[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-332496-105":59,"doc-detail-332496-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","comprehensive-analysis-of-consensus-molecular-subtypes-for-ovarian-cancer-from-bulk-to-single-cell-perspectives","Comprehensive analysis of consensus molecular subtypes for ovarian cancer from bulk to single-cell perspectives","","Molecular subtypes guide cancer risk assessment and treatment choices, yet ovarian cancer subtype definitions require integrated, large-cohort evidence across data scales. This study analyzed whole-tissue transcriptomes from 1987 patients across 26 Gene Expression Omnibus cohorts and identified four consensus subtypes (C1–C4). C1 showed poor prognosis with increased M2 macrophage infiltration, while C2 showed the best prognosis with higher CD4 resting T-cell infiltration. Cancer- and stroma-specific ligand–receptor interactions were mapped per subtype. Single-cell analyses further refined malignant epithelial states into four subtypes and tracked C1 enrichment from primary to relapse and metastasis, with C2 declining. A ten-gene risk model derived from subtype signatures was validated across independent datasets, supporting prognostic utility.",{"@graph":69,"@context":123},[70,84,106],{"@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/comprehensive-analysis-of-consensus-molecular-subtypes-for-ovarian-cancer-from-bulk-to-single-cell-perspectives/332496/",{"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/comprehensive-analysis-of-consensus-molecular-subtypes-for-ovarian-cancer-from-bulk-to-single-cell-perspectives/332496.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How many consensus molecular subtypes of ovarian cancer were identified, and what do C1 and C2 represent clinically?","Question",{"text":113,"@type":114},"Four consensus subtypes (C1–C4) were identified. C1 is associated with poor prognosis and higher M2 macrophage infiltration, whereas C2 shows the best prognosis with higher CD4 resting T-cell infiltration.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What biological interactions were characterized across cancer and stromal compartments?",{"text":118,"@type":114},"Cancer- and stromal-specific gene-expression profiles were characterized, and ligand–receptor interactions associated with each molecular subtype were analyzed.",{"name":120,"@type":111,"acceptedAnswer":121},"How was the prognostic risk model built and validated?",{"text":122,"@type":114},"A robust ten-gene risk model was developed using subtype gene signatures for prognostic evaluation, and its efficacy was demonstrated across independent datasets, including a TCGA validation cohort.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},332496,1790035517,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","RESEARCH ARTICLE  \nComprehensive analysis of consensus molecular subtypes for ovarian cancer from bulk to single-cell perspectives  \nReceived for publication, April 14, 2024, and in revised form, August 12, 2024 Published, Papers in Press, August 22, 2024, [https://doi.org/10.1016/j.jbc.2024.107710](https://doi.org/10.1016/j.jbc.2024.107710)  \nZiyan Zhao 1,‡, Linan Xing2,‡, Qian Cheng 1,‡, Zhiyi Wu 1, Fei Xue 1, Yunyi Peng 1, Yuxi Zhang 1, Guixiang Lv3, *, Yongjian Zhang4, *, and Chunlong Zhang 1, *  \nFrom the 1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China; 2Department of Gynecology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; 3Department of Biochemistry and Molecular Biology, Harbin Medical University, Harbin, China; 4Department of Gynecology Oncology, Harbin Medical University Cancer Hospital, Harbin, China  \nReviewed by members of the JBC Editorial Board. Edited by Paul Shapiro  \nMolecular subtypes play a pivotal role in guiding preclinical and clinical risk assessment and treatment strategies in cancer. In this study, we extracted whole-tissue transcriptomic data from 1987 ovarian cancer patients spanning 26 independent Gene Expression Omnibus cohorts. A total of four consensus subtypes (C1–C4) were identified, notably, subtype C1 samples exhibited a poor prognosis and higher M2 macrophages infiltration, whereas subtype C2 samples demonstrated the best prognosis and higher CD4 resting T cells infiltration. Additionally, we characterized cancer- and stromal-specific gene