通过综合生物信息学分析鉴定和验证肝硬化线粒体铁下沉和免疫微环境相关中枢生物标志物。

IF 2.9 4区 综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES
Science Progress Pub Date : 2025-07-01 Epub Date: 2025-09-18 DOI:10.1177/00368504251380638
PengChao Deng, Ram Prasad Chaulagain, Babalola Deborah Oluwaseun, FeiYang Gao, JiaXin Wang, RanYan Gao, XinYu Jiang, FengChun Li, LingYi Xu, HaoXuan Xu, KaiXin Yao, Shizhu Jin
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引用次数: 0

摘要

背景肝硬化是对全球公共卫生的重大挑战。然而,临床上缺乏可靠的诊断肝硬化的生物标志物。方法从Gene Expression Omnibus数据库获取肝硬化患者的转录组数据,鉴定共表达差异表达基因(DEGs)。分别从MitoCarta3.0和FerrDB V2中获得线粒体相关基因和铁凋亡相关基因。通过加权基因共表达网络分析(WGCNA)检测免疫相关模块基因。通过使用WGCNA结合机器学习方法,我们确定了肝硬化的免疫相关生物标志物。使用CIBERSORTx评估免疫细胞浸润情况,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Hub生物标志物,并通过组织学染色和免疫组化(IHC)提供额外的确认。结果本研究在肝硬化组和对照组之间鉴定出2474个deg。与凋亡相关基因和线粒体相关基因的交叉分析缩小到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测试显示,不同组的12种免疫细胞类型存在显著差异。WGCNA鉴定了免疫相关基因,并鉴定了四种免疫相关生物标志物(DHODH、FXN、CS和ISCU)作为枢纽生物标志物。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。通过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。通过免疫组化验证了ISCU的表达,与我们的生物信息学发现一致。分子对接确定了三个具有潜在效力的小分子。结论我们的研究发现线粒体凋亡相关基因(DHODH、FXN、CS和ISCU)是肝硬化进展的关键生物标志物,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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification and validation of mitochondrial ferroptosis and immune microenvironment-related hub biomarkers in liver cirrhosis by integrated bioinformatics analysis.

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 (DHODH, FXN, CS, and ISCU) 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. ISCU 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 (DHODH, FXN, CS, and ISCU) 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.

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来源期刊
Science Progress
Science Progress Multidisciplinary-Multidisciplinary
CiteScore
3.80
自引率
0.00%
发文量
119
期刊介绍: Science Progress has for over 100 years been a highly regarded review publication in science, technology and medicine. Its objective is to excite the readers'' interest in areas with which they may not be fully familiar but which could facilitate their interest, or even activity, in a cognate field.
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