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The study applied transcriptome-wide association studies (TWAS), proteome-wide association studies (PWAS), summary-data-based Mendelian randomization (SMR), and Mendelian randomization (MR) to pinpoint genes linked to cancer risk. Identified signals were further examined through phenotype scanning and enrichment analyses to infer shared biological processes and health effects, including MR for metabolic pathways. A total of 24 genes showed significant associations, with multi-method genes enriched in the NRF2 pathway, and gastrointestinal tumors implicating ubiquinol and urate biosynthesis.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/prioritization-of-therapeutic-targets-for-cancers-using-integrative-multi-omics-analysis/333309/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/prioritization-of-therapeutic-targets-for-cancers-using-integrative-multi-omics-analysis/333309.png","ImageObject",300,407,{"name":92,"@type":93},"Ava Thompson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Which integrative data types were combined to study cancer therapeutic targets?","Question",{"text":112,"@type":113},"The analysis integrates transcriptomic, proteomic, druggable genetic, and metabolomic association evidence to examine molecular features and shared pathways in cancers.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were genes associated with cancers identified?",{"text":117,"@type":113},"The study used TWAS, PWAS, summary-data-based Mendelian randomization (SMR), and MR, then applied phenotype scanning and enrichment analyses to explore health effects and shared pathways.",{"name":119,"@type":110,"acceptedAnswer":120},"What biological pathway was most enriched among genes supported by multiple methods?",{"text":121,"@type":113},"Genes identified by multiple methods were mainly enriched in the nuclear factor erythroid 2-related factor 2 (NRF2) pathway.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},333309,1790916127,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},1649267921044,"https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1786009248482753345","Jin et al. Human Genomics (2024) 18:42 [https://doi.org/10.1186/s40246-024-00571-2](https://doi.org/10.1186/s40246-024-00571-2)  \nHuman Genomics  \n RESEARCH Open Access  \nPrioritization of therapeutic targets for cancers using integrative multi-omics  \nanalysis  \nXin Jin1,2†, Yunyun Mei3†, Puyu Yang4†, Runze Huang1,2, Haifeng Zhang4, Yibin Wu1,2, Miao Wang1,2, Xigan He 1,2, Ziting Jiang2,5*, Weiping Zhu1,2* and Lu Wang1,2*  \nAbstract  \nBackground The integration of transcriptomic, proteomic, druggable genetic and metabolomic association studies facilitated a comprehensive investigation of molecular features and shared pathways for cancers’ development and progression.  \nMethods Comprehensive approaches consisting of transcriptome-wide association studies (TWAS), proteome-wide association studies (PWAS), summary-data-based Mendelian randomization (SMR) and MR were performed to identify genes significantly associated with cancers. The results identified in above analyzes were subsequently involved in phenotype scanning and enrichment analyzes to explore the possible health effects and shared pathways. Additionally, we also conducted MR analysis to investigate metabolic pathways related to cancers.  \nResults Totally 24 genes (18 transcriptomic, 1 proteomic and 5 druggable genetic) showed significant associations with cancers risk. All genes identified in multiple methods were mainly enriched in nuclear factor erythroid 2-related factor 2 (NRF2) pathway. Additionally, biosynthesis of ubiquinol and urate were found to play an important role in gastrointestinal tumors.  \nConclusions A set of putatively causal genes and pathways relevant to cancers were identified in this study, shedding light on the shared biological processes for tumorigenesis and providing compelling genetic evidence to prioritize anti-cancer drugs development.  \nKeywords Cancers, Genome-wide association studies, Therapeutic targets, Multi-omics, NRF2 pathway  \n†Xin Jin, Yunyun Mei and Puyu Yang contributed equally in this work.  \n*Correspondence:  \nZiting Jiang [zitingjiang@126.com](zitingjiang@126.com)[ ](zitingjiang@126.com)Weiping Zhu [wpzhush@hotmail.com](wpzhush@hotmail.com)[ ](wpzhush@hotmail.com)Lu Wang [wangluzl@fudan.edu.cn](wangluzl@fudan.edu.cn)  \n1 Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai 200032, People’s Republic of China  \n2 Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People’s Republic of China  \n3 Department of Neurosurgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai 200032, People’s Republic of China  \n4 Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, People’s Republic of China  \n5 Department of Endoscopy, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai 200032, People’s Republic of China  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[. The Creative Commons Public Dom","cbCaivICyUeOTJbi","https://ap.wps.com/l/cbCaivICyUeOTJbi","pdf",1231533,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# Background","[{\"question\":\"Which integrative data types were combined to study cancer therapeutic targets?\",\"answer\":\"The analysis integrates transcriptomic, proteomic, druggable genetic, and metabolomic association evidence to examine molecular features and shared pathways in cancers.\"},{\"question\":\"How were genes associated with cancers identified?\",\"answer\":\"The study used TWAS, PWAS, summary-data-based Mendelian randomization (SMR), and MR, then applied phenotype scanning and enrichment analyses to explore health effects and shared pathways.\"},{\"question\":\"What biological pathway was most enriched among genes supported by multiple methods?\",\"answer\":\"Genes identified by multiple methods were mainly enriched in the nuclear factor erythroid 2-related factor 2 (NRF2) pathway.\"}]","Prioritization of therapeutic targets for cancers using integrative multi-omics analysis | PDF",1790001880,25]