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This study links ferroptosis and disulfidptosis to prognosis by building a risk model using ferroptosis- and disulfidptosis-associated genes and machine-learning on RNA-seq, mutation, and clinical data from TCGA, GEO, and ICGC. Unsupervised clustering defined two HCC clusters with distinct immune traits; the risk score (RS) independently predicted outcomes, separated high/low risk groups, associated with immunosuppressive microenvironments, immune markers, immune checkpoint expression, cancer stemness similarity, and differential drug sensitivity.",{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/novel-application-of-the-ferroptosis-related-genes-risk-model-associated-with-disuldptosis-in-hepatocellular-carcinoma-prognosis-and-immune-inltration-abstract/338944/",{"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/novel-application-of-the-ferroptosis-related-genes-risk-model-associated-with-disuldptosis-in-hepatocellular-carcinoma-prognosis-and-immune-inltration-abstract/338944.png","ImageObject",300,407,{"name":92,"@type":93},"Himbo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of the study on HCC prognosis?","Question",{"text":112,"@type":113},"To develop a risk prognosis model based on genes associated with ferroptosis-related processes and disulfidptosis, enabling survival prediction and patient stratification.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the risk groups and prognostic clusters determined?",{"text":117,"@type":113},"The study used unsupervised clustering and calculated a risk score (RS) from ferroptosis/disulfidptosis-associated genes, then divided samples into high- and low-risk groups across TCGA, GEO, and ICGC.",{"name":119,"@type":110,"acceptedAnswer":120},"What immune and biological differences were observed between high- and low-risk groups?",{"text":121,"@type":113},"The high-risk group showed a more immunosuppressive microenvironment, higher expression of TMB, immune checkpoints, and HLA, and greater similarity between tumor cells and stem cells.","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},338944,1790975658,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"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},687197100911,"https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697","Submitted 13 July 2023  \nAccepted 31 December 2023 Published 2 February 2024  \nCorresponding authors Li Zhang, [syszhang@hotmail.com](syszhang@hotmail.com)[ ](syszhang@hotmail.com)Min Peng, [mpeng320@whu.edu.cn](mpeng320@whu.edu.cn)  \nAcademic editor Xin Zhang  \nAdditional Information and Declarations can be found on page 26  \nDOI 10.7717/peerj.16819  \nCopyright 2024 Wei et al.  \nDistributed under  \nCreative Commons CC-BY 4.0  \nNovel application of the ferroptosis-related genes risk model associated with disulfidptosis in hepatocellular carcinoma prognosis and immune infiltration  \nJiayan Wei 1, *, Jinsong Wang1, *, Xinyi Chen 1, Li Zhang2 and Min Peng 1  \n1 Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China  \n2 Basic Medical Sciences, Wuhan University School of Basic Medical Sciences, Wuhan, Hubei, China  \n* These authors contributed equally to this work.  \nABSTRACT  \nHepatocellular carcinoma(HCC)stands asthe prevailing manifestation ofprimary liver cancer and continues toposea formidable challenge to human well-being and longevity, owing to its elevated incidence and mortality rates. Nevertheless, the quest for reliable predictive biomarkers for HCC remains ongoing. Recent research has demonstrated a close correlation between ferroptosis and disulfidptosis, two cellular processes, and cancer prognosis, suggesting their potential as predictive factors for HCC. In this study, we employed a combination of bioinformatics algorithms and machine learning techniques, leveraging RNA sequencing data, mutation profiles, and clinical data from HCC samples in The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) databases, to develop a risk prognosis model based on genes associated with ferroptosis and disulfidptosis. We conducted an unsupervised clustering analysis, calculating a risk score (RS) to predict the prognosis of HCC using these genes. Clustering analysis revealed two distinct HCC clusters, each characterized by significantly different prognostic and immune features. The median RS stratified HCC samples in the TCGA, GEO, and ICGC cohorts into high-and low-risk groups. Importantly, RS emerged as an independent prognostic factor in all three cohorts, with the high-risk group  \ndemonstrating poorer prognosis and a more active immunosuppressive microenvironment. Additionally, the high-risk group exhibited higher expression levels of tumor mutation burden (TMB), immune checkpoints (ICs), and human leukocyte antigen (HLA), suggesting a heightened responsiveness to immunotherapy. A cancer stem cell infiltration analysis revealed a higher similarity between tumor cells and stem cells in the high-risk group. Furthermore, drug sensitivity analysis highlighted significant differences in response to antitumor drugs between the two risk groups.  \nIn summary, our risk prognostic model, constructed based on ferroptosis-related genes associated with disulfidptosis, effectively predicts HCC prognosis. These findings hold potential implications for patient stratification and clinical decision-making, offering valuable theoretical insights in this field.  \nSubjects Bioinformatics, Oncology, Pathology  \nKeywords Ferroptosis, Disulfidptosis, HCC, Prognosis model, Tumor microenvironment  \nHow to cite this article Wei J, Wang J, Chen X, Zhang L, Peng M. 2024. Novel application of the ferroptosis-related genes risk model associated with disulfidptosis in hepatocellular carcinoma prognosis and immune infiltration. PeerJ 12:e16819 DOI 10.7717/peerj.16819  \nINTRODUCTION  \nLiver cancer is a significant global contributor to cancer-related mortality, and its incidence is anticipated to rise considerably by 2025 (Llovet et al., 2021; Ganesan & Kulik, 2023) . HCC predominates as the primary liver cancer, comprising 90% of cases, making it the most prevalent pathological type (Forner, Reig & Bruix, 2018) . Prominent risk factors for HCC encompass cirrhosis, hepatitis B and C infecti","cbCaimFDeDjtOT1l","https://ap.wps.com/l/cbCaimFDeDjtOT1l","pdf",33128706,33,"English","# Abstract\n## Introduction","[{\"question\":\"What is the main goal of the study on HCC prognosis?\",\"answer\":\"To develop a risk prognosis model based on genes associated with ferroptosis-related processes and disulfidptosis, enabling survival prediction and patient stratification.\"},{\"question\":\"How were the risk groups and prognostic clusters determined?\",\"answer\":\"The study used unsupervised clustering and calculated a risk score (RS) from ferroptosis/disulfidptosis-associated genes, then divided samples into high- and low-risk groups across TCGA, GEO, and ICGC.\"},{\"question\":\"What immune and biological differences were observed between high- and low-risk groups?\",\"answer\":\"The high-risk group showed a more immunosuppressive microenvironment, higher expression of TMB, immune checkpoints, and HLA, and greater similarity between tumor cells and stem cells.\"}]","Novel application of the ferroptosis-related genes risk model associated with disulfidptosis in hepatocellular carcinoma prognosis and immune infiltration - Abstract | PDF",1790026675,83]