Wang Wei-Lu, Lian Haoran, Chen Yiling, Song Zhejun, Tam Paul Kwong Hang, Chen Yan
School of Pharmacy, Faculty of Medicine, Macau University of Science and Technology, Macau, China.
Precision Regenerative Medicine Research Centre, Medical Sciences Division, Macau University of Science and Technology, Macau, China.
Int J Mol Sci. 2025 Aug 28;26(17):8387. doi: 10.3390/ijms26178387.
Liver fibrosis (LF) poses significant challenges in diagnosis and treatment. This study aimed to identify effective biomarkers for diagnosis and therapy, as well as to gain deeper insights into the immunological features associated with LF. LF-related datasets were retrieved from the Gene Expression Omnibus (GEO) database. Two datasets were merged to generate a metadata cohort for bioinformatics analysis and machine learning, while another dataset was reserved for external validation. Seventy-eight machine learning algorithms were employed to screen signature genes. The diagnostic performance of these genes was evaluated using receiver operating characteristic (ROC) curves, and their expression levels were validated via qRT-PCR experiments. The R language was utilized to delineate the immune landscape. Finally, correlation analysis was conducted to investigate the relationship between the signature genes and immune infiltration. Through the intersection of GEO datasets and Weighted Gene Co-expression Network Analysis (WGCNA), 42 genes were identified. Machine learning methods further narrowed down 13 signature genes (alpha-2-macroglobulin (), ankyrin-3 (), complement component 7 (), cadherin 6 (), cysteine-rich motor neuron protein 1 (), dihydropyrimidinase-like 3 (), , gamma-aminobutyric acid (GABA) receptor subunit epsilon (), membrane metalloendopeptidase (), solute carrier family 38 member 1 (), tropomyosin alpha-1 chain (), von Willebrand factor (), and zinc finger protein 83 ()), and qRT-PCR confirmed these genes' expression patterns. Furthermore, these signature genes demonstrated strong correlations with multiple immune cell populations. In conclusion, the 13 genes (, , , , , , , , , , , , and ) represent robust potential biomarkers for the diagnosis and treatment of LF. Among these genes, we first identified as related to LF and expressed in hepatocytes and cholangiocytes. The immune response mediated by these signature biomarkers plays a pivotal role in the pathogenesis and progression of LF through dynamic interactions between the biomarkers and immune-infiltrating cells.
肝纤维化（LF）在诊断和治疗方面面临重大挑战。本研究旨在识别用于诊断和治疗的有效生物标志物，并更深入地了解与LF相关的免疫特征。从基因表达综合数据库（GEO）中检索与LF相关的数据集。将两个数据集合并以生成用于生物信息学分析和机器学习的元数据队列，而另一个数据集则保留用于外部验证。采用78种机器学习算法筛选特征基因。使用受试者工作特征（ROC）曲线评估这些基因的诊断性能，并通过qRT-PCR实验验证其表达水平。利用R语言描绘免疫景观。最后，进行相关性分析以研究特征基因与免疫浸润之间的关系。通过GEO数据集与加权基因共表达网络分析（WGCNA）的交叉分析，鉴定出42个基因。机器学习方法进一步将特征基因缩小至13个（α-2-巨球蛋白、锚蛋白-3、补体成分7、钙黏蛋白6、富含半胱氨酸的运动神经元蛋白1、二氢嘧啶酶样3、γ-氨基丁酸（GABA）受体亚基ε、膜金属内肽酶、溶质载体家族38成员1、原肌球蛋白α-1链、血管性血友病因子和锌指蛋白83），qRT-PCR证实了这些基因的表达模式。此外，这些特征基因与多种免疫细胞群体表现出强烈的相关性。总之，这13个基因（、、、、、、、、、、、和）代表了用于LF诊断和治疗的强大潜在生物标志物。在这些基因中，我们首次鉴定出与LF相关且在肝细胞和胆管细胞中表达。这些特征生物标志物介导的免疫反应通过生物标志物与免疫浸润细胞之间的动态相互作用，在LF的发病机制和进展中起关键作用。
