BACKGROUND: Gluconeogenesis is a critical metabolic pathway for maintaining glucose homeostasis, and its dysregulation can lead to glycometabolic disorders. This study aimed to identify hub biomarkers of these disorders to provide a theoretical foundation for enhancing diagnosis and treatment. METHODS: Gene expression profiles from liver tissues of three well-characterized gluconeogenesis mouse models were analyzed to identify commonly differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA), machine learning techniques, and diagnostic tests on transcriptome data from publicly available datasets of type 2 diabetes mellitus (T2DM) patients were employed to assess the clinical relevance of these DEGs. Subsequently, we identified hub biomarkers associated with gluconeogenesis-related glycometabolic disorders, investigated potential correlations with immune cell types, and validated expression using quantitative polymerase chain reaction in the mouse models. RESULTS: Only a few common DEGs were observed in gluconeogenesis-related glycometabolic disorders across different contributing factors. However, these DEGs were consistently associated with cytokine regulation and oxidative stress (OS). Enrichment analysis highlighted significant alterations in terms related to cytokines and OS. Importantly, osteomodulin ( OMD ), apolipoprotein A4 ( APOA4 ), and insulin like growth factor binding protein 6 ( IGFBP6 ) were identified with potential clinical significance in T2DM patients. These genes demonstrated robust diagnostic performance in T2DM cohorts and were positively correlated with resting dendritic cells. CONCLUSIONS Gluconeogenesis-related glycometabolic disorders exhibit considerable heterogeneity, yet changes in cytokine regulation and OS are universally present. OMD , APOA4 , and IGFBP6  may serve as hub biomarkers for gluconeogenesis-related glycometabolic disorders.
Machine Learning ; Humans ; Computational Biology/methods* ; Biomarkers/metabolism* ; Diabetes Mellitus, Type 2/genetics* ; Animals ; Mice ; Gluconeogenesis/physiology* ; Gene Expression Profiling ; Transcriptome/genetics* ; Gene Regulatory Networks/genetics* ; Clinical Relevance

Heart failure(HF) is the terminal stage of various cardiovascular diseases, characterized by high morbidity and mortality, and it represents one of the major disease burdens for families and society. In recent years, as research on the molecular mechanisms of HF has deepened, a competing endogenous RNA(ceRNA) network mediated by long non-coding RNAs(lncRNAs) and circular RNAs(circRNAs) has been gradually constructed. Extensive research results have confirmed that the ceRNA network is widely involved in pathological processes such as inflammation, oxidative stress, myocardial hypertrophy, apoptosis, remodeling of extracellular matrix components and structure, and ferroptosis in HF. It reveals the complex pathological mechanisms of HF at the epigenetic level. Traditional Chinese medicine(TCM) plays a unique role in improving symptoms and prognosis of HF and intervenes in the ceRNA network in HF through multi-level and multi-target mechanisms. It improves key pathological processes such as myocardial fibrosis and inflammation, making progress in treating HF at the molecular level. This article summarized recent Chinese and international research on the regulatory mechanisms of ceRNA networks in HF, elaborated on the mechanisms of action of ceRNA networks in different pathological stages of HF, and summarized how effective components and compounds of TCM intervene in the ceRNA network to improve HF, so as to refine the molecular mechanisms of HF and provide directions for more precise molecular targeted therapeutic strategies.
Humans ; Heart Failure/metabolism* ; Medicine, Chinese Traditional ; Animals ; Drugs, Chinese Herbal/therapeutic use* ; RNA, Circular/genetics* ; RNA, Long Noncoding/metabolism* ; Gene Regulatory Networks/drug effects* ; RNA/metabolism* ; RNA, Competitive Endogenous

