Lactylation is closely involved in cancer progression, but its role in hepatocellular carcinoma (HCC) is unclear. The present work set out to develop a lactylation-related gene (LRG) signature for HCC. The lactylation score of tumor and normal groups was calculated using the gene set variation analysis (GSVA) package. The single-cell RNA sequencing (scRNA-seq) analysis of HCC was performed in the "Seurat" package. Prognostic LRGs were selected by performing univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses to develop and validate a Riskscore model. Functional enrichment analysis was conducted by gene set enrichment analysis (GSEA) using the "clusterProfiler" package. Genomic characteristics between different risk groups were compared, and tumor mutational burden (TMB) was calculated by the "Maftools" package. Immune cell infiltration was assessed by algorithms of cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT), microenvironment cell populations-counter (MCP-counter), estimating the proportions of immune and cancer cells (EPIC), tumor immune estimation resource (TIMER), and single-sample gene set enrichment analysis (ssGSEA). Immunotherapy response was predicted by the tumor immune dysfunction and exclusion (TIDE) algorithm. Drug sensitivity was analyzed using the "pRRophetic" package. A nomogram was established using the "rms" package. The expressions of the prognostic LRGs in HCC cells were verified by in vitro test, and cell counting kit-8 (CCK-8), wound healing, and transwell assays were carried out to measure the viability, migration, and invasion of HCC cells. The lactylation score, which was higher in the tumor group than in the normal group, has been confirmed as an independent factor for the prognostic evaluation in HCC. Six prognostic LRGs, including two protective genes ( and ) and four risk genes (, , , and ), were identified to develop a Riskscore model with a strong prognostic prediction performance in HCC. The scRNA-seq analysis revealed that was largely expressed in myeloid cells, while , , , , and were mainly expressed in hepatocytes. The high-risk group was primarily enriched in the pathways involved in tumor occurrence and development, with higher T cell infiltration. Moreover, the high-risk group was found to be less responsive to immunotherapy but was more sensitive to chemotherapeutic drugs. By integrating Riskscore and clinical features, a nomogram with a high predictive accuracy was developed. Additionally, , , , and were highly expressed in HCC cells. Silencing inhibited the viability, migration, and invasion of HCC cells. The present work developed a novel LRG gene signature that could be considered a promising therapeutic target and biomarker for HCC.
