Interaction Network Construction Research Articles

Identification and Verification of Endoplasmic Reticulum Stress-Related Genes as Novel Signatures for Osteoarthritis Diagnosis and Therapy: A Bioinformatics Analysis-Oriented Pilot Study.

Endoplasmic reticulum stress (ERS) has been reported to be closely associated with the development of osteoarthritis (OA), but the underlying mechanisms are not fully delineated. The present study was designed to investigate the involvement of ERS-related genes in regulating OA progression. The expression profiles of OA patients and normal people were downloaded from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) in datasets GSE55457 and GSE55235 were screened and identified by R software with the construction of the protein-protein interaction (PPI) networks. Through the STRING and Venn diagram analysis, hub ERS-related genes were obtained. Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were performed. Biomarkers with high diagnostic values of osteoarthritis (OA) were studied. The hematoxylin and eosin (H&E) staining and micro-CT were applied to evaluate the establishment of the OA model. The expression levels of biomarkers were validated with the use of reverse transcription‑quantitative polymerase chain reaction (RT-qPCR) and western blot. Finally, we evaluated the correlations of hub ERS-related genes with the immune infiltration cells via the CIBERSORT algorithm. A total of 60 downregulated and 52 upregulated DEGs were identified, and the following GO and KEGG pathway analyses verified that those DEGs were mainly enriched in biological process (BP), cellular component (CC), molecular function (MF), and inflammation-associated signal pathways. Interestingly, among all the DEGs, six ER stress-associated genes, including activating transcription factor 3 (ATF3), DEAD-Box Helicase 3 X-Linked (DDX3X), AP-1 transcription factor subunit (JUN), eukaryotic initiation factor 4 (EIF4A1), KDEL endoplasmic reticulum protein retention receptor 3 (KDELR3), and vascular endothelial growth factor A (VEGFA), were found to be closely associated with OA progression, and the following RT-qPCR and Western Blot analysis confirmed that DDX3X, JUN, and VEGFA were upregulated, whereas KDELR3, EIF4A1, and ATF3 were downregulated in OA rats tissues compared to the normal tissues, which were in accordance with our bioinformatics findings. Furthermore, our receiver operating characteristic (ROC) curve analysis verified that the above six ER stress-associated genes could be used as ideal biomarkers for OA diagnosis and those genes also potentially regulated immune responses by influencing the biological functions of mast cells and macrophages. Collectively, the present study firstly identified six ER stress-associated genes (ATF3, DDX3X, JUN, EIF4A1, KDELR3, and VEGFA) that may play critical role in regulating the progression of OA.

Identification of key LncRNAs and mRNAs in fat deposition of Saba pigs based on comparative analysis of transcriptomics

ABSTRACT Long non-coding RNAs (LncRNAs) are gaining attention in the context of pork production due to their role in regulating fat deposition, a crucial economic trait. This study focused on profiling LncRNAs in back fat deposition in Saba and Yorkshire pigs, which exhibit significant differences in fat accumulation. Using transcriptome sequencing and bioinformatics, we identified 704 differentially expressed mRNAs and 50 LncRNAs. We employed cis-regulatory analysis to explore the function of LncRNAs. Gene Ontology (GO) enrichment revealed pathways and enzyme activity-related terms, indicating that LncRNAs may modulate gene expression to influence biological processes. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed enrichment in fat deposition pathways, underscoring the critical role of LncRNAs in this process. Further analysis and construction of LncRNA-mRNA interaction networks revealed 5 potential regulatory networks. Combining the data of differentially expressed LncRNAs and mRNAs, we identified candidate LncRNAs that regulate the expression of ACACA and FASN, which are functionally important genes affecting fat deposition. In summary, this study provides a theoretical foundation and valuable insights into the molecular mechanisms underlying LncRNA-mediated regulation of fat deposition in pigs. These findings are essential for advancing our understanding of porcine fat deposition and enhancing pork production.

