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      Comprehensive Glycoprofiling of Oral Tumors Associates N-Glycosylation With Lymph Node Metastasis and Patient Survival

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          Abstract

          While altered protein glycosylation is regarded a trait of oral squamous cell carcinoma (OSCC), the heterogeneous and dynamic glycoproteome of tumor tissues from OSCC patients remain unmapped. To this end, we here employ an integrated multi-omics approach comprising unbiased and quantitative glycomics and glycoproteomics applied to a cohort of resected primary tumor tissues from OSCC patients with (n = 19) and without (n = 12) lymph node metastasis. While all tumor tissues displayed relatively uniform N-glycome profiles suggesting overall stable global N-glycosylation during disease progression, altered expression of six sialylated N-glycans was found to correlate with lymph node metastasis. Notably, glycoproteomics and advanced statistical analyses uncovered altered site-specific N-glycosylation revealing previously unknown associations with several clinicopathological features. Importantly, the glycomics and glycoproteomics data unveiled that comparatively high abundance of two core-fucosylated and sialylated N-glycans (Glycan 40a and Glycan 46a) and one N-glycopeptide from fibronectin were associated with low patient survival, while a relatively low abundance of N-glycopeptides from both afamin and CD59 were also associated with poor survival. This study provides insight into the complex OSCC tissue N-glycoproteome, thereby forming an important resource to further explore the underpinning disease mechanisms and uncover new prognostic glycomarkers for OSCC.

          Graphical Abstract

          Highlights

          • Integrated glycomics and glycoproteomics analysis of oral tumor tissues.

          • First comprehensive map of the oral tumor N-glycoproteome.

          • Association of N-glycosylation with key clinicopathological features.

          • Specific N-glycans and N-glycopeptides report on patient survival.

          • Public resource to explore glycosylation-related mechanisms underpinning oral cancer.

          In Brief

          Powered by our comprehensive glycomics-assisted glycoproteomics approach, we here provide new insights into the heterogenous N-glycoproteome in oral cancer from patients with and without lymph node metastasis. We find evidence supporting previously unknown associations between glycosylation changes and important clinicopathological features including patient survival. The study and the publicly available glycomics and glycoproteomics data form an important resource to further explore the underpinning disease mechanisms and uncover new prognostic glycomarkers for oral cancer.

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          Most cited references92

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

            DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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              MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

              Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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                Author and article information

                Contributors
                Journal
                Mol Cell Proteomics
                Mol Cell Proteomics
                Molecular & Cellular Proteomics : MCP
                American Society for Biochemistry and Molecular Biology
                1535-9476
                1535-9484
                01 June 2023
                July 2023
                01 June 2023
                : 22
                : 7
                : 100586
                Affiliations
                [1 ]Laboratório de Espectrometria de Massas, Laboratório Nacional de Biociências (LNBio), Centro Nacional de Pesquisa em Energia e Materiais (CNPEM), Campinas, São Paulo, Brazil
                [2 ]Oral Pathology, Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Campinas, São Paulo, Brazil
                [3 ]Molecular Biology and Genetic Engineering Center, University of Campinas, Campinas, São Paulo, Brazil
                [4 ]Serviço de Odontologia Oncológica, Instituto do Câncer do Estado de São Paulo, ICESP-FMUSP, São Paulo, São Paulo, Brazil
                [5 ]Universidade Brasil, Fernandópolis, São Paulo, Brazil
                [6 ]Serviço de Cirurgia de Cabeça e Pescoço, Instituto do Câncer do Estado de São Paulo, ICESP-FMUSP, São Paulo, São Paulo, Brazil
                [7 ]Departamento de Cirurgia de Cabeça e Pescoço e Otorrinolaringologia, A.C. Camargo Cancer Center, São Paulo, São Paulo, Brazil
                [8 ]Faculdade de Medicina, Departamento de Cirurgia de Cabeça e Pescoço, Universidade de São Paulo - USP, São Paulo, São Paulo, Brazil
                [9 ]School of Natural Sciences, Macquarie University, Sydney, Australia
                [10 ]Institute for Glyco-core Research (iGCORE), Nagoya University, Nagoya, Japan
                Author notes
                []For correspondence: Adriana Franco Paes Leme; Rebeca Kawahara; Morten Thaysen-Andersen adriana.paesleme@ 123456lnbio.cnpem.br rebeca.kawaharasakuma@ 123456mq.edu.au morten.andersen@ 123456mq.edu.au
                [‡]

                These authors contributed equally to this work.

                Article
                S1535-9476(23)00097-X 100586
                10.1016/j.mcpro.2023.100586
                10336694
                37268159
                02027c3d-541c-4d24-a071-bbfe45bdf0a3
                © 2023 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 1 February 2023
                : 8 May 2023
                Categories
                Research

                Molecular biology
                glycosylation,oral cancer,lymph node metastasis,glycoproteome,glycoproteomics,glycomics

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