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Instead of relying primarily on single-gene or differential expression analyses, this study targets crucial molecular interplay underlying acquired gastric cancer drug resistance by identifying differentially regulated gene networks. The proposed approach measures network dissimilarity using Laplacian eigenvalues, selecting sparse eigen loadings to capture informative graph eigenstructure while limiting noise. Network-constrained regularization incorporates network biology knowledge to improve interpretability. Monte Carlo simulations demonstrate strong performance for differentially regulated gene network identification. Applied results highlight drug resistance markers supported by literature, with COL4A1, PXDN, and TGFBI and their extracellular pathway-enriched interplays suggesting opportunities to enhance chemosensitivity.",{"@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/sparse-spectral-graph-analysis-and-its-application-to-gastric-cancer-drug-resistance-specific-molecular-interplays-identification/332200/",{"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/sparse-spectral-graph-analysis-and-its-application-to-gastric-cancer-drug-resistance-specific-molecular-interplays-identification/332200.png","ImageObject",300,407,{"name":92,"@type":93},"Maeve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why does the study move beyond single-gene or differential expression analysis for drug resistance?","Question",{"text":112,"@type":113},"Drug resistance arises from perturbations in complex molecular networks. Single-gene analysis cannot capture network-level abnormalities that drive acquired drug resistance.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the proposed method quantify differences between gene networks?",{"text":117,"@type":113},"It measures network dissimilarity based on eigenvalues of the Laplacian matrix and determines network eigenstructure via sparse eigen loadings to reduce noise effects.",{"name":119,"@type":110,"acceptedAnswer":120},"What biological insight does the study derive from applying the method to gastric cancer?",{"text":121,"@type":113},"The analysis identifies drug resistance markers, including COL4A1, PXDN, and TGFBI, whose suppression and/or induction and related extracellular pathway interplays may help enhance chemosensitivity.","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},332200,1791187187,{"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":39,"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,"preview_page_count":4},5909877438554,"https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272","PLOS ONE  \nOPEN ACCESS  \nCitation: Park H, Miyano S (2024) Sparse spectral graph analysis and its application to gastric cancer drug resistance-specific molecular interplays identification. PLoS ONE 19(7): e0305386 . [https://](https://)[ ](https://)[doi.org/10.1371/journal.pone.0305386](doi.org/10.1371/journal.pone.0305386)  \nEditor: Maria Ines Fariello Rico, Universidad de la  \nRepublica Uruguay: Facultad de Ingenier´ıa, URUGUAY  \nReceived: September 13, 2023  \nAccepted: May 28, 2024  \nPublished: July 5, 2024  \nCopyright: © 2024 Park, Miyano. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: The third-party datasets used in this study are publicly available from Sanger Genomics of Drug Sensitivity in Cancer (GDSC) dataset ([https://www](https://www). [cancerrxgene.org](cancerrxgene.org)) . The Expression levels (i.e., RMA normalised expression data for cell-lines) are available here ([https://www.cancerrxgene.org/](https://www.cancerrxgene.org/)[ ](https://www.cancerrxgene.org/)[gdsc1000/GDSC1000_WebResources/Home.](gdsc1000/GDSC1000_WebResources/Home.)[ ](gdsc1000/GDSC1000_WebResources/Home.)[html](html)) . The drug sensitivities (i.e., log of IC50 values: GDSC1-dataset) are also available here ([https://www.cancerrxgene.org/downloads/bulk_](https://www.cancerrxgene.org/downloads/bulk_)[download](download)).  \nRESEARCH ARTICLE  \nSparse spectral graph analysis and its application to gastric cancer drug resistancespecific molecular interplays identification  \nHeewon Park1 *, Satoru Miyano2,3  \n1 School of Mathematics, Statistics and Data Science, Sungshin Women’s University, Seoul, Republic of Korea, 2 M&D Data Science Center, Tokyo Medical and Dental University, Yushima, Bunkyo-ku, Tokyo, Japan, 3 Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan  \n* [hwpark@sungshin.ac.kr](hwpark@sungshin.ac.kr)  \nAbstract  \nUncovering acquired drug resistance mechanisms has garnered considerable attention as drug resistance leads to treatment failure and death in patients with cancer. Although several bioinformatics studies developed various computational methodologies to uncover the drug resistance mechanisms in cancer chemotherapy, most studies were based on individual or differential gene expression analysis. However the single gene-based analysis is not enough, because perturbations in complex molecular networks are involved in anti-cancer drug resistance mechanisms. The main goal of this study is to reveal crucial molecular interplay that plays key roles in mechanism underlying acquired gastric cancer drug resistance. To uncover the mechanism and molecular characteristics of drug resistance , we propose a novel computational strategy that identified the differentially regulated gene networks. Our method measures dissimilarity of networks based on the eigenvalues of the Laplacian matrix. Especially, our strategy determined the networks’ eigenstructure based on sparse eigen loadings, thus, the only crucial features to describe the graph structure are involved in the eigenanalysis without noise disturbance. We incorporated the network biology knowledge into eigenanalysis based on the network-constrained regularization. Therefore, we can achieve a biologically reliable interpretation of the differentially regulated gene network identification. Monte Carlo simulations show the outstanding performances of the proposed methodology for differentially regulated gene network identification. We applied our strategy to gastric cancer drug-resistant-specific molecular interplays and related markers. The identified drug resistance markers are verified through the literature. Our results suggest that the suppression and/or induction of COL4A1, PXDN and TGFBI and their molecular inter","cbCaigRV97G0UJ25","https://ap.wps.com/l/cbCaigRV97G0UJ25","pdf",1517764,22,"English","# Abstract\n## Introduction\n## Data and Methods\n## Results\n## Discussion\n## Conclusion","[{\"question\":\"Why does the study move beyond single-gene or differential expression analysis for drug resistance?\",\"answer\":\"Drug resistance arises from perturbations in complex molecular networks. Single-gene analysis cannot capture network-level abnormalities that drive acquired drug resistance.\"},{\"question\":\"How does the proposed method quantify differences between gene networks?\",\"answer\":\"It measures network dissimilarity based on eigenvalues of the Laplacian matrix and determines network eigenstructure via sparse eigen loadings to reduce noise effects.\"},{\"question\":\"What biological insight does the study derive from applying the method to gastric cancer?\",\"answer\":\"The analysis identifies drug resistance markers, including COL4A1, PXDN, and TGFBI, whose suppression and/or induction and related extracellular pathway interplays may help enhance chemosensitivity.\"}]","Sparse spectral graph analysis and its application to gastric cancer drug resistance - specific molecular interplays identification | PDF",1789997291,55]