Abstract
Abstract This study is dedicated to the development and application of 'GPNotebook', an innovative Python-based data analysis tool, specifically designed to enhance glycopeptide research in cancer studies by enabling the detailed investigation of glycosylation patterns and their correlation with various clinical cancer phenotypes. In our study, we recognize the critical role of protein glycosylation in biological processes and its relevance in cancer. To address the lack of specialized tools for glycoproteomic analysis, we developed GPNotebook, a comprehensive Python package designed for detailed investigation of intact glycopeptides (IGPs). This tool integrates multiple functionalities, including statistical profiling, differential expression analysis, categorization of glycosylation subtypes, and exploration of the interactions between glycosylation and phosphorylation. It also encompasses survival analysis and assessments of glycosylation enzymes. We applied GPNotebook in a research project focused on 10 cancer types collected from Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects to validate its efficacy and demonstrate its wide-ranging capabilities. Through this application, we were able to analyze and interpret complex glycoproteomic data, revealing potential IGPs as biomarkers for cancer detection and differentiation of cancer subtypes. We identified hundreds of cancer-specific IGPs, with several showing promise as early-stage cancer biomarkers. In addition, our analysis revealed unique glycosylation patterns linked to specific molecular subtypes and immune cell distributions in tumor microenvironments. We also clarified the roles of certain glycosylation enzymes across different cancer types, highlighting their potential significance in cancer progression. GPNotebook has proven to be a critical tool in glycopeptide research. Our findings not only enhance the understanding of glycosylation in cancer but also demonstrate GPNotebook's potential in identifying novel biomarkers and providing insights into cancer subtype differentiation. This study positions GPNotebook as a valuable resource for exploring the complex relationships between protein glycosylation and cancer phenotypes. Citation Format: Hui Zhang, Trung Hoang, Yingwei Hu. GPNotebook: A toolkit for identifying cancer-related glycosylation alterations in glycoproteomic analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2339.
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