{"title":"基于计算机断层扫描的放射组学预测成纤维细胞相关基因 EZH2 的表达水平和肝细胞癌的存活率","authors":"Ting-Yu Yu, Ze-Juan Zhan, Qi Lin, Zhen-Huan Huang","doi":"10.12998/wjcc.v12.i24.5568","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer. The primary treatment strategies for HCC currently include liver transplantation and surgical resection. However, these methods often yield unsatisfactory outcomes, leading to a poor prognosis for many patients. This underscores the urgent need to identify and evaluate novel therapeutic targets that can improve the prognosis and survival rate of HCC patients.</p><p><strong>Aim: </strong>To construct a radiomics model that can accurately predict the <i>EZH2</i> expression in HCC.</p><p><strong>Methods: </strong>Gene expression, clinical parameters, HCC-related radiomics, and fibroblast-related genes were acquired from public databases. A gene model was developed, and its clinical efficacy was assessed statistically. Drug sensitivity analysis was conducted with identified hub genes. Radiomics features were extracted and machine learning algorithms were employed to generate a radiomics model related to the hub genes. A nomogram was used to illustrate the prognostic significance of the computed Radscore and the hub genes in the context of HCC patient outcomes.</p><p><strong>Results: </strong><i>EZH2</i> and <i>NRAS</i> were independent predictors for prognosis of HCC and were utilized to construct a predictive gene model. This model demonstrated robust performance in diagnosing HCC and predicted an unfavorable prognosis. A negative correlation was observed between <i>EZH2</i> expression and drug sensitivity. Elevated <i>EZH2</i> expression was linked to poorer prognosis, and its diagnostic value in HCC surpassed that of the risk model. A radiomics model, developed using a logistic algorithm, also showed superior efficiency in predicting <i>EZH2</i> expression. The Radscore was higher in the group with high <i>EZH2</i> expression. A nomogram was constructed to visually demonstrate the significant roles of the radiomics model and <i>EZH2</i> expression in predicting the overall survival of HCC patients.</p><p><strong>Conclusion: </strong><i>EZH2</i> plays significant roles in diagnosing HCC and therapeutic efficacy. A radiomics model, developed using a logistic algorithm, efficiently predicted <i>EZH2</i> expression and exhibited strong correlation with HCC prognosis.</p>","PeriodicalId":23912,"journal":{"name":"World Journal of Clinical Cases","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11269978/pdf/","citationCount":"0","resultStr":"{\"title\":\"Computed tomography-based radiomics predicts the fibroblast-related gene <i>EZH2</i> expression level and survival of hepatocellular carcinoma.\",\"authors\":\"Ting-Yu Yu, Ze-Juan Zhan, Qi Lin, Zhen-Huan Huang\",\"doi\":\"10.12998/wjcc.v12.i24.5568\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer. The primary treatment strategies for HCC currently include liver transplantation and surgical resection. However, these methods often yield unsatisfactory outcomes, leading to a poor prognosis for many patients. This underscores the urgent need to identify and evaluate novel therapeutic targets that can improve the prognosis and survival rate of HCC patients.</p><p><strong>Aim: </strong>To construct a radiomics model that can accurately predict the <i>EZH2</i> expression in HCC.</p><p><strong>Methods: </strong>Gene expression, clinical parameters, HCC-related radiomics, and fibroblast-related genes were acquired from public databases. A gene model was developed, and its clinical efficacy was assessed statistically. Drug sensitivity analysis was conducted with identified hub genes. Radiomics features were extracted and machine learning algorithms were employed to generate a radiomics model related to the hub genes. A nomogram was used to illustrate the prognostic significance of the computed Radscore and the hub genes in the context of HCC patient outcomes.</p><p><strong>Results: </strong><i>EZH2</i> and <i>NRAS</i> were independent predictors for prognosis of HCC and were utilized to construct a predictive gene model. This model demonstrated robust performance in diagnosing HCC and predicted an unfavorable prognosis. A negative correlation was observed between <i>EZH2</i> expression and drug sensitivity. Elevated <i>EZH2</i> expression was linked to poorer prognosis, and its diagnostic value in HCC surpassed that of the risk model. A radiomics model, developed using a logistic algorithm, also showed superior efficiency in predicting <i>EZH2</i> expression. The Radscore was higher in the group with high <i>EZH2</i> expression. A nomogram was constructed to visually demonstrate the significant roles of the radiomics model and <i>EZH2</i> expression in predicting the overall survival of HCC patients.</p><p><strong>Conclusion: </strong><i>EZH2</i> plays significant roles in diagnosing HCC and therapeutic efficacy. A radiomics model, developed using a logistic algorithm, efficiently predicted <i>EZH2</i> expression and exhibited strong correlation with HCC prognosis.</p>\",\"PeriodicalId\":23912,\"journal\":{\"name\":\"World Journal of Clinical Cases\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2024-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11269978/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"World Journal of Clinical Cases\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.12998/wjcc.v12.i24.5568\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Journal of Clinical Cases","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.12998/wjcc.v12.i24.5568","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
Computed tomography-based radiomics predicts the fibroblast-related gene EZH2 expression level and survival of hepatocellular carcinoma.
Background: Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer. The primary treatment strategies for HCC currently include liver transplantation and surgical resection. However, these methods often yield unsatisfactory outcomes, leading to a poor prognosis for many patients. This underscores the urgent need to identify and evaluate novel therapeutic targets that can improve the prognosis and survival rate of HCC patients.
Aim: To construct a radiomics model that can accurately predict the EZH2 expression in HCC.
Methods: Gene expression, clinical parameters, HCC-related radiomics, and fibroblast-related genes were acquired from public databases. A gene model was developed, and its clinical efficacy was assessed statistically. Drug sensitivity analysis was conducted with identified hub genes. Radiomics features were extracted and machine learning algorithms were employed to generate a radiomics model related to the hub genes. A nomogram was used to illustrate the prognostic significance of the computed Radscore and the hub genes in the context of HCC patient outcomes.
Results: EZH2 and NRAS were independent predictors for prognosis of HCC and were utilized to construct a predictive gene model. This model demonstrated robust performance in diagnosing HCC and predicted an unfavorable prognosis. A negative correlation was observed between EZH2 expression and drug sensitivity. Elevated EZH2 expression was linked to poorer prognosis, and its diagnostic value in HCC surpassed that of the risk model. A radiomics model, developed using a logistic algorithm, also showed superior efficiency in predicting EZH2 expression. The Radscore was higher in the group with high EZH2 expression. A nomogram was constructed to visually demonstrate the significant roles of the radiomics model and EZH2 expression in predicting the overall survival of HCC patients.
Conclusion: EZH2 plays significant roles in diagnosing HCC and therapeutic efficacy. A radiomics model, developed using a logistic algorithm, efficiently predicted EZH2 expression and exhibited strong correlation with HCC prognosis.
期刊介绍:
The World Journal of Clinical Cases (WJCC) is a high-quality, peer reviewed, open-access journal. The primary task of WJCC is to rapidly publish high-quality original articles, reviews, editorials, and case reports in the field of clinical cases. In order to promote productive academic communication, the peer review process for the WJCC is transparent; to this end, all published manuscripts are accompanied by the anonymized reviewers’ comments as well as the authors’ responses. The primary aims of the WJCC are to improve diagnostic, therapeutic and preventive modalities and the skills of clinicians and to guide clinical practice in clinical cases.