expression profiles, and conducted an analysis of ligand– receptor interactions within these compartments. Based on cancer compartment, subtype-specific interactions as well as gene signatures for each molecular subtype were identified. Leveraging single-cell transcriptomic data, we delineated malignant epithelial cells with four molecular subtypes and observed an increase in C1 cell proportions from primary to relapse to metastasis stages, with a corresponding decrease in C2 cell proportions. Furthermore, we investigated subtypespecific interaction with T cells through integrated analysis of bulk and single-cell datasets. Finally, we developed a robust ten-gene risk model based on subtype gene signatures for prognostic evaluation in ovarian cancer, demonstrating its efficacy across independent datasets. In summary, this study systematically explored ovarian cancer molecular subtypes and provided a framework for other cancer types.  \nOvarian cancer (OC) is a malignant neoplasm affecting the female reproductive system and represents a significant cause of mortality among women worldwide (1). Recent improvements in cancer survival rates can be largely attributed to pioneering research and continuous advancements in screening, surgical techniques, and therapeutic approaches. While cancer classification traditional relies on histopathological and clinical criteria, molecular data offer the potential  \n‡ These authors contributed equally to this work.  \n* For correspondence: Chunlong Zhang, [zhangchunlong@hrbmu.edu.cn](zhangchunlong@hrbmu.edu.cn); Yongjian Zhang, [zyj951898305@163.com](zyj951898305@163.com); Guixiang Lv, lgxcloud@163. com.  \nto delineate subtypes with distinct biological characteristics and prognostic implications (2, 3). However, there remains a need for integrated analysis utilizing large-scale datasets to comprehensively define OC molecular subtypes and to explore their systemic functions.  \nTumors are complex entities composed of heterogeneous populations of cancer cells and infiltrating nontumor cells, representing a fundamental characteristic of malignancy. Previous studies have focused on assessing the cellular composition within individual tumor samples (4, 5). OC patients commonly exhibit high recurrence rates, poor long-term survival, and extensive intertumoral and intratumoral heterogeneity (6). Understanding the mechanisms driving tumor heterogeneous remains a","cbCaikfG5C2qfjQj","https://ap.wps.com/l/cbCaikfG5C2qfjQj","pdf",4792424,11,"English","# Background and Rationale\n## Need for integrated bulk–single-cell subtype analysis\n# Dataset Integration and Consensus Subtype Discovery\n## Extraction of bulk transcriptomes from GEO cohorts\n## Identification of four consensus subtypes (C1–C4)\n# Subtype-Associated Biology and Microenvironment\n## Prognostic differences and immune infiltration patterns\n## Cancer- and stromal-specific gene-expression profiles\n## Ligand–receptor interaction mapping\n# Single-Cell Refinement and Dynamic Changes\n## Four malignant epithelial subtypes\n## Proportion shifts across primary, relapse, and metastasis\n# T-Cell Associations via Integrated Bulk–Single-Cell Analysis\n# Prognostic Modeling and Validation\n## Ten-gene risk model performance across independent cohorts","[{\"question\":\"How many consensus molecular subtypes of ovarian cancer were identified, and what do C1 and C2 represent clinically?\",\"answer\":\"Four consensus subtypes (C1–C4) were identified. C1 is associated with poor prognosis and higher M2 macrophage infiltration, whereas C2 shows the best prognosis with higher CD4 resting T-cell infiltration.\"},{\"question\":\"What biological interactions were characterized across cancer and stromal compartments?\",\"answer\":\"Cancer- and stromal-specific gene-expression profiles were characterized, and ligand–receptor interactions associated with each molecular subtype were analyzed.\"},{\"question\":\"How was the prognostic risk model built and validated?\",\"answer\":\"A robust ten-gene risk model was developed using subtype gene signatures for prognostic evaluation, and its efficacy was demonstrated across independent datasets, including a TCGA validation cohort.\"}]","Comprehensive analysis of consensus molecular subtypes for ovarian cancer from bulk to single-cell perspectives | PDF",1789998478,28]