Background: Hepatocellular carcinoma (HCC) is a complex malignancy influenced by genetic, epigenetic and immune-related factors. The tumour immune microenvironment plays a critical role in HCC progression and response to immunotherapy. Identifying key immune-related gene signatures through clustering techniques can provide insights into tumour biology and therapeutic targets.

Methods: We employed K-means, Markov Clustering Algorithm (MCL) and density-based spatial clustering of applications with noise (DBSCAN) to analyse immune-related genes in HCC. Functional enrichment analysis was conducted using Gene Ontology (GO) biological process, cellular component and molecular function categories, along with pathway analysis from Kyoto encyclopedia of genes and genomes (KEGG) and Reactome databases. Additionally, protein–protein interaction (PPI) hub analysis and microRNAs (miRNA) target predictions were integrated to understand the regulatory networks.

Results: K-means clustering segregated immune genes into three clusters, with major histocompatibility complex (MHC) class II genes forming a distinct cluster. MCL and DBSCAN identified a more unified immune cluster incorporating both MHC class I and II molecules, suggesting their coordinated role in antigen presentation. GO analysis revealed enrichment in antigen processing and presentation pathways, immunoglobulin-mediated responses and glutamate receptor signaling. KEGG pathway analysis highlighted associations with autoimmune diseases and viral infections. PPI hub analysis identified CD4, CD74 and HLA-DQA1 as central nodes, while miRNA analysis suggested regulatory interactions affecting immune gene expression.

Conclusion: Our clustering analysis highlights distinct immune-related gene signatures in HCC, emphasising the role of antigen presentation and immune modulation in tumour progression. The findings provide a foundation for further investigation into immunotherapeutic strategies targeting key immune pathways in HCC.