Jun 2026· International Journal of Molecular Sciences· Vol 27, pp. 5923· 0 citations· 21 references
Medicine
TL;DR
It is suggested that coordinated immune-inflammatory and metabolic signaling networks contribute to the progression from MeS to DMCAD and may serve as potential biomarkers and therapeutic targets for inflammation-driven cardiometabolic disease.
Abstract
Metabolic syndrome (MeS) is a major risk factor for cardiovascular disease and is characterized by chronic low-grade inflammation, immune dysregulation, and metabolic abnormalities. However, the molecular mechanisms linking MeS to diabetic coronary artery disease (DMCAD) remain incompletely understood. Publicly available peripheral blood mononuclear cell (PBMC) transcriptomic datasets of MeS and DMCAD were analyzed using an integrative bioinformatics approach. Differentially expressed genes (DEGs) were identified using the limma package, followed by functional enrichment, protein–protein interaction (PPI) network construction, weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), and miRNA regulatory network analysis. Candidate genes were further evaluated using an independent type 2 diabetes mellitus (T2DM) dataset for external transcriptomic validation. Integrated analyses identified immune-inflammatory and immuno-metabolic pathways as central features of both MeS and DMCAD. Enrichment analyses highlighted cytokine signaling, leukocyte activation, chemotaxis, complement activation, oxidative stress, and vascular inflammatory responses. Network analyses identified CD86, CD33, CCR1, C5AR1, FPR1, CXCL16, and LILRA5 as key hub genes associated with immune regulation and cardiometabolic dysfunction. External transcriptomic validation supported the relevance of CD33, CD86, and LILRA5. miRNA network analysis identified members of the miR-17/92 family and miR-146a-5p as potential upstream regulators. TAM 2.0 enrichment analysis further linked these miRNAs to metabolic syndrome, diabetes mellitus, atherosclerosis, coronary heart disease, immune response, inflammation, and angiogenesis. Our findings suggest that coordinated immune-inflammatory and metabolic signaling networks contribute to the progression from MeS to DMCAD. The identified hub genes and miRNAs may serve as potential biomarkers and therapeutic targets for inflammation-driven cardiometabolic disease.
Aim: Metabolic syndrome (MetS) is a clustering of risk factors that increases susceptibility to type 2 diabetes and cardiovascular disease. This study aimed to perform a comprehensive bioinformatic analysis of genomic data to elucidate molecular pathways underlying MetS.
Methodology: GWAS data from previous MetS studies were analyzed using TargetScan, miRTarBase, Reactome Pathways, KEGG, protein–protein interaction (PPI) networks, and Gene Ontology (GO) mapping. Integration of these datasets identified key miRNAs, metabolic pathways, biological processes, and molecular activities associated with MetS.
Results: hsa-miR-126 was markedly enriched and strongly correlated with MetS. Pathway analysis highlighted cholesterol metabolism (p <0.05) and plasma lipoprotein remodeling (p <0.05) as significant contributors. GO analysis revealed triglyceride homeostasis (p <0.05) and very-low-density lipoprotein particle remodeling (p <0.05) as a key biological processes. Metabolomic analysis established strong links between triacylglycerol and glycerol metabolism. Lipid transport and metabolism emerged as central to MetS pathogenesis, with notable enrichments for high-density lipoprotein particles (p <0.05) and phosphatidylcholine-sterol O-acyltransferase activator activity (p <0.05).
Interpretation: This comprehensive analysis indicates that dysregulation of lipid metabolism is a major pathway in MetS, with specific miRNAs functioning as critical regulatory molecules. These insights suggest potential therapeutic strategies targeting miRNA-mediated regulation of lipid metabolism.
