Zhao Yingying, Xu Xueqing, Cai Huaiyang, Wu Wenhong, Wang Yingwei, Huang Cheng, Qin Heping, Mo Shuangyang
Shandong University of Traditional Chinese Medicine, Jinan, China.
Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China.
Front Genet. 2024 Dec 4;15:1499915. doi: 10.3389/fgene.2024.1499915. eCollection 2024.
The etiopathogenesis of hepatic stellate cells (HSC) activation has yet to be completely comprehended, and there has been broad concern about the interplay between amino acid transporter and cell proliferation. This study proposed exploring the molecular mechanism from amino acid transport-related genes in HSC activation by bioinformatic methods, seeking to identify the potentially crucial biomarkers.
GSE68000, the mRNA expression profile dataset of activated HSC, was applied as the training dataset, and GSE67664 as the validation dataset. Differently expressed amino acid transport-related genes (DEAATGs), GO, DO, and KEGG analyses were utilized. We applied the protein-protein interaction analysis and machine learning of LASSO and random forests to identify the target genes. Moreover, single-gene GESA was executed to investigate the potential functions of target genes via the KEGG pathway terms. Then, a ceRNA network and a drug-gene interaction network were constructed. Ultimately, correlation analysis was explored between target genes and collagen alpha I (COL1A), alpha-smooth muscle actin (α-SMA), and immune checkpoints.
We identified 15 DEAATGs, whose enrichment analyses indicated that they were primarily enriched in the transport and metabolic process of amino acids. Moreover, two target genes (SLC7A5 and SLC1A5) were recognized from the PPI network and machine learning, confirmed through the validation dataset. Then single-gene GESA analysis revealed that SLC7A5 and SLC1A5 had a significant positive correlation to ECM-receptor interaction, cell cycle, and TGF-β signaling pathway and negative association with retinol metabolism conversely. Furthermore, the mRNA expression of target genes was closely correlated with the COL1A and α-SMA, as well as immune checkpoints. Additionally, 12 potential therapeutic drugs were in the drug-gene interaction network, and the ceRNA network was constructed and visualized.
SLC7A5 and SLC1A5, with their relevant molecules, could be potentially vital biomarkers for the activation of HSC.
肝星状细胞（HSC）激活的发病机制尚未完全阐明，氨基酸转运体与细胞增殖之间的相互作用受到广泛关注。本研究旨在通过生物信息学方法探索HSC激活过程中氨基酸转运相关基因的分子机制，寻找潜在的关键生物标志物。
将激活的HSC的mRNA表达谱数据集GSE68000作为训练数据集，GSE67664作为验证数据集。利用差异表达的氨基酸转运相关基因（DEAATGs）、GO、DO和KEGG分析。应用蛋白质-蛋白质相互作用分析以及LASSO和随机森林机器学习来识别靶基因。此外，进行单基因GESA以通过KEGG通路术语研究靶基因的潜在功能。然后，构建ceRNA网络和药物-基因相互作用网络。最后，探索靶基因与I型胶原（COL1A）、α-平滑肌肌动蛋白（α-SMA）和免疫检查点之间的相关性分析。
我们鉴定出15个DEAATGs，其富集分析表明它们主要富集于氨基酸的转运和代谢过程。此外，从蛋白质-蛋白质相互作用网络和机器学习中识别出两个靶基因（SLC7A5和SLC1A5），并通过验证数据集进行了确认。然后单基因GESA分析显示，SLC7A5和SLC1A5与细胞外基质-受体相互作用、细胞周期和TGF-β信号通路呈显著正相关，相反与视黄醇代谢呈负相关。此外，靶基因的mRNA表达与COL1A和α-SMA以及免疫检查点密切相关。此外，药物-基因相互作用网络中有12种潜在治疗药物，并构建并可视化了ceRNA网络。
SLC7A5和SLC1A5及其相关分子可能是HSC激活的重要生物标志物。
