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Using CESC data from The Cancer Genome Atlas (TCGA), patients were split into training and testing groups to build an RNA editing-based risk model via Cox regression and LASSO selection. Risk scores supported subgrouping into high- and low-risk patients, and a nomogram integrated risk scores and clinical factors. The study further related RNA editing levels at prognostic sites to host gene expression and adenosine deaminase acting on RNA, then compared immune and tumor-related pathways among differentially expressed genes using enrichment analysis.",{"@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/development-and-assessment-of-an-rna-editing-based-risk-model-for-the-prognosis-of-cervical-cancer-patients/342771/",{"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/development-and-assessment-of-an-rna-editing-based-risk-model-for-the-prognosis-of-cervical-cancer-patients/342771.png","ImageObject",300,407,{"name":92,"@type":93},"Eliana","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-23","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What data and statistical approach were used to build the RNA editing-based risk model?","Question",{"text":112,"@type":113},"CESC-related information was obtained from The Cancer Genome Atlas (TCGA) and patients were randomly assigned to training and testing groups. Cox regression and LASSO were used to establish the risk model.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are patients categorized using the constructed risk model?",{"text":117,"@type":113},"The model generates a risk score for each patient, and the median score is used to divide patients into high-risk and low-risk subgroups.",{"name":119,"@type":110,"acceptedAnswer":120},"What biological relationships and pathways were analyzed to interpret the model?",{"text":121,"@type":113},"The study examined how RNA editing levels at prognostic sites relate to host gene expression and adenosine deaminase acting on RNA. It also compared immune response and tumor progression-related functions and pathways between subgroups using enrichment analysis.","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},342771,1790180151,{"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":14,"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},4398048949847,"https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267","®  \n Diagnostic Accuracy Study   \nDevelopment and assessment of an RNA editingbased risk model for the prognosis of cervical cancer patients  \nZihan Zhu, MSa , Jing Lu, MDb ,*  \n\n| Abstract\u003Cbr>RNA editing, as an epigenetic mechanism, exhibits a strong correlation with the occurrence and development of cancers. Nevertheless, few studies have been conducted to investigate the impact of RNA editing on cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) . In order to study the connection between RNA editing and CESC patients’ prognoses, we obtained CESC-related information from The Cancer Genome Atlas (TCGA) database and randomly allocated the patients into the training group or testing group. An RNA editing-based risk model for CESC patients was established by Cox regression analysis and least absolute shrinkage and selection operator (LASSO) . According to the median score generated by this RNA editing-based risk model, patients were categorized into subgroups with high and low risks. We further constructed the nomogram by risk scores and clinical characteristics and analyzed the impact of RNA editing levels on host gene expression levels and adenosine deaminase acting on RNA. Finally, we also compared the biological functions and pathways of differentially expressed genes (DEGs) between different subgroups by enrichment analysis. In this risk model, we screened out 6 RNA editing sites with significant prognostic value. The constructed nomogram performed well in forecasting patients’ prognoses. Furthermore, the level of RNA editing at the prognostic site exhibited a strong correlation with host gene expression. In the high-risk subgroup, we observed multiple biological functions and pathways associated with immune response, cell proliferation, and tumor progression. This study establishes an RNA editing-based risk model that helps forecast patients’ prognoses and offers a new understanding of the underlying mechanism of RNA editing in CESC. |\n| --- |\n| Abbreviations: ADARs = adenosine deaminases acting on RNA , AUC = area under the curve, C-index = concordance index, CC = cellular component, CESC = cervical squamous cell carcinoma and endocervical adenocarcinoma, BP = biological process, DEGs = differentially expressed genes, GO = gene ontology, GSEA = gene set enrichment analysis, HPV = human papillomavirus, KEGG = Kyoto encyclopedia of genes and genomes, LASSO = least absolute shrinkage and selection operator, MF = molecular function, OS = overall survival, PFS = progression-free survival, ROC = receiver operating characteristic, TCGA = The Cancer Genome Atlas. |\n| Keywords: cervical squamous cell carcinoma and endocervical adenocarcinoma, prognostic model, RNA editing, tumor immune microenvironment |\n\n1. Introduction  \nCervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are among the most prevalent types of cancer affecting women globally, ranking fourth in both incidence and mortality.[1] Squamous cell carcinoma and adenocarcinoma are the predominant histological subtypes in CESC, comprising around 70% and 25% of CESC cases, respectively.[2,3] CESC develops primarily due to continued human papillomavirus (HPV) infection,[4,5] and thus is often prevented through HPV  \nscreening and vaccination. [3,6,7] Nevertheless, HPV testing is negative in a small number of CESC cases, which include true HPV-negative cancer patients and false-negative cases. [8–10] This has some detrimental implications for strategies based on HPV screening and vaccination to prevent CESC. In clinical practice, treatment methods for CESC patients mainly involve hysterectomy, chemotherapy, radiotherapy or adjuvant therapy.[3] Although patients with early-stage CESC have relatively good outcomes after treatment, those with  \nAll authors have reviewed the manuscript and consented for publication. The authors have no funding and conflicts of interest to disclose.  \nThe datasets generated during and/or analyzed during the","cbCaioD5o3m8TvKM","https://ap.wps.com/l/cbCaioD5o3m8TvKM","pdf",3788133,11,"English","# Introduction\n## Background and rationale\n## Clinical management and need for prognostic biomarkers\n# Methods and model construction\n## Data source and patient grouping\n## Cox regression and LASSO\n# Risk stratification and nomogram\n## High- vs low-risk subgrouping\n## Predictive performance of the nomogram\n# Biological interpretation\n## RNA editing and host gene expression\n## Enrichment analysis of differentially expressed genes","[{\"question\":\"What data and statistical approach were used to build the RNA editing-based risk model?\",\"answer\":\"CESC-related information was obtained from The Cancer Genome Atlas (TCGA) and patients were randomly assigned to training and testing groups. Cox regression and LASSO were used to establish the risk model.\"},{\"question\":\"How are patients categorized using the constructed risk model?\",\"answer\":\"The model generates a risk score for each patient, and the median score is used to divide patients into high-risk and low-risk subgroups.\"},{\"question\":\"What biological relationships and pathways were analyzed to interpret the model?\",\"answer\":\"The study examined how RNA editing levels at prognostic sites relate to host gene expression and adenosine deaminase acting on RNA. It also compared immune response and tumor progression-related functions and pathways between subgroups using enrichment analysis.\"}]","Development and assessment of an RNA editing-based risk model for the prognosis of cervical cancer patients | PDF",1790048162,28]