Objective To investigate the common molecular features of immunothrombosis, thus enhancing the comprehension of thrombosis triggered by immune and inflammatory responses and offering crucial insights for identifying potential diagnostic and therapeutic targets. Methods Differential gene expression analysis and functional enrichment analysis were conducted on datasets of systemic lupus erythematosus (SLE) and venous thromboembolism (VTE). The intersection of differentially expressed genes in SLE and VTE with those of neutrophil extracellular traps (NET) yielded cross-talk genes (CG) for SLE-NET and VTE-NET interaction. Further analysis included functional enrichment and protein-protein interaction (PPI) network assessments of these CG to identify hub genes. Venn diagrams and receiver operating characteristic (ROC) curve analysis were employed to pinpoint the most effective shared diagnostic CG, which were validated using a graft-versus-host disease (GVHD) dataset. Results Differential expression genes in SLE and VTE were associated with distinct biological processes, whereas SLE-NET-CG and VTE-NET-CG were implicated in pathways related to leukocyte migration, inflammatory response, and immune response. Through PPI network analysis, several hub genes were identified, with matrix metalloproteinase 9 (MMP9) and S100 calcium-binding protein A12 (S100A12) emerging as the best shared diagnostic CG for SLE (AUC: 0.936 and 0.832) and VTE (AUC: 0.719 and 0.759). Notably, MMP9 exhibited good diagnostic performance in the GVHD dataset (AUC: 0.696). Conclusion This study unveils the common molecular features of SLE, VTE, and NET, emphasizing MMP9 and S100A12 as the optimal shared diagnostic CG, thus providing valuable evidence for the diagnosis and therapeutic strategies related to immunothrombosis. Additionally, the expression of MMP9 in GVHD highlights its critical role in the risk of VTE associated with immune system disorders.
Humans ; Computational Biology/methods* ; Lupus Erythematosus, Systemic/immunology* ; Protein Interaction Maps/genetics* ; Venous Thromboembolism/therapy* ; Matrix Metalloproteinase 9/genetics* ; Extracellular Traps/metabolism* ; Gene Regulatory Networks ; Thrombosis/immunology* ; Graft vs Host Disease/genetics* ; Gene Expression Profiling

Objective To explore macrophage subpopulations in ischemic stroke (IS) by using single-cell RNA sequencing (scRNA-seq) data analysis and High-Dimensional Weighted Gene Co-Expression Network Analysis (hdWGCNA). Methods Based on single-cell sequencing data, transcriptomic information for different cell types was obtained, and macrophages were selected for subpopulation identification. hdWGCNA, cell-cell communication, and pseudotime trajectory analysis were used to explore the characteristics of macrophage subpopulations following IS. Key genes related to IS were identified using microarray data and validated for diagnostic potential through Receiver Operating Characteristic (ROC) analysis. Gene Set Enrichment Analysis (GSEA) was conducted to investigate the potential functions of these genes. Results The scRNA-seq data analysis revealed significant changes in macrophage subpopulation composition after IS. A specific macrophage subpopulation enriched in the stroke group was identified and designated as MCAO-specific macrophages (MSM). Pseudotime trajectory analysis indicated that MSM cells were in an intermediate stage of macrophage differentiation. Cell-cell communication analysis uncovered complex interactions between MSM cells and other cells, with the CCL6-CCR1 signaling axis potentially playing a crucial role in neuroinflammation. Two gene modules associated with MSM were identified via hdWGCNA, significantly enriched in pathways related to NOD-like receptors and antigen processing. By integrating differentially expressed MSM genes with conventional transcriptomic data, three IS-related hub genes were identified: Arg1, CLEC4D, and CLEC4E. Conclusion This study reveals the characteristics and functions of macrophage subpopulations following IS and identifies three hub genes with potential diagnostic value, providing novel insights into the pathological mechanisms of IS.
Macrophages/metabolism* ; Computational Biology/methods* ; Single-Cell Analysis/methods* ; Transcriptome ; Ischemic Stroke/metabolism* ; Animals ; Gene Regulatory Networks ; Gene Expression Profiling ; Humans ; Male

