Study on the prediction model of liver cancer based on chronic liver disease and the related molecular mechanism.

IF 3.7 3区 医学 Q2 GASTROENTEROLOGY & HEPATOLOGY
Xiaojing Zhang, Xinye Chen
{"title":"Study on the prediction model of liver cancer based on chronic liver disease and the related molecular mechanism.","authors":"Xiaojing Zhang, Xinye Chen","doi":"10.1016/j.aohep.2024.101572","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction and objective: </strong>Due to the high heterogeneity of HCC, which leads to poor prognostic outcomes for patients, there is a need to develop a novel predictive model for accurate classification of HCC in order to improve patient survival rates.</p><p><strong>Materials and methods: </strong>The data of the HCV, cirrhosis, and HCC were obtained from TCGA and GEO databases. Multivariable Cox regression analysis and survival analysis was conducted to assess the prognostic relevance of these differentially expressed genes. Single-cell sequencing was used to explore the intercellular interaction patterns and identify relevant signaling pathways. Drug sensitivity analysis was conducted to determine personalized treatment strategies for patients.</p><p><strong>Results: </strong>In this study, we conducted integrated analysis of hepatitis, cirrhosis, and hepatocellular carcinoma datasets and identified 10 liver disease progression genes associated with prognosis. These genes exhibited significant downregulation in expression as the disease advanced, suggesting their crucial involvement in HCC development. By performing multivariable Cox analysis, we established a prognostic model for liver disease progression to predict the prognosis of HCC patients. The model was validated using ROC analysis, demonstrating good accuracy and stability in prognostic evaluation. Single-cell sequencing analysis revealed that these genes primarily exert their effects through the MIF signaling pathway during HCC progression. Furthermore, we observed that patients in the low-risk group exhibited higher sensitivity to TACE treatment, while patients in the high-risk group showed better response to sorafenib treatment.</p><p><strong>Conclusions: </strong>In summary, we have elucidated the key genes involved in the progression of liver diseases and established a precise prognostic model for assessing the prognosis of HCC patients. Our study provides novel insights and strategies for the treatment of HCC.</p>","PeriodicalId":7979,"journal":{"name":"Annals of hepatology","volume":" ","pages":"101572"},"PeriodicalIF":3.7000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of hepatology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.aohep.2024.101572","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Introduction and objective: Due to the high heterogeneity of HCC, which leads to poor prognostic outcomes for patients, there is a need to develop a novel predictive model for accurate classification of HCC in order to improve patient survival rates.

Materials and methods: The data of the HCV, cirrhosis, and HCC were obtained from TCGA and GEO databases. Multivariable Cox regression analysis and survival analysis was conducted to assess the prognostic relevance of these differentially expressed genes. Single-cell sequencing was used to explore the intercellular interaction patterns and identify relevant signaling pathways. Drug sensitivity analysis was conducted to determine personalized treatment strategies for patients.

Results: In this study, we conducted integrated analysis of hepatitis, cirrhosis, and hepatocellular carcinoma datasets and identified 10 liver disease progression genes associated with prognosis. These genes exhibited significant downregulation in expression as the disease advanced, suggesting their crucial involvement in HCC development. By performing multivariable Cox analysis, we established a prognostic model for liver disease progression to predict the prognosis of HCC patients. The model was validated using ROC analysis, demonstrating good accuracy and stability in prognostic evaluation. Single-cell sequencing analysis revealed that these genes primarily exert their effects through the MIF signaling pathway during HCC progression. Furthermore, we observed that patients in the low-risk group exhibited higher sensitivity to TACE treatment, while patients in the high-risk group showed better response to sorafenib treatment.

Conclusions: In summary, we have elucidated the key genes involved in the progression of liver diseases and established a precise prognostic model for assessing the prognosis of HCC patients. Our study provides novel insights and strategies for the treatment of HCC.

基于慢性肝病的肝癌预测模型及相关分子机制研究。
导言和目的:由于 HCC 的高度异质性导致患者预后不良,因此需要开发一种新型预测模型对 HCC 进行准确分类,以提高患者的生存率:HCV、肝硬化和HCC的数据来自TCGA和GEO数据库。为评估这些差异表达基因的预后相关性,进行了多变量 Cox 回归分析和生存分析。单细胞测序被用于探索细胞间相互作用模式和识别相关信号通路。我们还进行了药物敏感性分析,以确定患者的个性化治疗策略:在这项研究中,我们对肝炎、肝硬化和肝细胞癌数据集进行了整合分析,发现了10个与预后相关的肝病进展基因。随着病情的发展,这些基因的表达出现了明显的下调,这表明它们在 HCC 的发展过程中起着至关重要的作用。通过多变量 Cox 分析,我们建立了一个肝病进展预后模型来预测 HCC 患者的预后。该模型经ROC分析验证,在预后评估中表现出良好的准确性和稳定性。单细胞测序分析表明,这些基因在 HCC 进展过程中主要通过 MIF 信号通路发挥作用。此外,我们还观察到低危组患者对TACE治疗的敏感性更高,而高危组患者对索拉非尼治疗的反应更好:总之,我们阐明了参与肝病进展的关键基因,并建立了评估 HCC 患者预后的精确预后模型。我们的研究为治疗 HCC 提供了新的见解和策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Annals of hepatology
Annals of hepatology 医学-胃肠肝病学
CiteScore
7.90
自引率
2.60%
发文量
183
审稿时长
4-8 weeks
期刊介绍: Annals of Hepatology publishes original research on the biology and diseases of the liver in both humans and experimental models. Contributions may be submitted as regular articles. The journal also publishes concise reviews of both basic and clinical topics.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信