{"doi":"10.1016/j.mcpro.2023.100536","title":"Data-Independent Acquisition Phosphoproteomics of Urinary Extracellular Vesicles Enables Renal Cell Carcinoma Grade Differentiation","abstract":"•Optimal gas-phase fractionated (GPF) library for urinary EV phosphoproteomics.•Integrating EVtrap, PolyMAC, and GPF DIA for thousands of unique EV phosphosite identification.•Linear discriminant analysis (LDA) correctly clustered the grades of RCC patients.•Chronic kidney disease (CKD) served as a better control for RCC biomarker screening. Translating the research capability and knowledge in cancer signaling into clinical settings has been slow and ineffective. Recently, extracellular vesicles (EVs) have emerged as a promising source for developing disease phosphoprotein markers to monitor disease status. This study focuses on the development of a robust data-independent acquisition (DIA) using mass spectrometry to profile urinary EV phosphoproteomics for renal cell cancer (RCC) grades differentiation. We examined gas-phase fractionated library, direct DIA (library-free), forbidden zones, and several different windowing schemes. After the development of a DIA mass spectrometry method for EV phosphoproteomics, we applied the strategy to identify and quantify urinary EV phosphoproteomes from 57 individuals representing low-grade clear cell RCC, high-grade clear cell RCC, chronic kidney disease, and healthy control individuals. Urinary EVs were efficiently isolated by functional magnetic beads, and EV phosphopeptides were subsequently enriched by PolyMAC. We quantified 2584 unique phosphosites and observed that multiple prominent cancer-related pathways, such as ErbB signaling, renal cell carcinoma, and regulation of actin cytoskeleton, were only upregulated in high-grade clear cell RCC. These results show that EV phosphoproteome analysis utilizing our optimized procedure of EV isolation, phosphopeptide enrichment, and DIA method provides a powerful tool for future clinical applications. Translating the research capability and knowledge in cancer signaling into clinical settings has been slow and ineffective. Recently, extracellular vesicles (EVs) have emerged as a promising source for developing disease phosphoprotein markers to monitor disease status. This study focuses on the development of a robust data-independent acquisition (DIA) using mass spectrometry to profile urinary EV phosphoproteomics for renal cell cancer (RCC) grades differentiation. We examined gas-phase fractionated library, direct DIA (library-free), forbidden zones, and several different windowing schemes. After the development of a DIA mass spectrometry method for EV phosphoproteomics, we applied the strategy to identify and quantify urinary EV phosphoproteomes from 57 individuals representing low-grade clear cell RCC, high-grade clear cell RCC, chronic kidney disease, and healthy control individuals. Urinary EVs were efficiently isolated by functional magnetic beads, and EV phosphopeptides were subsequently enriched by PolyMAC. We quantified 2584 unique phosphosites and observed that multiple prominent cancer-related pathways, such as ErbB signaling, renal cell carcinoma, and regulation of actin cytoskeleton, were only upregulated in high-grade clear cell RCC. These results show that EV phosphoproteome analysis utilizing our optimized procedure of EV isolation, phosphopeptide enrichment, and DIA method provides a powerful tool for future clinical applications. Renal cell carcinoma (RCC) is currently the eighth leading cause of cancer death in the United States, affects nearly 300,000 individuals worldwide each year, and is responsible for more than 100,000 deaths annually (1Attalla K. Weng S. Voss M.H. Hakimi A.A. Epidemiology, risk assessment, and biomarkers for patients with advanced renal cell carcinoma.Urol. Clin. North Am. 2020; 47: 293-303Abstract Full Text Full Text PDF PubMed Scopus (20) Google Scholar, 2Siegel R.L. Miller K.D. Jemal A. Cancer statistics, 2019.CA. Cancer J. Clin. 2019; 69: 7-34Crossref PubMed Scopus (15310) Google Scholar). RCC originates from the renal cortex or the renal epithelial cells and accounts for more than 90% of all kid","journal":"Molecular & Cellular Proteomics","year":2023,"id":333185,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":23,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9255,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":992125,"name":"Zheng-Chi Lee","orcid":null,"position":1,"is_corresponding":false},{"id":993166,"name":"Zhuojun Luo","orcid":"0000-0001-6634-4090","position":2,"is_corresponding":false},{"id":993167,"name":"Guiyuan Zhang","orcid":"0000-0002-4737-7505","position":3,"is_corresponding":false},{"id":993751,"name":"Yajie Ding","orcid":null,"position":4,"is_corresponding":false},{"id":993168,"name":"Hao Zhang","orcid":"0000-0001-9744-1707","position":5,"is_corresponding":false},{"id":675733,"name":"Anton Iliuk","orcid":"0000-0002-2914-1363","position":6,"is_corresponding":false},{"id":289283,"name":"Роберто Пили","orcid":"0000-0002-5871-4896","position":7,"is_corresponding":false},{"id":460458,"name":"Ronald S. Boris","orcid":null,"position":8,"is_corresponding":false},{"id":250410,"name":"W. Andy Tao","orcid":"0000-0002-5535-5517","position":9,"is_corresponding":false},{"id":991764,"name":"Marco Hadisurya","orcid":"0000-0002-4453-5854","position":0,"is_corresponding":true}],"reference_count":71,"raw_metadata":null,"created_at":"2026-07-19T01:09:39.719497Z","pmid":"36997065","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}