Role of CENPF and NDC80 in the rehabilitation nursing of hepatocellular carcinoma and cirrhosis: An observational study.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors globally and often develops on the foundation of chronic liver disease or cirrhosis. Cirrhosis is a clinically prevalent chronic progressive liver disease characterized by diffuse liver damage resulting from long-term or repeated actions of 1 or more etiological factors. However, the impact of CENPF and nuclear division cycle 80 (NDC80) genes on rehabilitation nursing of HCC and cirrhosis remains unclear. HCC and cirrhosis datasets GSE63898 and GSE89377 profile files were downloaded from the gene expression omnibus database generated on platforms GPL13667 and GPL6947, respectively. Differentially expressed genes (DEGs) screening, weighted gene co-expression network analysis (WGCNA), construction and analysis of protein-protein interaction (PPI) networks, functional enrichment analysis, gene set enrichment analysis (GSEA), survival analysis, immune infiltration analysis, and comparative toxicogenomics database (CTD) analysis were conducted. Gene expression heatmaps were plotted. miRNAs regulating central DEGs were selected through TargetScan. A total of 626 DEGs were identified. According to gene ontology (GO) analysis, they were primarily enriched in small molecule metabolic processes, drug metabolic processes, binding of identical proteins, and lipid metabolic processes. Kyoto Encyclopedia of Gene and Genome (KEGG) analysis results indicated that the target genes were mainly enriched in metabolic pathways, phagosomes, glycine, serine, and threonine metabolism. The construction and analysis of the PPI network revealed 3 core genes (NDC80, CENPF, RRM2). Gene expression heatmaps showed that core genes (CENPF, NDC80) were highly expressed in HCC and cirrhosis samples. CTD analysis found that 2 genes (CENPF and NDC80) were associated with liver, jaundice, ascites, fever, dyspepsia, and hepatic encephalopathy. CENPF and NDC80 are highly expressed in HCC and cirrhosis, and CENPF and NDC80 might be the biomarkers of rehabilitation nursing of HCC and cirrhosis.

Identification of bioactive ingredients and potential mechanisms of flowers of Hosta Plantaginea in treating pneumonia based on network pharmacology and experimental validation

Flowers of Hosta plantaginea (H. plantaginea), a widely utilized medicinal herb in Mongolian medicine, holds a significant historical background in terms of its medicinal applications. This herb is renowned for its ability to clear heat and detoxify the body, alleviate cough, and provide relief to the throat. However, the active ingredients and the potential mechanism of action remain ambiguity. The objective of this study was to conduct a comprehensive analysis of the efficacy of H. plantaginea in treating pneumonia, identify its active ingredients and unveil the pharmacological mechanism in the treatment of pneumonia. In vivo experiments demonstrate the plant's anti-pneumonia properties, while mass spectrometry analysis identifies 62 chemicals, with 14 absorbed into the bloodstream. Network pharmacology and Venn analysis reveal 195 targets of 52 active ingredients, with 49 gene targets common to H. plantaginea and pneumonia. Protein-protein interaction (PPI) network construction and enrichment analysis highlight the key targets and pathways such as AKT, EGFR, IL-17. Western blotting confirms downregulation of these pathways, indicating the anti-inflammatory effects of H. plantaginea in treating acute lung injury. Our findings showed that the treatment of ALI with the H. plantaginea exerts its anti-inflammatory effects through multiple components, targets, and pathways. This study established a solid experimental foundation for investigating the various components, targets, and pathways involved in the treatment of pneumonia using H. plantaginea. It offers valuable insights from multiple perspectives and can serve as a reference for the clinical application of this plant in pneumonia treatment.

Transcriptomic analysis revealed potential regulatory biomarkers and repurposable drugs for breast cancer treatment.

Breast cancer (BC) is the most widespread cancer worldwide. Over 2 million new cases of BC were identified in 2020 alone. Despite previous studies, the lack of specific biomarkers and signaling pathways implicated in BC impedes the development of potential therapeutic strategies. We employed several RNAseq datasets to extract differentially expressed genes (DEGs) based on the intersection of all datasets, followed by protein-protein interaction network construction. Using the shared DEGs, we also identified significant gene ontology (GO) and KEGG pathways to understand the signaling pathways involved in BC development. A molecular docking simulation was performed to explore potential interactions between proteins and drugs. The intersection of the four datasets resulted in 146 DEGs common, including AURKB, PLK1, TTK, UBE2C, CDCA8, KIF15, and CDC45 that are significant hub-proteins associated with breastcancer development. These genes are crucial in complement activation, mitotic cytokinesis, aging, and cancer development. We identified key microRNAs (i.e., hsa-miR-16-5p, hsa-miR-1-3p, hsa-miR-147a, hsa-miR-195-5p, and hsa-miR-155-5p) that are associated with aggressive tumor behavior and poor clinical outcomes in BC. Notable transcription factors (TFs) were FOXC1, GATA2, FOXL1, ZNF24 and NR2F6. These biomarkers are involved in regulating cancer cell proliferation, invasion, and migration. Finally, molecular docking suggested Hesperidin, 2-amino-isoxazolopyridines, and NMS-P715 as potential lead compounds against BC progression. We believe that these findings will provide important insight into the BC progression as well as potential biomarkers and drug candidates for therapeutic development.

