CDT1是NAFLD进展为HCC和癌症恶化的潜在治疗靶点。

IF 1.4 4区 生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Current Genomics Pub Date : 2025-01-01 Epub Date: 2024-10-04 DOI:10.2174/0113892029313473240919105819
Xingyu He, Jun Ma, Xue Yan, Xiangyu Yang, Ping Wang, Lijie Zhang, Na Li, Zheng Shi
{"title":"CDT1是NAFLD进展为HCC和癌症恶化的潜在治疗靶点。","authors":"Xingyu He, Jun Ma, Xue Yan, Xiangyu Yang, Ping Wang, Lijie Zhang, Na Li, Zheng Shi","doi":"10.2174/0113892029313473240919105819","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>This study aimed to identify potential therapeutic targets in the progression from non-alcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC), with a focus on genes that could influence disease development and progression.</p><p><strong>Background: </strong>Hepatocellular carcinoma, significantly driven by non-alcoholic fatty liver disease, represents a major global health challenge due to late-stage diagnosis and limited treatment options. This study utilized bioinformatics to analyze data from GEO and TCGA, aiming to uncover molecular biomarkers that bridge NAFLD to HCC. Through identifying critical genes and pathways, our research seeks to advance early detection and develop targeted therapies, potentially improving prognosis and personalizing treatment for NAFLD-HCC patients.</p><p><strong>Objectives: </strong>Identify key genes that differ between NAFLD and HCC; Analyze these genes to understand their roles in disease progression; Validate the functions of these genes in NAFLD to HCC transition.</p><p><strong>Methods: </strong>Initially, we identified a set of genes differentially expressed in both NAFLD and HCC using second-generation sequencing data from the GEO and TCGA databases. We then employed a Cox proportional hazards model and a Lasso regression model, applying machine learning techniques to the large sample data from TCGA. This approach was used to screen for key disease-related genes, and an external dataset was utilized for model validation. Additionally, pseudo-temporal sequencing analysis of single-cell sequencing data was performed to further examine the variations in these genes in NAFLD and HCC.</p><p><strong>Results: </strong>The machine learning analysis identified IGSF3, CENPW, CDT1, and CDC6 as key genes. Furthermore, constructing a machine learning model for CDT1 revealed it to be the most critical gene, with model validation yielding an ROC value greater than 0.80. The single-cell sequencing data analysis confirmed significant variations in the four predicted key genes between the NAFLD and HCC groups.</p><p><strong>Conclusion: </strong>Our study underscores the pivotal role of CDT1 in the progression from NAFLD to HCC. This finding opens new avenues for early diagnosis and targeted therapy of HCC, highlighting CDT1 as a potential therapeutic target.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"26 3","pages":"225-243"},"PeriodicalIF":1.4000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12107793/pdf/","citationCount":"0","resultStr":"{\"title\":\"CDT1 is a Potential Therapeutic Target for the Progression of NAFLD to HCC and the Exacerbation of Cancer.\",\"authors\":\"Xingyu He, Jun Ma, Xue Yan, Xiangyu Yang, Ping Wang, Lijie Zhang, Na Li, Zheng Shi\",\"doi\":\"10.2174/0113892029313473240919105819\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aims: </strong>This study aimed to identify potential therapeutic targets in the progression from non-alcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC), with a focus on genes that could influence disease development and progression.</p><p><strong>Background: </strong>Hepatocellular carcinoma, significantly driven by non-alcoholic fatty liver disease, represents a major global health challenge due to late-stage diagnosis and limited treatment options. This study utilized bioinformatics to analyze data from GEO and TCGA, aiming to uncover molecular biomarkers that bridge NAFLD to HCC. Through identifying critical genes and pathways, our research seeks to advance early detection and develop targeted therapies, potentially improving prognosis and personalizing treatment for NAFLD-HCC patients.</p><p><strong>Objectives: </strong>Identify key genes that differ between NAFLD and HCC; Analyze these genes to understand their roles in disease progression; Validate the functions of these genes in NAFLD to HCC transition.</p><p><strong>Methods: </strong>Initially, we identified a set of genes differentially expressed in both NAFLD and HCC using second-generation sequencing data from the GEO and TCGA databases. We then employed a Cox proportional hazards model and a Lasso regression model, applying machine learning techniques to the large sample data from TCGA. This approach was used to screen for key disease-related genes, and an external dataset was utilized for model validation. Additionally, pseudo-temporal sequencing analysis of single-cell sequencing data was performed to further examine the variations in these genes in NAFLD and HCC.</p><p><strong>Results: </strong>The machine learning analysis identified IGSF3, CENPW, CDT1, and CDC6 as key genes. Furthermore, constructing a machine learning model for CDT1 revealed it to be the most critical gene, with model validation yielding an ROC value greater than 0.80. The single-cell sequencing data analysis confirmed significant variations in the four predicted key genes between the NAFLD and HCC groups.</p><p><strong>Conclusion: </strong>Our study underscores the pivotal role of CDT1 in the progression from NAFLD to HCC. This finding opens new avenues for early diagnosis and targeted therapy of HCC, highlighting CDT1 as a potential therapeutic target.</p>\",\"PeriodicalId\":10803,\"journal\":{\"name\":\"Current Genomics\",\"volume\":\"26 3\",\"pages\":\"225-243\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12107793/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Genomics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.2174/0113892029313473240919105819\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/4 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Genomics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.2174/0113892029313473240919105819","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/4 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

