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Updated: May 12, 2025
Cell Type-specific Gene Expression Profiling in the Mouse Liver
Published on: September 17, 2019
Multiple machine learning algorithms identify 13 types of cell death-critical genes in large and multiple
Renao Jiang1, Longfei Dai1, Xinjian Xu1
1Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Hefei, Anhui Province, 230022, China.
Background:
Dysregulated programmed cell death pathways mechanistically contribute to hepatic inflammation and fibrogenesis in non-alcoholic steatohepatitis (NASH). Identification of cell death genes may offer insights into diagnostic and therapeutic strategies for NASH.
Methods:
Data from multiple NASH cohorts were integrated, and 12 machine learning algorithms were applied to identify key dysregulated cell death-related genes and develop a binary classification model for NASH. Spearman's rank correlation coefficients quantified associations between these genes and clinical markers, immune infiltration profiles, and signature genes encoding pro-inflammatory mediators, metabolic regulators, and fibrotic drivers. Gene set enrichment analysis (GSEA) was performed to delineate the mechanistic underpinnings of these key genes. Consensus clustering analysis was then used to stratify patients with NASH into distinct phenotypic subgroups based on expression levels of these genes.
Results:
A NASH prediction model, developed using the random forest (RF) algorithm, demonstrated high diagnostic accuracy across multiple cohorts. Four key genes, enriched in lipid metabolism and inflammation pathways, were identified. Their transcriptional levels were significantly correlated with the non-alcoholic fatty liver disease activity score (NAS), hepatic inflammatory infiltration, molecular signatures of metabolic dysregulation (lipid homeostasis regulators), and fibrosis progression. These genes also enabled accurate classification of patients with NASH into clusters reflecting varying disease severity.
Conclusions:
A binary classification model, developed using the RF algorithm, accurately identified patients with NASH. The four cell death genes, identified through 12 machine learning algorithms, represent potential biomarkers and therapeutic targets for NASH. These genes contribute to inflammation-related immune cell activation, lipid metabolism dysregulation, and liver fibrosis, highlighting the complex interplay between cell death and NASH progression.