Objective Through integrative bioinformatics analysis of multi-source transcriptomic data, potential biomarkers to asthma epithelial cells were identified. The expression of these candidate target was subsequently validated in lung tissues and epithelial cells from asthma models. Methods The gene expression profile data of epithelial cells from three asthma patient cohorts and corresponding healthy controls were integrated from the Gene Expression Omnibus (GEO) database. Differential expression analysis and gene co-expression network analysis were performed to identify key genes and biological pathways associated with asthma. The key genes were validated in lung tissues and epithelial cells in asthma animal models. Results Differential gene expression analysis revealed 1121 upregulated and 1484 downregulated genes in epithelial cells from asthma patients compared with healthy controls. The biological pathway enrichment analysis revealed that the upregulated genes were mainly involved in glycosylation processes, whereas the downregulated genes were mainly associated with immune cell differentiation process. The gene co-expression network analysis revealed that module 9, enriched in glycosylation-related pathways, was significantly positively correlated with asthma, whereas module 17, associated with insulin and other signaling pathways, showed a significant negative correlation with asthma. We identified the genes of polypeptide N-acetylgalactosaminyltransferase 5 (GALNT5), pyrroline-5-carboxylate reductase 1 (PYCR1), and carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) as key genes within module 9, all of which were significantly upregulated in asthma. Finally, we validated that the expression levels of GALNT5, PYCR1, and CEACAM5 were significantly upregulated in epithelial cells from asthmatic lung tissue. Additionally, using a rat asthma model, we further confirmed that the protein levels of these three genes were significantly upregulated in lung tissues of the model group. Conclusion Through data integration and experimental validation, this study identified key genes and biological pathways closely associated with asthma pathogenesis. These findings provide a novel theoretical basis and potential targets for the diagnosis and treatment of asthma.
Asthma/metabolism* ; Humans ; Epithelial Cells/metabolism* ; Animals ; Biomarkers/metabolism* ; Gene Expression Profiling ; Transcriptome ; Gene Regulatory Networks ; Rats ; Computational Biology

PROPOSE: To investigate the molecular mechanisms underlying the protective effects of ischemic preconditioning (IPC) in patients undergoing total knee arthroplasty. METHODS: GSE21164 was extracted from an online database, followed by an investigation of differentially expressed genes (DEGs) between IPC treatment samples at 2 time points (T0T and T1T). Function and pathway enrichment analyses were performed on the DEGs. A protein-protein interaction network was constructed to identify hub genes according to 5 different algorithms, followed by enrichment analysis. In addition, long noncoding RNAs (lncRNAs) were identified between the T0T and T1T samples. Furthermore, a competing endogenous RNA network was predicted based on the identified lncRNA-messenger RNA (mRNA), lncRNA-microRNA (miRNA), and mRNA-miRNA relationships revealed in this study. Finally, a drug-gene network was investigated. Statistical analyses were performed using GraphPad Prism 8.0. Differences between groups were determined using an unpaired t-test. p < 0.05 was considered significant. RESULTS: A total of 343 DEGs at T0 and 10 DEGs at T1 were identified and compared with their respective control groups, followed by 100 DEGs between T0T and T1T. Based on these 100 DEGs, protein-protein interaction network analysis revealed 9 hub genes, mainly with mitochondria-related functions and the carbon metabolism pathway. Six differentially expressed lncRNAs were investigated between T0T and T1T. A competing endogenous RNA network was constructed using 259 lncRNA-miRNA-mRNA interactions, including alpha-2-macroglobulin antisense RNA 1-miR-7161-5p-iron-sulfur cluster scaffold. Finally, 13 chemical drugs associated with the hub genes were explored. CONCLUSION Iron-sulfur cluster scaffold may promote IPC-induced ischemic tolerance mediated by alpha-2-macroglobulin antisense RNA 1-miR-7161-5p axis. Moreover, IPC may induce a protective response after total knee arthroplasty via mitochondria-related functions and the carbon metabolism pathway, which should be further validated in the near future.
Humans ; Arthroplasty, Replacement, Knee ; Ischemic Preconditioning ; RNA, Long Noncoding/genetics* ; Protein Interaction Maps ; MicroRNAs/genetics* ; RNA, Messenger/genetics* ; Gene Regulatory Networks

