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A cancer stemness index (CSI) is built from public ovarian and pan-cancer datasets and validated in single-cell and PD1/PD-L1 cohorts. The CSI shows strong prognostic and immunotherapy predictive performance with mean AUC values above 0.8, while experiments confirm SNRPE roles in proliferation, migration, and tumor microenvironment remodeling that supports immune evasion.",{"@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/multi-omics-analysis-and-experiments-uncover-the-function-of-cancer-stemness-in-ovarian-cancer-and-establish-a-machine-learning-based-model-for-predicting-immunotherapy-responses/329718/",{"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/multi-omics-analysis-and-experiments-uncover-the-function-of-cancer-stemness-in-ovarian-cancer-and-establish-a-machine-learning-based-model-for-predicting-immunotherapy-responses/329718.png","ImageObject",300,407,{"name":92,"@type":93},"Genevieve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-25","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is predicting immunotherapy response in ovarian cancer difficult?","Question",{"text":112,"@type":113},"Cancer heterogeneity and the complex tumor immune microenvironment make response prediction challenging. The contribution of cancer stemness to resistance to immune checkpoint inhibitors is not fully understood.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is the cancer stemness index (CSI) constructed and validated?",{"text":117,"@type":113},"CSI is developed using genome-scale CRISPR screening of Achilles RNA-seq data and transcriptomic datasets, including ovarian and pan-cancer ICI cohorts, plus single-cell RNA evidence. Validation uses PD1/PD-L1 transcriptomic cohorts and single-cell ICI settings.",{"name":119,"@type":110,"acceptedAnswer":120},"What role does SNRPE play according to the study results?",{"text":121,"@type":113},"SNRPE amplification is linked to remodeling the tumor microenvironment and enabling malignant cells to evade immune recognition. 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Immunol. 15:1486652 .  \ndoi: 10.3389/fimmu.2024.1486652  \nCOPYRIGHT  \n© 2024 Liu, Han, Ji, Wang, Jian, Zhai, Xu, Wang, Wang and Ning. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) .  \nThe use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMulti-omics analysis and experiments uncover the function of cancer stemness in ovarian cancer and establish a machine learning-based model for predicting immunotherapy responses  \nZhibing Liu 1,2†, Lei Han 3†, Xiaoyu Ji 4†, Xiaole Wang 1, Jinbo Jian 1, Yujie Zhai 1, Yingjiang Xu 5, Feng Wang 1, Xiuwen Wang 2* and Fangling Ning 1*  \n1 Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, China,  \n2 Department of Oncology, Qilu Hospital of Shandong University, Jinan, Shandong, China,  \n3 Department of Reproductive Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China, 4 Department of Oncology, Huashan Hospital Fudan University, Shanghai, China, 5 Department of Interventional Vascular Surgery, Binzhou Medical University Hospital, Binzhou, Shandong, China  \nBackground: The heterogeneity of cancer makes it challenging to predict its response to immunotherapy, highlighting the need to find reliable biomarkers for assessment. The sophisticated role of cancer stemness in mediating resistance to immune checkpoint inhibitors (ICIs) is still inadequately comprehended.  \nMethods: Genome-scale CRISPR screening of RNA sequencing data from Project Achilles was utilized to pinpoint crucial genes unique to Ovarian Cancer (OV) . Thirteen publicly accessible OV transcriptomic datasets, seven pan-cancer ICI transcriptomic cohorts, and one single-cell RNA dataset from melanoma patients treated with PD-1 were utilized to scale a novel cancer stemness index (CSI) . An OV single-cell RNA dataset was amassed and scrutinized to uncover the role of Small Nuclear Ribon uc leoprotein Polypeptide E (SNRPE) in the tumor microenvironment (TME) . Vitro experiments were performed to validate the function of SNRPE in promoting proliferation and migration of ovarian cancer.  \nResults: Through the analysis of extensive datasets on ovarian cancer, a specific gene set that impacts the stemness characteristics of tumors has been identified and we unveiled a negative correlation between cancer stemness, and benefits of ICI treatment in single cell ICI cohorts. This identified gene set underpinned the development of the CSI, a groundbreaking tool leveraging advanced machine learning to predict prognosis and immunotherapy responses in ovarian cancer patients. The accuracy of the CSI was further confirmed by applying PD1/PD-L1 ICI transcriptomic cohorts, with a mean AUC exceeding 0.8 for predicting tumor progression and immun","cbCaiheK2gZnlWFw","https://ap.wps.com/l/cbCaiheK2gZnlWFw","pdf",10546976,15,"English","# Background\n# Methods\n# Results\n# Conclusions\n# Introduction","[{\"question\":\"Why is predicting immunotherapy response in ovarian cancer difficult?\",\"answer\":\"Cancer heterogeneity and the complex tumor immune microenvironment make response prediction challenging. The contribution of cancer stemness to resistance to immune checkpoint inhibitors is not fully understood.\"},{\"question\":\"How is the cancer stemness index (CSI) constructed and validated?\",\"answer\":\"CSI is developed using genome-scale CRISPR screening of Achilles RNA-seq data and transcriptomic datasets, including ovarian and pan-cancer ICI cohorts, plus single-cell RNA evidence. Validation uses PD1/PD-L1 transcriptomic cohorts and single-cell ICI settings.\"},{\"question\":\"What role does SNRPE play according to the study results?\",\"answer\":\"SNRPE amplification is linked to remodeling the tumor microenvironment and enabling malignant cells to evade immune recognition. Functional in vitro experiments support SNRPE involvement in ovarian cancer proliferation and migration.\"}]","Multi-omics analysis and experiments uncover the function of cancer stemness in ovarian cancer and establish a machine learning-based model for predicting immunotherapy responses | PDF",1789985324,38]