Integrative analysis identifies oxidative stress biomarkers in non-alcoholic fatty liver disease via machine learning and weighted gene co-expression network analysis - Wang, Haining; Cheng, Wei; Hu, Ping; Ling, Tao; Hu, Chao; Chen, Yongzhen; Zheng, Yanan; Wang, Junqi; Zhao, Ting; You, Qiang (Frontiers in Immunology, 2024)
Background: Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease globally, with the potential to progress to non-alcoholic steatohepatitis (NASH), cirrhosis, and even hepatocellular carcinoma. Given the absence of effective treatments to halt its progression, novel molecular approaches to the NAFLD diagnosis and treatment are of paramount importance.Methods: Firstly, we downloaded oxidative stress-related genes from the GeneCards database and retrieved NAFLD-related datasets from the GEO database. Using the Limma R package and WGCNA, we identified differentially expressed genes closely associated with NAFLD.In our study, we identified 31 intersection genes by analyzing the intersection among oxidative stress-related genes, NAFLD-related genes, and genes closely associated with NAFLD as identified through Weighted Gene Co-expression Network Analysis (WGCNA). In a study of 31 intersection genes between NAFLD and Oxidative Stress (OS), we identified three hub genes using three machine learning algorithms:Least Absolute Shrinkage and Selection Operator (LASSO) regression,Support Vector 批注 [海王1]: Line 13. Replace Non-alcoholic fatty liver disease with Non-alcoholic fatty liver disease (NAFLD).