He Xingyu, Ma Jun, Yan Xue, Yang Xiangyu, Wang Ping, Zhang Lijie, Li Na, Shi Zheng
Clinical Medical College & Affiliated Hospital of Chengdu University, Chengdu University, 610083, P.R. China.
West China Hospital, Sichuan University, 610083, P.R. China.
Curr Genomics. 2025;26(3):225-243. doi: 10.2174/0113892029313473240919105819. Epub 2024 Oct 4.
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.
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.
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.
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.
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.
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.
本研究旨在确定非酒精性脂肪性肝病(NAFLD)向肝细胞癌(HCC)进展过程中的潜在治疗靶点,重点关注可能影响疾病发生发展的基因。
肝细胞癌受非酒精性脂肪性肝病的显著驱动,由于诊断较晚且治疗选择有限,是一项重大的全球健康挑战。本研究利用生物信息学分析来自基因表达综合数据库(GEO)和癌症基因组图谱(TCGA)的数据,旨在揭示连接非酒精性脂肪性肝病与肝细胞癌的分子生物标志物。通过识别关键基因和通路,我们的研究旨在推进早期检测并开发靶向治疗方法,有可能改善非酒精性脂肪性肝病 - 肝细胞癌患者的预后并实现个性化治疗。
确定非酒精性脂肪性肝病和肝细胞癌之间存在差异的关键基因;分析这些基因以了解它们在疾病进展中的作用;验证这些基因在非酒精性脂肪性肝病向肝细胞癌转变中的功能。
首先,我们使用来自GEO和TCGA数据库的二代测序数据,确定了一组在非酒精性脂肪性肝病和肝细胞癌中均差异表达的基因。然后,我们采用Cox比例风险模型和套索回归模型,将机器学习技术应用于来自TCGA的大样本数据。该方法用于筛选关键疾病相关基因,并使用外部数据集进行模型验证。此外,对单细胞测序数据进行了伪时间测序分析,以进一步研究这些基因在非酒精性脂肪性肝病和肝细胞癌中的变化。
机器学习分析确定免疫球蛋白超家族3(IGSF3)、着丝粒蛋白W(CENPW)、微小染色体维持缺陷蛋白(CDT1)和细胞分裂周期蛋白6(CDC6)为关键基因。此外,构建的CDT1机器学习模型显示它是最关键的基因,模型验证的受试者工作特征曲线(ROC)值大于0.80。单细胞测序数据分析证实非酒精性脂肪性肝病组和肝细胞癌组之间这四个预测的关键基因存在显著差异。
我们的研究强调了CDT1在非酒精性脂肪性肝病向肝细胞癌进展中的关键作用。这一发现为肝细胞癌的早期诊断和靶向治疗开辟了新途径,突出了CDT1作为潜在治疗靶点的地位。