OBJECTIVES: To investigate the potential circular RNA (circRNA)-microRNA (miRNA)-messenger RNA (mRNA) immune regulatory network in childhood allergic asthma by analyzing microarray datasets. METHODS: GEO database was used to obtain the datasets of circRNA, miRNA, and mRNA from children with allergic asthma and healthy controls. The Limma package was used to identify differentially expressed circRNA (DEcircRNA), miRNA (DEmiRNA), and mRNA (DEmRNA). ENCORI and other tools were used to predict and construct the regulatory network of endogenous RNA. The DAVID database was used to perform GO and KEGG enrichment analyses, and CIBERSORT and Pearson were used to identify genes associated with immune cell infiltration. RESULTS: A total of 130 DEcircRNAs, 40 DEmiRNAs, and 802 DEmRNAs were identified between the asthma and control groups, and a regulatory network consisting of 12 circRNAs, 7 miRNAs, and 75 mRNAs was established. The GO analysis showed that the differentially expressed genes were mainly involved in the regulation of growth and development, and the KEGG analysis showed that they were mainly involved in the mTOR signaling pathway. The CIBERSORT analysis showed that compared with the control group, the asthma group had higher percentages of CD8⁺ T cells and resting NK cells and lower percentages of resting CD4⁺ memory T cells and activated mast cells. In addition, the Pearson correlation analysis identified six key mRNAs that were positively correlated with immune cell infiltration. CONCLUSIONS The ceRNA immune regulatory network constructed in this study provides a basis for research on the mechanism of childhood allergic asthma and potential therapeutic targets.
Humans ; Asthma/genetics* ; RNA, Circular/physiology* ; MicroRNAs/physiology* ; Child ; Gene Regulatory Networks ; RNA, Messenger/physiology* ; RNA/physiology* ; Male ; Female ; Child, Preschool

OBJECTIVES: Osteoarthritis (OA) and sarcopenia are significant health concerns in the elderly, substantially impacting their daily activities and quality of life. However, the relationship between them remains poorly understood. This study aims to uncover common biomarkers and pathways associated with both OA and sarcopenia. METHODS: Gene expression profiles related to OA and sarcopenia were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control groups were identified using R software. Common DEGs were extracted via Venn diagram analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify biological processes and pathways associated with shared DEGs. Protein-protein interaction (PPI) networks were constructed, and candidate hub genes were ranked using the maximal clique centrality (MCC) algorithm. Further validation of hub gene expression was performed using 2 independent datasets. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of key genes for OA and sarcopenia. Mouse models of OA and sarcopenia were established. Hematoxylin-eosin and Safranin O/Fast Green staining were used to validate the OA model. The sarcopenia model was validated via rotarod testing and quadriceps muscle mass measurement. Real-time reverse transcription PCR (real-time RT-PCR) was employed to assess the mRNA expression levels of candidate key genes in both models. Gene set enrichment analysis (GSEA) was conducted to identify pathways associated with the selected shared key genes in both diseases. RESULTS: A total of 89 common DEGs were identified in the gene expression profiles of OA and sarcopenia, including 76 upregulated and 13 downregulated genes. These 89 DEGs were significantly enriched in protein digestion and absorption, the PI3K-Akt signaling pathway, and extracellular matrix-receptor interaction. PPI network analysis and MCC algorithm analysis of the 89 common DEGs identified the top 17 candidate hub genes. Based on the differential expression analysis of these 17 candidate hub genes in the validation datasets, AEBP1 and COL8A2 were ultimately selected as the common key genes for both diseases, both of which showed a significant upregulation trend in the disease groups (all P<0.05). The value of area under the curve (AUC) for AEBP1 and COL8A2 in the OA and sarcopenia datasets were all greater than 0.7, indicating that both genes have potential value in predicting OA and sarcopenia. Real-time RT-PCR results showed that the mRNA expression levels of AEBP1 and COL8A2 were significantly upregulated in the disease groups (all P<0.05), consistent with the results observed in the bioinformatics analysis. GSEA revealed that AEBP1 and COL8A2 were closely related to extracellular matrix-receptor interaction, ribosome, and oxidative phosphorylation in OA and sarcopenia. CONCLUSIONS AEBP1 and COL8A2 have the potential to serve as common biomarkers for OA and sarcopenia. The extracellular matrix-receptor interaction pathway may represent a potential target for the prevention and treatment of both OA and sarcopenia.
Sarcopenia/genetics* ; Osteoarthritis/genetics* ; Computational Biology/methods* ; Humans ; Protein Interaction Maps/genetics* ; Animals ; Mice ; Gene Expression Profiling ; Gene Ontology ; Transcriptome ; Male ; Signal Transduction/genetics* ; Gene Regulatory Networks

