Integrated transcriptomic and metabolomic profiling reveals immune–metabolic crosstalk and predictive biomarkers of ligustrazine treatment response in idiopathic pulmonary fibrosis

Abstract Background Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive interstitial lung disease characterized by aberrant immune responses and tissue remodeling. Ligustrazine has multiple effects, such as enhancing immunity and alleviating cellular hypoxia, and can improve pulmonary fibrosis. However, the underlying immune–metabolic interactions and predictive molecular biomarkers remain poorly defined. Understanding the mechanisms underlying therapeutic response is critical for developing precision medicine approaches. Methods We performed an integrative analysis using data from a rat model and humans, including bulk and single-cell transcriptomics (GSE150910, GSE32537, GSE122960) and untargeted metabolomics, to identify differentially expressed genes and metabolites associated with IPF and therapeutic intervention with ligustrazine. Key genes (IPFGs) and metabolites (IPFMs) were identified on the basis of their expression and correlation profiles. Consensus clustering was applied to stratify IPF molecular subtypes, and biological function differences between different clusters were further explored through ssGSEA. GSEA, miRNA and transcription factor (TF) network prediction, and single-cell analysis were performed to characterize the functional roles and regulatory mechanisms of key IPFGs. Diagnostic nomograms were constructed based on key IPFGs and related immune cell subsets and were evaluated by receiver operating characteristic (ROC) curve analysis. Results Five key IPFGs (CCL2, UBD, TIGIT, SYDE2, and COL4A3) and eight IPFMs (O−[(9Z) − 17 − Carboxyheptadec − 9 − enoyl]carnitine; LPE O − 17:0; PC 16:1_19:1; sodium deoxycholate; alloursodeoxycholic acid; prostaglandin F2α alpha−carissanol; and aspartame) were significantly altered across the IPF, treatment and control groups and strongly correlated with immune cell infiltration, particularly with activated dendritic cells, γδ T cells, and T helper cell subsets. Three IPFG-based clusters exhibited distinct immune microenvironments, pathway activities and programmed cell death profiles. Integration of transcriptomic and immune features yielded a combined diagnostic nomogram with an AUC of 0.970. Single-cell analysis revealed cell type-specific expression of IPFGs, with CCL2 enriched in macrophages and fibroblasts and SYDE2 and COL4A3 enriched in alveolar epithelial cells. GSEA revealed that key IPFGs were involved in JAK/STAT, WNT, TGF-β, and Toll-like receptor signaling pathways. Conclusions This integrative multiomics analysis delineates key immunometabolic circuits involved in IPF pathogenesis and treatment. The identified IPFGs represent both mechanistic drivers and predictive biomarkers, offering translational potential for immunomodulatory therapies and clinical stratification.