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A prognostic model was developed using disulfidptosis-related long noncoding RNAs (DRLRs) to distinguish high-grade (G3–G4) from low-grade (G1–G2) hepatocellular carcinoma. Based on TCGA transcriptomic data, univariate, LASSO, and multivariate Cox analyses selected nine DRLRs to form a 9-DRLR risk signature with strong discrimination (AUC 0.845, 0.841, 0.885 for 1-, 3-, 5-year survival). High-risk patients showed immune dysfunction markers, higher TMB, and increased sensitivity to 5-fluorouracil and Dasatinib. AL031985.3 was highlighted as a key oncogenic lncRNA, with significant overexpression, qRT-PCR validation, in vitro suppression of colony formation and invasion upon knockdown, and in vivo reduction of tumor growth and EMT changes, establishing DRLR prognostic value and AL031985.3 as a potential target.",{"@graph":69,"@context":121},[70,84,104],{"@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/a-prognostic-model-utilizing-disulfidptosis-related-long-noncoding-rnas-to-differentiate-pathological-grades-in-hepatocellular-carcinoma-functional-analysis-of-al0319853/352898/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-prognostic-model-utilizing-disulfidptosis-related-long-noncoding-rnas-to-differentiate-pathological-grades-in-hepatocellular-carcinoma-functional-analysis-of-al0319853/352898.png","ImageObject",300,407,{"name":92,"@type":93},"Putri","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-22",true,{"@type":101,"interactionType":102,"userInteractionCount":4},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107,113,117],{"name":108,"@type":109,"acceptedAnswer":110},"What is the purpose of the DRLR prognostic model in hepatocellular carcinoma?","Question",{"text":111,"@type":112},"It is designed to distinguish high-grade (G3–G4) from low-grade (G1–G2) hepatocellular carcinoma using disulfidptosis-related long noncoding RNAs and to stratify prognosis by grade.","Answer",{"name":114,"@type":109,"acceptedAnswer":115},"How were the nine key DRLRs selected for the risk signature?",{"text":116,"@type":112},"The study used transcriptomic data from TCGA and applied univariate Cox regression, LASSO regression, and multivariate Cox regression to identify nine DRLRs used to construct the 9-DRLR model.",{"name":118,"@type":109,"acceptedAnswer":119},"Why is AL031985.3 considered important in the model?",{"text":120,"@type":112},"AL031985.3 showed significant overexpression in HCC tissues versus adjacent controls, and its knockdown suppressed colony formation and invasion in vitro and attenuated tumor growth with altered EMT markers in vivo.","https://schema.org",{"og:url":83,"og:type":123,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":125,"canonical":83},"index,follow",{"doc_id":127,"site_id":62},352898,1790101929,{"code":4,"msg":5,"data":130},{"doc_id":127,"user_id":131,"nickname":92,"user_avatar":132,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":4,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":138,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":128,"read_time":143},962085571259,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Yang et al. World Journal of Surgical Oncology (2026) 24:197  \n[https://doi.org/10.1186/s12957-026-04311-9](https://doi.org/10.1186/s12957-026-04311-9)  \nWorld Journal of Surgical Oncology  \nRESEARCH Open Access  \nA prognostic model utilizing disulfidptosis- related long noncoding RNAs to differentiate pathological grades in hepatocellular carcinoma: functional analysis of AL031985.3  \nLing Yang1†, Fei Li2†, Hailin Yu3†, Xuefeng Gu1 and Wei You2,4*  \nAbstract  \nDisulfidptosis, a recently identified form of cell death, plays a crucial role in cancer progression, specifically by altering intracellular disulfide bonds within the actin cytoskeleton. Our research centres on developing a prognostic model based on disulfidptosis-related long noncoding RNAs (DRLRs) to differentiate high-grade (G3-G4) from low-grade (G1-G2) Hepatocellular Carcinoma (HCC) . Utilizing transcriptomic data from The Cancer Genome Atlas (TCGA), we employed univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator(LASSO) regression, and multivariate Cox regression to ultimately identify nine key drlncRNAs (AL442125.2, AC018529.2, AL031985.3, AC119150. 1, LINC02256, AC026979.4, POLH-AS1, MED8-AS1, and AC026356. 1) . These were subsequently used to construct a prognostic risk signature model. This 9-DRLR model demonstrated robust discriminative capacity, achieving Area Under the Curve(AUCs) of 0. 845, 0. 841, and 0.885 for 1-, 3-, and 5-year overall survival prediction in G3-G4 HCC patients, respectively. The model provides strong prognostic stratification of HCC patients by grade and correlates with biological processes linked to tumour aggressiveness and immune evasion, as shown by Gene Ontology (GO) and gene set enrichment analyses (GSEA) . High-risk groups exhibited markers of immune dysfunction, elevated tumour mutation burden (TMB), and increased sensitivity to targeted therapies including 5-Fluorouracil and Dasatinib. AL031985.3 emerged as a key lncRNA within this model, with its significant overexpression in HCC tissues versus adjacent controls (p \u003C 0. 001) validated through quantitative Real-Time PCR (qRT-PCR) . Functional analyses revealed AL031985.3 as a critical oncogenic driver, where its knockdown significantly suppressed colony formation (p \u003C 0. 01) and reduced invasive capacity (p \u003C 0. 01) in vitro. These findings were corroborated in vivo, with orthotopic implantation and experimental pulmonary metastasis models demonstrating attenuated tumour growth and modulated epithelial-mesenchymal transition markers following AL031985.3 silencing. Our findings underscore the prognostic value of the DRLR signature and establish AL031985.3 as a potential therapeutic target for advanced HCC.  \n†Ling Yang, Fei Li and Hailin Yu contributed equally to this work.  \n*Correspondence: Wei You [weiyounj025@163.com](weiyounj025@163.com)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/licenses/by-","cbCaiqzXIdS19v6m","https://ap.wps.com/l/cbCaiqzXIdS19v6m","pdf",9203457,25,"English","# Abstract\n# Introduction\n## Background and clinical challenge in HCC\n## Edmondson-Steiner (E-S) grading system and TCGA grading\n## Grade 1–4 pathological differentiation criteria","[{\"question\":\"What is the purpose of the DRLR prognostic model in hepatocellular carcinoma?\",\"answer\":\"It is designed to distinguish high-grade (G3–G4) from low-grade (G1–G2) hepatocellular carcinoma using disulfidptosis-related long noncoding RNAs and to stratify prognosis by grade.\"},{\"question\":\"How were the nine key DRLRs selected for the risk signature?\",\"answer\":\"The study used transcriptomic data from TCGA and applied univariate Cox regression, LASSO regression, and multivariate Cox regression to identify nine DRLRs used to construct the 9-DRLR model.\"},{\"question\":\"Why is AL031985.3 considered important in the model?\",\"answer\":\"AL031985.3 showed significant overexpression in HCC tissues versus adjacent controls, and its knockdown suppressed colony formation and invasion in vitro and attenuated tumor growth with altered EMT markers in vivo.\"}]","A prognostic model utilizing disulfidptosis-related long noncoding RNAs to differentiate pathological grades in hepatocellular carcinoma - functional analysis of AL031985.3 | PDF",63]