目的:本研究旨在确定从非酒精性脂肪性肝病(NAFLD)到肝细胞癌(HCC)进展的潜在治疗靶点,重点关注可能影响疾病发生和进展的基因。背景:肝细胞癌主要由非酒精性脂肪性肝病驱动,由于晚期诊断和有限的治疗选择,它代表了一个主要的全球健康挑战。本研究利用生物信息学分析GEO和TCGA的数据,旨在揭示NAFLD与HCC之间的分子生物标志物。通过识别关键基因和途径,我们的研究旨在推进早期发现和开发靶向治疗,潜在地改善NAFLD-HCC患者的预后和个性化治疗。目的:确定NAFLD和HCC之间的关键基因差异;分析这些基因以了解它们在疾病进展中的作用;验证这些基因在NAFLD向HCC转变中的功能。方法:首先,我们利用GEO和TCGA数据库的第二代测序数据确定了一组在NAFLD和HCC中差异表达的基因。然后,我们采用Cox比例风险模型和Lasso回归模型,将机器学习技术应用于TCGA的大样本数据。该方法用于筛选关键疾病相关基因,并利用外部数据集进行模型验证。此外,对单细胞测序数据进行伪时间测序分析,以进一步检查这些基因在NAFLD和HCC中的变化。结果:机器学习分析鉴定出IGSF3、CENPW、CDT1和CDC6为关键基因。此外,构建了CDT1的机器学习模型,发现它是最关键的基因,模型验证的ROC值大于0.80。单细胞测序数据分析证实了NAFLD和HCC组之间四个预测关键基因的显著差异。结论:我们的研究强调了CDT1在NAFLD向HCC发展过程中的关键作用。这一发现为HCC的早期诊断和靶向治疗开辟了新的途径,突出了CDT1作为潜在的治疗靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CDT1 is a Potential Therapeutic Target for the Progression of NAFLD to HCC and the Exacerbation of Cancer.

Aims: This study aimed to identify potential therapeutic targets in the progression from non-alcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC), with a focus on genes that could influence disease development and progression.

Background: Hepatocellular carcinoma, significantly driven by non-alcoholic fatty liver disease, represents a major global health challenge due to late-stage diagnosis and limited treatment options. This study utilized bioinformatics to analyze data from GEO and TCGA, aiming to uncover molecular biomarkers that bridge NAFLD to HCC. Through identifying critical genes and pathways, our research seeks to advance early detection and develop targeted therapies, potentially improving prognosis and personalizing treatment for NAFLD-HCC patients.

Objectives: Identify key genes that differ between NAFLD and HCC; Analyze these genes to understand their roles in disease progression; Validate the functions of these genes in NAFLD to HCC transition.

Methods: Initially, we identified a set of genes differentially expressed in both NAFLD and HCC using second-generation sequencing data from the GEO and TCGA databases. We then employed a Cox proportional hazards model and a Lasso regression model, applying machine learning techniques to the large sample data from TCGA. This approach was used to screen for key disease-related genes, and an external dataset was utilized for model validation. Additionally, pseudo-temporal sequencing analysis of single-cell sequencing data was performed to further examine the variations in these genes in NAFLD and HCC.

Results: The machine learning analysis identified IGSF3, CENPW, CDT1, and CDC6 as key genes. Furthermore, constructing a machine learning model for CDT1 revealed it to be the most critical gene, with model validation yielding an ROC value greater than 0.80. The single-cell sequencing data analysis confirmed significant variations in the four predicted key genes between the NAFLD and HCC groups.

Conclusion: Our study underscores the pivotal role of CDT1 in the progression from NAFLD to HCC. This finding opens new avenues for early diagnosis and targeted therapy of HCC, highlighting CDT1 as a potential therapeutic target.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Current Genomics
Current Genomics 生物-生化与分子生物学
CiteScore
5.20
自引率
0.00%
发文量
29
审稿时长
>0 weeks
期刊介绍: Current Genomics is a peer-reviewed journal that provides essential reading about the latest and most important developments in genome science and related fields of research. Systems biology, systems modeling, machine learning, network inference, bioinformatics, computational biology, epigenetics, single cell genomics, extracellular vesicles, quantitative biology, and synthetic biology for the study of evolution, development, maintenance, aging and that of human health, human diseases, clinical genomics and precision medicine are topics of particular interest. The journal covers plant genomics. The journal will not consider articles dealing with breeding and livestock. Current Genomics publishes three types of articles including: i) Research papers from internationally-recognized experts reporting on new and original data generated at the genome scale level. Position papers dealing with new or challenging methodological approaches, whether experimental or mathematical, are greatly welcome in this section. ii) Authoritative and comprehensive full-length or mini reviews from widely recognized experts, covering the latest developments in genome science and related fields of research such as systems biology, statistics and machine learning, quantitative biology, and precision medicine. Proposals for mini-hot topics (2-3 review papers) and full hot topics (6-8 review papers) guest edited by internationally-recognized experts are welcome in this section. Hot topic proposals should not contain original data and they should contain articles originating from at least 2 different countries. iii) Opinion papers from internationally recognized experts addressing contemporary questions and issues in the field of genome science and systems biology and basic and clinical research practices.
×
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:604180095
Book学术官方微信