Deng PengChao, Chaulagain Ram Prasad, Oluwaseun Babalola Deborah, Gao FeiYang, Wang JiaXin, Gao RanYan, Jiang XinYu, Li FengChun, Xu LingYi, Xu HaoXuan, Yao KaiXin, Jin Shizhu
Department of Gastroenterology and Hepatology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Sci Prog. 2025 Jul-Sep;108(3):368504251380638. doi: 10.1177/00368504251380638. Epub 2025 Sep 18.
BackgroundLiver cirrhosis represents a significant challenge to global public health. However, reliable biological markers for diagnosing liver cirrhosis are lacking in clinical practice.MethodsTranscriptome data from liver cirrhosis patients were acquired from the Gene Expression Omnibus database to identify coexpressed differentially expressed genes (DEGs). Mitochondria-related and ferroptosis-related genes were obtained from MitoCarta3.0 and FerrDB V2, respectively. Immune-related module genes were examined through Weighted Gene Co-Expression Network Analysis (WGCNA). By using WGCNA combined with machine learning methods, we identified immune-related biomarkers for liver cirrhosis. The immune cell infiltration was evaluated using CIBERSORTx, with core immune cell types further refined through LASSO regression and random forest. Hub biomarkers were validated using single-cell sequencing, with additional confirmation provided by histological staining and immunohistochemistry (IHC).ResultsThis study identified 2474 DEGs between liver cirrhosis and control groups. Intersection analysis with ferroptosis-related genes and mitochondria-related genes narrowed to 13 hub genes, from which machine learning selected 8 biomarkers. CIBERSORT and Wilcoxon tests revealed notable variations in the 12 immune cell types across the different groups. The WGCNA identified immune-related genes, with four immune-related biomarkers (, , , and ) identified as hub biomarkers. Integrated LASSO regression, random forest, and immune infiltration analyses pinpointed the core cells influencing disease progression. The relationship between the hub biomarkers and immune cells was validated by single-cell data analysis. expression was verified through IHC, consistent with our bioinformatics findings. Molecular docking identified three small molecules with potential effectiveness.ConclusionOur study identified mitochondrial ferroptosis-related genes ( and ) as pivotal biomarkers in liver cirrhosis progression and demonstrated a close connection with the immune microenvironment. These genes may serve as diagnostic indicators and therapeutic targets, thereby providing novel perspectives on the pathogenesis of liver cirrhosis.
背景
肝硬化对全球公共卫生构成重大挑战。然而,临床实践中缺乏用于诊断肝硬化的可靠生物标志物。
方法
从基因表达综合数据库获取肝硬化患者的转录组数据,以识别共表达的差异表达基因(DEG)。线粒体相关基因和铁死亡相关基因分别从MitoCarta3.0和FerrDB V2中获取。通过加权基因共表达网络分析(WGCNA)检测免疫相关模块基因。通过将WGCNA与机器学习方法相结合,我们确定了肝硬化的免疫相关生物标志物。使用CIBERSORTx评估免疫细胞浸润,并通过LASSO回归和随机森林进一步细化核心免疫细胞类型。使用单细胞测序验证枢纽生物标志物,并通过组织学染色和免疫组织化学(IHC)提供额外的确认。
结果
本研究确定了肝硬化组和对照组之间的2474个DEG。与铁死亡相关基因和线粒体相关基因的交集分析缩小到13个枢纽基因,机器学习从中选择了8个生物标志物。CIBERSORT和Wilcoxon检验显示不同组间12种免疫细胞类型存在显著差异。WGCNA确定了免疫相关基因,其中4个免疫相关生物标志物(、、和)被确定为枢纽生物标志物。综合LASSO回归、随机森林和免疫浸润分析确定了影响疾病进展的核心细胞。单细胞数据分析验证了枢纽生物标志物与免疫细胞之间的关系。通过IHC验证了的表达,与我们的生物信息学结果一致。分子对接确定了三种具有潜在有效性的小分子。
结论
我们的研究确定线粒体铁死亡相关基因(和)是肝硬化进展中的关键生物标志物,并证明其与免疫微环境密切相关。这些基因可能作为诊断指标和治疗靶点,从而为肝硬化的发病机制提供新的视角。