Multiple Machine Learning Identifies Key Gene PHLDA1 Suppressing NAFLD Progression.

IF 4.5 2区 医学 Q2 CELL BIOLOGY Inflammation Pub Date : 2024-11-04 DOI:10.1007/s10753-024-02164-6
Zhenwei Yang, Zhiqin Chen, Jingchao Wang, Yizhang Li, Hailin Zhang, Yu Xiang, Yuwei Zhang, Zhaozhao Shao, Pei Wu, Ding Lu, Huajiang Lin, Zhaowei Tong, Jiang Liu, Quan Dong
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Abstract

Non-alcoholic fatty liver disease (NAFLD) poses a serious global health threat, with its progression mechanisms not yet fully understood. While several molecular markers for NAFLD have been developed in recent years, a lack of robust evidence hampers their clinical application. Therefore, identifying novel and potent biomarkers would directly aid in the prediction, prevention, and personalized treatment of NAFLD. We downloaded NAFLD-related datasets from the Gene Expression Omnibus (GEO). Differential expression analysis and functional analysis were initially conducted. Subsequently, Weighted Gene Co-expression Network Analysis (WGCNA) and multiple machine learning strategies were employed to screen and identify key genes, and the diagnostic value was assessed using Receiver Operating Characteristic (ROC) analysis. We then explored the relationship between genes and immune cells using transcriptome data and single-cell RNA sequencing (scRNA-seq) data. Finally, we validated our findings in cell and mouse NAFLD models. We obtained 23 overlapping differentially expressed genes (DEGs) across three NAFLD datasets. Enrichment analysis revealed that DEGs were associated with Apoptosis, Parathyroid hormone synthesis, secretion and action, Colorectal cancer, p53 signaling pathway, and Biosynthesis of unsaturated fatty acids. After employing machine learning strategies, we identified one gene, pleckstrin homology like domain family A member 1 (PHLDA1), downregulated in NAFLD and showing high diagnostic accuracy. CIBERSORT analysis revealed significant associations of PHLDA1 with various immune cells. Single-cell data analysis demonstrated downregulation of PHLDA1 in NAFLD, with PHLDA1 exhibiting a significant negative correlation with macrophages. Furthermore, we found PHLDA1 to be downregulated in an in vitro hepatic steatosis cell model, and overexpression of PHLDA1 significantly reduced lipid accumulation, as well as the expression of key molecules involved in hepatic lipogenesis and fatty acid uptake, such as FASN, SCD-1, and CD36. Additionally, gene set enrichment analysis (GSEA) pathway enrichment analysis suggested that PHLDA1 may influence NAFLD progression through pathways such as Cytokine Cytokine Receptor Interaction, Ecm Receptor Interaction, Parkinson's Disease, and Ribosome pathways. Our conclusions were further validated in a mouse model of NAFLD. Our study reveals that PHLDA1 inhibits the progression of NAFLD, as overexpression of PHLDA1 significantly reduces lipid accumulation in cells and markedly decreases the expression of key molecules involved in liver lipogenesis and fatty acid uptake. Therefore, PHLDA1 may emerge as a novel potential target for future prediction, diagnosis, and targeted prevention of NAFLD.

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多重机器学习发现抑制非酒精性脂肪肝进展的关键基因 PHLDA1
非酒精性脂肪肝(NAFLD)对全球健康构成严重威胁,其发展机制尚未完全明了。虽然近年来已开发出几种非酒精性脂肪肝的分子标记物,但由于缺乏有力的证据,阻碍了它们的临床应用。因此,鉴定新型、有效的生物标志物将直接有助于非酒精性脂肪肝的预测、预防和个性化治疗。我们从基因表达总库(GEO)中下载了非酒精性脂肪肝相关数据集。首先进行了差异表达分析和功能分析。随后,我们采用加权基因共表达网络分析(WGCNA)和多种机器学习策略来筛选和识别关键基因,并使用接收者操作特征(ROC)分析评估其诊断价值。然后,我们利用转录组数据和单细胞 RNA 测序(scRNA-seq)数据探讨了基因与免疫细胞之间的关系。最后,我们在细胞和小鼠非酒精性脂肪肝模型中验证了我们的发现。我们在三个非酒精性脂肪肝数据集中获得了 23 个重叠的差异表达基因(DEGs)。富集分析表明,DEGs 与细胞凋亡、甲状旁腺激素的合成、分泌和作用、结直肠癌、p53 信号通路和不饱和脂肪酸的生物合成有关。采用机器学习策略后,我们发现了一个基因,即pleckstrin homology like domain family A member 1 (PHLDA1),该基因在非酒精性脂肪肝中下调,并显示出较高的诊断准确性。CIBERSORT分析显示,PHLDA1与各种免疫细胞有显著关联。单细胞数据分析显示,PHLDA1在非酒精性脂肪肝中下调,PHLDA1与巨噬细胞呈显著负相关。此外,我们还发现 PHLDA1 在体外肝脂肪变性细胞模型中下调,过表达 PHLDA1 可显著减少脂质积累,以及参与肝脏脂肪生成和脂肪酸摄取的关键分子(如 FASN、SCD-1 和 CD36)的表达。此外,基因组富集分析(GSEA)通路富集分析表明,PHLDA1可能通过细胞因子受体相互作用、Ecm受体相互作用、帕金森病和核糖体通路等通路影响非酒精性脂肪肝的进展。我们的结论在非酒精性脂肪肝小鼠模型中得到了进一步验证。我们的研究揭示了 PHLDA1 可抑制非酒精性脂肪肝的进展,因为过表达 PHLDA1 可显著减少细胞中的脂质积累,并明显降低参与肝脏脂肪生成和脂肪酸摄取的关键分子的表达。因此,PHLDA1可能成为未来预测、诊断和有针对性地预防非酒精性脂肪肝的一个新的潜在靶点。
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来源期刊
Inflammation
Inflammation 医学-免疫学
CiteScore
9.70
自引率
0.00%
发文量
168
审稿时长
3.0 months
期刊介绍: Inflammation publishes the latest international advances in experimental and clinical research on the physiology, biochemistry, cell biology, and pharmacology of inflammation. Contributions include full-length scientific reports, short definitive articles, and papers from meetings and symposia proceedings. The journal''s coverage includes acute and chronic inflammation; mediators of inflammation; mechanisms of tissue injury and cytotoxicity; pharmacology of inflammation; and clinical studies of inflammation and its modification.
期刊最新文献
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