BACKGROUND: PANoptosis has the features of pyroptosis, apoptosis, and necroptosis. Numerous studies have confirmed the diverse roles of various types of cell death in acute liver failure (ALF), but limited attention has been given to the crosstalk among them. In this study, we aimed to explore the role of PANoptosis in ALF and uncover new targets for its prevention or treatment. METHODS: Three ALF-related datasets (GSE14668, GSE62029, and GSE74000) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). Hub genes were identified through intersecting DEGs, genes obtained from weighted gene co-expression network analysis (WGCNA), and genes related to PANoptosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein‍‒‍protein interaction (PPI) analyses and gene set enrichment analysis (GSEA) were performed to determine functional roles. Verification was performed using an ALF mouse model. RESULTS: Our results showed that expression of seven hub genes (B-cell lymphoma-2-modifying factor (BMF), B-cell lymphoma-2-interacting protein 3-like (BNIP3L), Caspase-1 (CASP1), receptor-interacting protein kinase 3 (RIPK3), uveal autoantigen with coiled-coil domains and ankyrin repeats protein (UACA), uncoordinated-5 homolog B receptor (UNC5B), and Z-DNA-binding protein 1 (ZBP1)) was up-regulated in liver samples of patients. However, in the ALF mouse model, the expression of BNIP3L, RIPK3, phosphorylated RIPK3 (P-RIPK3), UACA, and cleaved caspase-1 was up-regulated, while the expression of CASP1 and UNC5B was down-regulated. The expression of ZBP1 and BMF increased only during the development of ALF, and there was no significant change in the end stage. Immunofluorescence of mouse liver tissue showed that macrophages expressed all seven markers. Western blot results showed that pyroptosis, apoptosis, and necroptosis were always involved in lipopolysaccharide (LPS)/ d-galactosamine (d-gal)‍-induced ALF mice. The ALF cell model showed that bone marrow-derived macrophages (BMDMs) form PANoptosomes after LPS stimulation. CONCLUSIONS: Our results suggest that PANoptosis of macrophages promotes the development of ALF. The seven new ALF biomarkers identified and validated in this study may contribute to further investigation of diagnostic markers or novel therapeutic targets of ALF.
Animals ; Liver Failure, Acute/genetics* ; Computational Biology ; Mice ; Pyroptosis/genetics* ; Humans ; Protein Interaction Maps ; Apoptosis/genetics* ; Necroptosis/genetics* ; Gene Regulatory Networks ; Gene Ontology ; Gene Expression Profiling ; Disease Models, Animal

OBJECTIVES: To explore the role of oxidative stress and immune infiltration in rheumatoid arthritis (RA). METHODS: RA datasets GSE55235 (10 RA vs 10 normal samples) and GSE55457 (13 RA vs 10 normal samples) from the GEO database were merged as the test set to identify the differentially expressed genes (DEGs) in RA using R. The DEGs were intersected with oxidative stress-related genes to obtain oxidative stress-associated DEGs. KEGG and GO enrichment analyses of the DEGs were performed, and the RA-related pathways and biological processes were analyzed using GSEA. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape, and the top 10 key genes were obtained using the Degree algorithm. The validation dataset GSE1919 from GEO database was used for ROC analysis of the key genes to obtain the core genes, and their correlations with infiltrating immune cells were analyzed using CIBERSORT. The results were verified by RT-qPCR for detecting expression levels of the core genes in RA and normal joint samples. RESULTS: We identified 89 oxidative stress-associated DEGs. Enrichment analysis suggested that these DEGs were involved in the biological processes including oxidative stress, chemical stress response, reactive oxygen species response, and lipopolysaccharide response. ROC analysis showed that the 5 core genes (STAT1, MMP9, MYC, CCL5, and JUN) all had AUC values >0.7, indicating their high diagnostic sensitivity and specificity for RA. These genes were closely correlated with immune cells, particularly T cells. RT-qPCR confirmed significant differential expressions of the core genes between RA and normal samples. CONCLUSIONS Oxidative stress and diverse immune responses are features of RA, and the immune responses contribute to activation of oxidative stress. The identified core genes can potential serve as new diagnostic markers for RA.
Arthritis, Rheumatoid/genetics* ; Oxidative Stress/genetics* ; Humans ; Computational Biology ; Protein Interaction Maps ; Gene Expression Profiling ; Gene Regulatory Networks