Bioinformatics Profiling of a Paeoniflorin-Associated “Macrophage–Lactylation” Axis in Hepatocellular Carcinoma: An LDHA-Based Prognostic Model and Structural Computational Evidence

Hepatocellular carcinoma (HCC) progression is shaped by crosstalk between the tumor immune microenvironment (TME) and metabolic reprogramming. This study aims to characterize a macrophage–lactylation molecular axis in HCC and to develop a quantitative prognostic stratification model. Using the TCGA-LIHC cohort, differentially expressed genes were intersected with Paeoniflorin (PF)-related targets, HCC disease targets, and macrophage-/lactylation-related genes to identify candidate genes. Prognostic genes were selected through Cox and LASSO-Cox analyses to construct a risk score model, followed by survival analysis and ROC curve evaluation. Immune infiltration was assessed using ESTIMATE and ssGSEA algorithms, and PF–protein binding interactions were explored via molecular docking and molecular dynamics simulations. Intersection analysis identified eight key genes, and prognostic model genes (HNRNPU, LDHA, and NPM1) were used to construct the prognostic model. High-risk patients exhibited significantly poorer overall survival (p < 0.001), with 1- and 3-year AUC values ranging from 0.70 to 0.90. HNRNPU was positively correlated with activated CD4 T cells (r = 0.385) and negatively correlated with eosinophils (r = −0.498). Molecular docking indicated favorable binding of PF to the model proteins, with the highest predicted affinity observed for LDHA (Vina score = −8.9 kcal/mol), and molecular dynamics simulations suggested the formation of a stable LDHA–PF complex during the later stage of the simulation. We propose a prognostic risk model for HCC constructed using three prognostic model genes and provide computational evidence linking PF to key molecular nodes such as LDHA. External cohort validation and experimental studies are warranted.