC16), an ovarian cancer biomarker, in the tumor and non-tumor samples. (D) The abundance changes of glycosite-containing peptides NTSVGPLYSGCR of protein CA125 (MUC16) in the comparison between tumors and non-tumors. The identifier of each glycosite-containing peptide was presented using the specific format: MUC16(gene name)_12272(start position of the peptide) _NTSVGPLYSGCR(peptide sequence)_1(number of glycosites)_12272(glycosite position(s)). (E) The abundance changes of glycosite-containing peptides NTSVGLLYSGCR of protein CA125 (MUC16) in the comparison between tumors and non-tumors. (F–H) Micro-heterogeneity of glycosylation expression on the same IGPs of translocon-associated protein subunit beta (SSR2). The identifier of IGPs was presented using the format: SSR2(gene name) IAPASNVSHTVVLRPK(peptide sequence)+N2H8F0S0G0(glycan composition), in which N2H8F0S0G0 represents the glycan composition of HexNAc/N:2, Hexose/H:8, Fucose/F:0, Neu5Ac/S:0, and Neu5Gc/G:0.

Figure 5.. Association of IGP Abundance and Protein Levels of Glycosylation Enzymes in 83 Tumors and 23 Non-tumors

(A) The hierarchal-clustered correlation matrix of IGPs and glycosylation enzymes. The glycan types were highlighted in the top rows. (B) The bar chart log₂ fold change (FC) ratio values of glycosylation enzymes between tumor and non-tumor samples from the GLOBAL dataset. (C) Correlation between FUT11 and IGPs with/without Fuc glycans (Fuc and non-Fuc). (D) Correlation between PRKCSH and IGPs with/without HM (HM and non-HM). (E) Correlation between MAN1A1 and IGPs with/without HM (HM and non-HM). (F) The abundances of FUT11 in tumors and non-tumors. (G) The abundances of PRKCSH in tumors and non-tumors. (H) The abundances of MAN1A1 in tumors and non-tumors.

Figure 6.. The Synthesis Pathway and Protein-Protein Interaction (PPI) Network of the Elevated IGPs Modified by HM Glycans in Tumor

(A) The possible mechanism of glycan biosynthesis with the elevated HM glycosylation in ovarian cancer. (B) The PPI network of significantly upregulated HM IGPs in tumors. The annotations were also marked by different colors on the nodes of the involved genes.