Integration of Single-Cell and Bulk RNA Sequencing Data to Identify Lactylation-Related Gene Signatures in Hepatic Ischemia–Reperfusion Injury Using Machine Learning Algorithms

Abstract

Hepatic ischemia–reperfusion injury (HIRI) is not only a common complication of liver transplantation and major hepatic surgery but also a critical determinant of postoperative prognosis. Lactate metabolic reprogramming has been observed in HIRI, yet the role of lactate and its related lactylation in the pathogenesis of HIRI remains unclear. To address this, we integrated single-cell and bulk RNA-seq data with multiple bioinformatic approaches. Five single-cell gene set activity scoring methods (AUCell, UCell, singscore, ssGSEA, and AddModuleScore) were applied to evaluate lactylation activity across cell types, followed by differentially expressed gene (DEG) analysis and high-dimensional Weighted Correlation Network Analysis (hdWGCNA) to identify lactylation-associated genes. Five machine learning algorithms (Random Forest, Boruta, LASSO, GBM, and Decision Tree) were used to screen optimal feature genes, with SHAP analysis further explaining their importance. Bulk RNA sequencing data from the Gene Expression Omnibus (GEO) database were used for validation. Furthermore, NR4A3-related inhibitors were screened using the ChEMBL online tool and assessed by docking and molecular dynamic simulation. We observed significant heterogeneity in lactate metabolism activity across cell types in hepatic ischemia–reperfusion injury (HIRI), with higher activity levels observed for hepatocytes and mononuclear phagocytes. The integration of SHAP and machine learning identified PFKFB3, ZYX, and NR4A3 as closely associated with high lactylation after HIRI, and cross-analysis with bulk RNA data confirmed their consistent upregulation. Candidate gene expression was experimentally validated in a murine liver IRI model through Western blotting and RT-qPCR. Although lactylation has been previously reported in HIRI, this study’s unique contribution is to reveal the cell-type heterogeneity of lactylation-related gene expression at the single-cell level through multi-omics integration and machine learning. The identification of NR4A3, PFKFB3, and ZYX as lactylation-associated regulators proposes novel therapeutic targets for improving graft survival in liver transplantation.