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This study constructs and validates an LLPS-associated prognostic risk model using single-cell and bulk transcriptomic resources, integrating differential expression, weighted gene co-expression network analysis, and Cox-LASSO selection. The resulting seven-gene signature stratifies patients with distinct immune profiles, shows independent prognostic value, and is supported by experimental RT-qPCR, immunohistochemistry, and functional assays targeting EXOSC8.",{"@graph":69,"@context":126},[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/molecular-characterization-and-prognostic-modeling-of-liquid-liquid-phase-separation-related-genes-in-osteosarcoma-based-on-single-cell-sequencing-and-weighted-gene-co-expression-network-analysis/448482/",{"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/molecular-characterization-and-prognostic-modeling-of-liquid-liquid-phase-separation-related-genes-in-osteosarcoma-based-on-single-cell-sequencing-and-weighted-gene-co-expression-network-analysis/448482.png","ImageObject",300,407,{"name":92,"@type":93},"Rhys","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-04","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What was the main goal of this study in osteosarcoma?","Question",{"text":112,"@type":113},"To construct an LLPS-related prognostic risk model and evaluate how LLPS-associated genes affect osteosarcoma outcomes.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were LLPS scores and the prognostic genes identified?",{"text":117,"@type":113},"LLPS expression scores were computed using a single-cell dataset and bulk transcriptomic data, followed by differential analysis and WGCNA. Overlapping genes were then filtered using Cox regression and LASSO to identify the prognostic set.",{"name":119,"@type":110,"acceptedAnswer":120},"What did the seven-gene signature show in patient stratification?",{"text":121,"@type":113},"Patients were separated into high- and low-risk groups with distinct immunological differences, and the model demonstrated independent prognostic value.",{"name":123,"@type":110,"acceptedAnswer":124},"How was the role of EXOSC8 validated experimentally?",{"text":125,"@type":113},"EXOSC8 upregulation was confirmed in osteosarcoma cell lines and tissues by RT-qPCR and immunohistochemistry. Functional assays (CCK-8, EdU, and clonogenic formation) supported its impact on cell proliferation.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},448482,1790811959,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"language":143,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":144,"faqs":145,"seo_title":146,"seo_description":67,"update_tm":147,"read_time":31},687207024643,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Molecular characterization and prognostic modeling of liquidliquid phase separation-related genes in osteosarcoma based on single-cell sequencing and weighted gene co-expression network analysis  \nXinyu Huang1, Liang Xiong2, Jiaxing Zeng3, Shanhang Li4, Yangjie Cai2, Zhuan Zou1, Mingxiu Yang1, Hening Li5, Yun Liu1, Maolin He1  \n1Department of Spine and Osteopathic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China; 2Department of Traumatic Orthopedic and Hand Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China; 3Department of Traumatic Surgery & Microsurgery & Hand Surgery, Guangxi Zhuang Autonomous Region People’s Hospital, Nanning, China; 4Department of Bone and Soft Tissue Tumor, Guangxi Medical University Cancer Hospital, Nanning, China; 5Department of Oncology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China  \nContributions: (I) Conception and design: X Huang, L Xiong; (II) Administrative support: M He, Y Liu; (III) Provision of study materials or patients: M He, Y Liu; (IV) Collection and assembly of data: J Zeng, Z Zou; (V) Data analysis and interpretation: X Huang, L Xiong, Y Cai, S Li, M Yang, H Li; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.  \nCorrespondence to: Yun Liu, PhD; Maolin He, PhD. Department of Spine and Osteopathic Surgery, The First Affiliated Hospital of Guangxi Medical  \nUniversity, Shuangyong Road 6, Nanning 530021, China. Email: [liuyun@gxmu.edu.cn](liuyun@gxmu.edu.cn); [hemaolin@stu.gxmu.edu.cn](hemaolin@stu.gxmu.edu.cn).  \nBackground: In recent years, liquid-liquid phase separation (LLPS) has garnered increasing attention in the field of oncology. However, its role in osteosarcoma remains largely unexplored. We aimed to construct a prognostic risk model associated with LLPS and to investigate the impact of LLPS-related genes on  \nosteosarcoma briefly.  \nMethods: Based on the single-cell dataset GSE162454, LLPS gene expression scores were calculated tostratify osteosarcoma samples into high and low LLPS expression cohorts, followed by differential analysis between the two groups. Using bulk transcriptomic data from GSE21257, LLPS scores were computed for each sample, and weighted gene co-expression network analysis (WGCNA) was performed to identify modules most strongly related to LLPS. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were performed on the genes that overlap between the two datasets to identify LLPS-related prognostic genes.  \nA prognostic model was subsequently constructed, a nomogram was developed for clinical application, and its  \nindependent prognostic value was evaluated. Additionally, selected results were validated experimentally.  \nResults: The LLPS-related prognostic model comprising seven genes, CCT6A, EXOSC8, ACO2, SEPHS1, SMARCB1, AASDH, and C15orf40, was successfully established and externally validated. Stratification of samples based on this gene signature revealed immunological differences between subgroups. The model was also shown to have independent prognostic value. Reverse-transcription quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) confirmed that EXOSC8 was upregulated in osteosarcoma cell lines and tissues. Cell Counting Kit-8 (CCK-8), EdU incorporation, and clonogenic  \nformation assays demonstrated the effect of EXOSC8 on proliferation.  \nConclusions: This study offers a novel framework for risk stratification in osteosarcoma, identifies seven novel LLPS-associated prognostic biomarkers, and presents valuable perspectives on the pathogenesis and  \npotential therapeutic targets of osteosarcoma.  \nKeywords: Single cell; osteosarcoma; liquid-liquid phase separation (LLPS); prognosis  \n© AME Publishing Company. Transl Cancer Res 2025;14(12):8365-8384 | [https://dx.doi.org/10.21037/tcr-2025-1366](https://dx.doi.org/10.21037/tcr-2025-1366)  \n8366 Huang et al. L","cbCaig6WoKC9P7MQ","https://ap.wps.com/l/cbCaig6WoKC9P7MQ","pdf",8860625,"English","# Background\n# Methods\n# Results\n# Conclusions\n# Introduction\n# Key findings\n## What is known and what is new?","[{\"question\":\"What was the main goal of this study in osteosarcoma?\",\"answer\":\"To construct an LLPS-related prognostic risk model and evaluate how LLPS-associated genes affect osteosarcoma outcomes.\"},{\"question\":\"How were LLPS scores and the prognostic genes identified?\",\"answer\":\"LLPS expression scores were computed using a single-cell dataset and bulk transcriptomic data, followed by differential analysis and WGCNA. Overlapping genes were then filtered using Cox regression and LASSO to identify the prognostic set.\"},{\"question\":\"What did the seven-gene signature show in patient stratification?\",\"answer\":\"Patients were separated into high- and low-risk groups with distinct immunological differences, and the model demonstrated independent prognostic value.\"},{\"question\":\"How was the role of EXOSC8 validated experimentally?\",\"answer\":\"EXOSC8 upregulation was confirmed in osteosarcoma cell lines and tissues by RT-qPCR and immunohistochemistry. Functional assays (CCK-8, EdU, and clonogenic formation) supported its impact on cell proliferation.\"}]","Molecular characterization and prognostic modeling of liquid-liquid phase separation-related genes in osteosarcoma - Based on single-cell sequencing and weighted gene co-expression network analysis | PDF",1790727312]