Jingjing Zhu, Ke Wu, Shuai Liu, Alexandra Masca, Hua Zhong, Tai Yang, Dalia H Ghoneim, Praveen Surendran, Tanxin Liu, Qizhi Yao, Tao Liu, Sarah Fahle, Adam Butterworth, Md Ashad Alam, Jaydutt V Vadgama, Youping Deng, Hong-Wen Deng, Chong Wu, Yong Wu, Lang Wu
{"title":"全蛋白质组关联研究和功能验证确定了胰腺导管腺癌的新型蛋白质标记物。","authors":"Jingjing Zhu, Ke Wu, Shuai Liu, Alexandra Masca, Hua Zhong, Tai Yang, Dalia H Ghoneim, Praveen Surendran, Tanxin Liu, Qizhi Yao, Tao Liu, Sarah Fahle, Adam Butterworth, Md Ashad Alam, Jaydutt V Vadgama, Youping Deng, Hong-Wen Deng, Chong Wu, Yong Wu, Lang Wu","doi":"10.1093/gigascience/giae012","DOIUrl":null,"url":null,"abstract":"<p><p>Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC.</p><p><strong>Significance: </strong>PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. This dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":null,"pages":null},"PeriodicalIF":11.8000,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11010651/pdf/","citationCount":"0","resultStr":"{\"title\":\"Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma.\",\"authors\":\"Jingjing Zhu, Ke Wu, Shuai Liu, Alexandra Masca, Hua Zhong, Tai Yang, Dalia H Ghoneim, Praveen Surendran, Tanxin Liu, Qizhi Yao, Tao Liu, Sarah Fahle, Adam Butterworth, Md Ashad Alam, Jaydutt V Vadgama, Youping Deng, Hong-Wen Deng, Chong Wu, Yong Wu, Lang Wu\",\"doi\":\"10.1093/gigascience/giae012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC.</p><p><strong>Significance: </strong>PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. 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Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma.
Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC.
Significance: PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. This dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions.
期刊介绍:
GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.