Multiple Machine Learning Identifies Key Gene PHLDA1 Suppressing NAFLD Progression

作者：Zhenwei Yang, Zhiqin Chen, Jingchao Wang, Yizhang Li, Hailin Zhang, Yu Xiang, Yuwei Zhang, Zhaozhao Shao, Pei Wu, Lu Ding, Huajiang Lin, Zhaowei Tong, Jiang Liu, Quanfeng Dong · 发表于：Inflammation · 年份：2024 · DOI：10.1007/s10753-024-02164-6 · 被引用次数：8 · 研究领域：Liver Disease Diagnosis and Treatment、Lipid metabolism and disorders、RNA modifications and cancer

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 showin...