Transcriptomic Analysis, Serum Metabolomic–Lipidomic Integration and Machine Learning Identify Key Genes and Distinct Molecular Signatures of TCM Syndromes in Chronic Insomnia

Background: Chronic insomnia is an important global health concern, but its molecular heterogeneity remains incompletely understood. Objective biomarkers may help characterize chronic insomnia and its traditional Chinese medicine (TCM) syndrome patterns. Methods: We applied a Public Evidence-informed Prioritization–Multi-omics Discovery–Targeted Validation (PMT) framework integrating public transcriptomic datasets with serum profiles from healthy controls and patients with chronic insomnia classified as excess (Shi) or deficiency (Xu) syndrome. Analyses included transcriptomic data processing and machine learning-based gene prioritization, serum metabolomics and lipidomics, targeted neurotransmitter quantification, bile acid profiling, and pathway-level comparison across omics datasets. Results: Transcriptomic analysis and weighted gene co-expression network analysis identified a phenotype-associated module containing 178 differentially expressed genes (DEGs), and three machine learning methods prioritized 11 candidate genes. Compared with controls, the Shi group had 53 differential metabolites and 87 differential lipids, whereas the Xu group had 49 differential metabolites and 80 differential lipids. Serum analyses revealed syndrome-associated differences in N-acetylated amino acids, lipid species, neurotransmitter-related metabolites, and bile acids. Kyoto Encyclopedia of Genes and Genomes (KEGG)-based integrative analysis identified shared pathways linking transcriptomic alterations with serum metabolomic and lipidomic changes, highlighting pathway-level convergence across the three omics layers. Cross-validated receiver operating characteristic analyses showed variable discriminatory performance, generally stronger for insomnia identification than for differentiation between Shi and Xu syndromes. Conclusions: Using the PMT framework, this study delineated distinct gene–metabolite–lipid signatures of chronic insomnia and its major TCM syndromes, providing a molecular basis for objective diagnosis, syndrome differentiation, and precision intervention.