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The study introduces SAGES (Structural Analysis of Gene and protein Expression Signatures), which represents expression patterns using sequence-based predictive features and 3D structural models. SAGES with machine learning characterizes healthy and breast-cancer tissues using gene expression from 23 patients and protein profiles plus COSMIC mutation data, identifying prominent intrinsically disordered regions and links between drug perturbation and disease signatures. Results indicate broad applicability to disease states and drug effects.",{"@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/structural-analysis-of-genomic-and-proteomic-signatures-reveal-dynamic-expression-of-intrinsically-disordered-regions-in-breast-cancer-and-tissue/385546/",{"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/structural-analysis-of-genomic-and-proteomic-signatures-reveal-dynamic-expression-of-intrinsically-disordered-regions-in-breast-cancer-and-tissue/385546.png","ImageObject",300,407,{"name":92,"@type":93},"MrHarris58","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-27","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":8},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is SAGES in this study?","Question",{"text":112,"@type":113},"SAGES (Structural Analysis of Gene and protein Expression Signatures) is a method that describes expression data using features derived from sequence-based predictions and 3D structural models.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the analyses performed for breast cancer and tissue?",{"text":117,"@type":113},"The method combined SAGES with machine learning, using gene expression data from breast cancer patients, protein expression profiles, and genetic mutation information from COSMIC, then compared healthy and cancer tissues.",{"name":119,"@type":110,"acceptedAnswer":120},"What key biological findings were reported?",{"text":121,"@type":113},"The study reports prominent expression of intrinsically disordered regions in breast cancer proteins and identifies relationships between drug perturbation signatures and breast cancer disease signatures.","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},385546,1790470912,{"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":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},687212988360,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","bioRxiv preprint doi: [https://doi.org/10.1101/2023.02.23.529755](https://doi.org/10.1101/2023.02.23.529755); this version posted February 24, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made  \navailable under aCC-BY-ND 4.0 International license.  \nStructural Analysis of Genomic and Proteomic Signatures Reveal Dynamic Expression of Intrinsically Disordered Regions in Breast Cancer and Tissue  \nNicole Zatorski1, Yifei Sun1, Abdulkadir Elmas2, Christian Dallago3,4, Timothy Karl4, David Stein1,  \nBurkhard Rost4, Kuan-Lin Huang2, Martin Walsh1, Avner Schlessinger1  \n1. Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave Levey Pl NY, NY 10029, USA  \n2. Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave Levey Pl NY, NY 10029, USA  \n3. NVIDIA DE GmbH, Einsteinstraße 172, 81677 München, Germany  \n4. Faculty of Informatics, Bioinformatics & Computational Biology, Technical University Munich (TUM), 85748 Garching, Germany  \n[Email: nicole.zatorski@icahn.mssm.edu](Email: nicole.zatorski@icahn.mssm.edu); [avner.schlessinger@mssm.edu](avner.schlessinger@mssm.edu)  \nSummary  \nStructural features of proteins capture underlying information about protein evolution and function, which enhances the analysis of proteomic and transcriptomic data. Here we develop Structural Analysis of Gene and protein Expression Signatures (SAGES), a method that describes expression data using features calculated from sequence-based prediction methods and 3D structural models. We used SAGES, along with machine learning, to characterize tissues from healthy individuals and those with breast cancer. We analyzed gene expression data from 23 breast cancer patients and genetic mutation data from the COSMIC database as well as 17 breast tumor protein expression profiles. We identified prominent expression of intrinsically disordered regions in breast cancer proteins as well as relationships between drug perturbation signatures and breast cancer disease signatures. Our results suggest that SAGES is generally applicable to describe diverse biological phenomena including disease states and drug effects.  \nKeywords: protein structural features, breast cancer, human precision transcriptomics and proteomics  \n1. Introduction  \nWith the advent of improved sequencing technology there has been a great emphasis placed on using proteomic and transcriptomic data to understand underlying disease etiologies and characteristics 1. This has led to successful identification of biomarkers that have advanced precision medicine2 in fields such as oncology3. For example, single cell RNA sequencing on primary breast cancer tumors has explored the heterogeneity of gene expression in tumor tissue and the preponderance of immune cell response to disease4. Analysis of gene sets from RNA expression can be based on a comparison of the gene names found to be differentially expressed in a sample population as compared to the control population. The alternative, well-established method for analyzing sequencing data is through the use of a gene ontology (GO) enrichment of the differentially expressed genes, which provides a standardized vocabulary and relationship for genes5. This gives a slightly more nuanced view of the signature; in that it provides annotations about the function of proteins encoded by genes. In proteomic analysis, protein names or fragments  \nbioRxiv preprint doi: [https://doi.org/10.1101/2023.02.23.529755](https://doi.org/10.1101/2023.02.23.529755); this version posted February 24, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made  \navailable under aCC-BY-ND 4.0 International license.  \nof protein sequences found u","cbCaieoqG3B6R3B2","https://ap.wps.com/l/cbCaieoqG3B6R3B2","pdf",307622,25,"English","# Summary\n# Keywords\n# 1. Introduction\n## Sequencing and precision medicine context\n## Gene ontology enrichment and proteomic abundance analysis\n## Structural features and limitations of current approaches\n## Structural representation and computational modeling\n## Developing and applying SAGES","[{\"question\":\"What is SAGES in this study?\",\"answer\":\"SAGES (Structural Analysis of Gene and protein Expression Signatures) is a method that describes expression data using features derived from sequence-based predictions and 3D structural models.\"},{\"question\":\"How were the analyses performed for breast cancer and tissue?\",\"answer\":\"The method combined SAGES with machine learning, using gene expression data from breast cancer patients, protein expression profiles, and genetic mutation information from COSMIC, then compared healthy and cancer tissues.\"},{\"question\":\"What key biological findings were reported?\",\"answer\":\"The study reports prominent expression of intrinsically disordered regions in breast cancer proteins and identifies relationships between drug perturbation signatures and breast cancer disease signatures.\"}]","Structural Analysis of Genomic and Proteomic Signatures Reveal Dynamic Expression of Intrinsically Disordered Regions in Breast Cancer and Tissue | PDF",1790269943,63]