Key words: Cholesterol homeostasis, Lipid metabolism, Lipoprotein remodeling, Metabolic syndrome, miRNA regulation
C. Deepthi, E. V. Ravikanth, P. Reddemma et al.· Journal of environmental bio...· 0 citations
To elucidate the molecular characteristics of synergistic interactions across the clinical stages of coronary heart disease (CHD)—specifically stable angina pectoris (SAP), unstable angina pectoris (UAP), and acute myocardial infarction (AMI)—through integrated metabolomic and proteomic analyses. Based on a cohort including SAP, UAP, AMI, and healthy controls, metabolomic and proteomic analyses were performed to identify differentially expressed molecules, followed by KEGG pathway enrichment analysis. Pathways co-enriched across both omics platforms were selected to construct metabolite-protein interaction networks. The number of pathways co-enriched in both metabolomic and proteomic analyses increased markedly with disease stage. Only two pathways (histidine metabolism and arginine and proline metabolism) were identified in the SAP stage; this number increased to five in the UAP stage (including ferroptosis and efferocytosis) and expanded to 25 in the AMI stage, encompassing three major functional modules: immune inflammation, metabolic reprogramming, and cell signaling. The core network exhibited a stepwise increase in connectivity, shifting from a sparse structure in the SAP stage to a highly interconnected architecture in the AMI stage, with L-glutamate and KNG1 identified as the central hubs in this cross-sectional network. In addition, CNDP1 exhibited a stage-dependent functional transition, shifting from downregulation in SAP to upregulation in AMI. In this cross-sectional analysis, metabolic dysregulation and immune activation exhibited stepwise increases in interconnectivity across the SAP, UAP, and AMI groups, with the most extensive crosstalk observed in the AMI stage—a network configuration consistent with a tightly coupled “molecular storm”. These findings provide novel insights into stage-associated molecular signatures of CHD and identify candidate hub molecules for stage-oriented therapeutic investigation.
Xi-Lun Tan, Yuanxiaoxue Gao, Jia Wang et al.· Scientific Reports· 0 citations
Summary Kidney fibrosis, the final pathological outcome of chronic kidney disease, lacks reliable biomarkers, and the role of lactylation in its pathogenesis remains poorly defined. We integrated multi-omics, machine learning, and experimental validation to identify lactylation-associated biomarkers using two public transcriptomic datasets. After batch correction and differential expression analysis, 13 overlapping lactylation-modified genes were obtained, and eight hub genes were identified via 113 model combinations. Downregulated hub genes regulate core renal metabolic pathways including TCA cycle and oxidative phosphorylation, while upregulated genes mediate immune-inflammatory activation and cytokine signaling. Immune infiltration analysis showed significant immune cell enrichment in fibrotic tissues, with hub genes correlated with T cell subsets. Six hub genes were validated in UUO mouse models and human fibrotic kidney tissues. A five-gene diagnostic panel exhibited robust diagnostic performance with AUCs of 0.89–0.90. This study establishes a lactylation-related diagnostic signature, reveals metabolic-immune crosstalk, and supports early clinical diagnosis of kidney fibrosis.
Ye Kuang, Xiuhong Xiang, Chuan-Mei Peng et al.· iScience· 0 citations
These findings bridge the gap between metabolic lipotoxicity and epigenetic regulation, suggesting that lactylation acts as a critical driver in NAFLD pathogenesis.
Qing-Xuan He, Xu Wang, Peng-Fei Wang et al.· Endocrine, Metabolic & Immun...· 0 citations
Acute myocardial infarction (AMI) is a leading cause of morbidity and mortality worldwide, highlighting the need for novel complementary biomarkers. By integrating bulk and single-cell transcriptomic data with machine learning approaches, calmodulin-related genes associated with AMI and explored their immune-metabolic features were identified. Differential expression and weighted co-expression analyses revealed 60 calmodulin-related genes, from which six key genes (SOCS3, GBP4, ST14, KPNA5, STAB1 and CCL4) were screened using multiple machine learning algorithms and validated in independent datasets. Functional analyses indicated enrichment in immune, inflammatory, and metabolic pathways. Immune infiltration and single-cell transcriptomics showed cell-type-specific expression patterns, with CCL4 predominantly expressed in T and NK cells and markedly reduced in AMI samples. qPCR validation confirmed significant expression changes for four of the six genes in the local cohort. Drug-gene interaction and docking analyses suggested candidate compounds for further investigation. Collectively, the findings suggest that CCL4 may serve as a potential diagnostic biomarker for AMI, and the observed immune-metabolic associations provide a basis for future mechanistic and translational studies.
Jinzhong Bo, Y. Xiang, Yanshuo Ni et al.· Journal of Visualized Experi...· 0 citations
An integrated bioinformatics analysis facilitated the screening of candidate therapeutic targets, mechanisms, and drugs for T2DM and ASCVD, offering new insights into molecular therapies for these conditions.
Wei Du, Guiying Ma, Bingzu Li et al.· Journal of Visualized Experi...· 0 citations