BACKGROUND: Non-alcoholic steatohepatitis (NASH), a prominent driver of hepatocellular carcinoma (HCC) besides virus and alcohol, induces a series of complex liver structural and immune microenvironment changes, which make the early diagnosis and treatment of NASH-associated HCC (NASH-HCC) more challenging. This study aims to identify signature genes and explore the role of immune cell infiltration in NASH-HCC to improve early detection and prognosis assessment. METHODS: Differential gene and immune cell infiltration are important indicators for predicting the progress of oncology and responsiveness of tumor patients to immunotherapy, usually confirmed through biopsy tests with poor patient compliance. To obtain a highly correlated signature gene set and validate immune cell infiltration status, the GSE164760 and GSE102079 datasets from the Gene Expression Omnibus (GEO) database were analyzed using machine learning algorithms. Feature genes were identified based on differentially expressed genes and key modular genes identified by weighted gene co-expression network analysis (WGCNA). The signature genes were screened using the least absolute shrinkage and selection operator (LASSO), random forest, and support vector machine recursive feature elimination (SVM-RFE) machine learning algorithms. Subsequently, the signature genes were subjected to diagnostic efficacy tests, gene set enrichment analysis, immune cell infiltration assessment and real-time reverse transcription polymerase chain reaction (RT-qPCR) validation. RESULTS: Six signature genes were identified, including C-C motif chemokine ligand 14 (