CDT1 is a Potential Therapeutic Target for the Progression of NAFLD to HCC and the Exacerbation of Cancer

被引:0
|
作者
He, Xingyu [1 ,2 ]
Ma, Jun [1 ,2 ]
Yan, Xue [1 ,2 ]
Yang, Xiangyu [3 ]
Wang, Ping [3 ]
Zhang, Lijie [1 ,2 ]
Li, Na [1 ,2 ]
Shi, Zheng [1 ,2 ]
机构
[1] Chengdu Univ, Clin Med Coll, Chengdu 610083, Peoples R China
[2] Chengdu Univ, Affiliated Hosp, Chengdu 610083, Peoples R China
[3] Sichuan Univ, West China Hosp, Chengdu 610083, Peoples R China
基金
中国国家自然科学基金;
关键词
Nonalcoholic fatty liver disease; hepatocellular carcinoma; machine learning; CDT1; single-cell sequencing; early diagnosis; FATTY LIVER;
D O I
10.2174/0113892029313473240919105819
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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 utilizes 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.Objective 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.
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页数:19
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