CXCR4-mediated neutrophil dynamics in periodontitis

Background and objectivePeriodontitis is a common oral disease closely related to immune response and this study is aimed to identify the key immune-related pathogenic genes and analyze the infiltration and function of immune cells in the disease using bioinformatics methods. MethodsTranscriptome datasets and single-cell RNA sequencing (scRNA-seq) datasets were downloaded from the GEO database. We utilized weighted correlation network analysis and least absolute selection and shrinkage operator, protein–protein interaction network construction to screen out key pathogenic genes as well as conducted the cell-type identification by estimating relative subsets of RNA transcripts algorithm to analyze and characterize immune cell types in periodontal tissues. In addition to bioinformatics validations, clinical and cell samples were collected and mouse periodontitis models were constructed to validate the important role of key genes in periodontitis. ResultsBioinformatics analysis pointed out the positive correlation between CXCR4 expression and periodontitis, and revealed the increased infiltration of neutrophils in periodontal inflammatory. Similar results were obtained from clinical samples and animal models. In addition, the clustering and functional enrichment results based on CXCR4 expression levels included activation of immune response and cell migration, implying the possible function of CXCR4 on regulating neutrophil dynamics, which might contribute to periodontitis. Subsequent validation experiments confirmed that the increased expression of CXCR4 in neutrophils under periodontitis, where cell migration-related pathways also were activated. ConclusionCXCR4 could be the key pathogenic gene of periodontitis and CXCR4/CXCL12 signal axial might contribute to the development of periodontitis by mediating neutrophil dynamics, suggesting that CXCR4 could be a potential target to help identify novel strategies for the clinical diagnosis and treatment of periodontitis.

​Comprehensive mendelian randomization analysis of plasma proteomics to identify new therapeutic targets for the treatment of coronary heart disease and myocardial infarction.

Ischemic heart disease is one of the leading causes of mortality worldwide, and thus calls for development of more effective therapeutic strategies. This study aimed to identify potential therapeutic targets for coronary heart disease (CHD) and myocardial infarction (MI) by investigating the causal relationship between plasma proteins and these conditions. A two-sample Mendelian randomization (MR) study was performed to evaluate more than 1600 plasma proteins for their causal associations with CHD and MI. The MR findings were further confirmed through Bayesian colocalization, Summary-data-based Mendelian Randomization (SMR), and Transcriptome-Wide Association Studies (TWAS) analyses. Further analyses, including enrichment analysis, single-cell analysis, MR analysis of cardiovascular risk factors, phenome-wide Mendelian Randomization (Phe-MR), and protein-protein interaction (PPI) network construction were conducted to verify the roles of selected causal proteins. Thirteen proteins were causally associated with CHD, seven of which were also causal for MI. Among them, FES and PCSK9 were causal proteins for both diseases as determined by several analytical methods. PCSK9 was a risk factor of CHD (OR = 1.25, 95% CI: 1.13-1.38, P = 7.47E-06) and MI (OR = 1.36, 95% CI: 1.21-1.54, P = 2.30E-07), whereas FES was protective against CHD (OR = 0.68, 95% CI: 0.59-0.79, P = 6.40E-07) and MI (OR = 0.65, 95% CI: 0.54-0.77, P = 5.38E-07). Further validation through enrichment and single-cell analysis confirmed the causal effects of these proteins. Moreover, MR analysis of cardiovascular risk factors, Phe-MR, and PPI network provided insights into the potential drug development based on the proteins. This study investigated the causal pathways associated with CHD and MI, highlighting the protective and risk roles of FES and PCSK9, respectively. FES. Specifically, the results showed that these proteins are promising therapeutic targets for future drug development.

Integrative analysis based on the cell cycle-related genes identifies TPX2 as a novel prognostic biomarker associated with tumor immunity in breast cancer.

This study aims to identify the essential cell cycle-related genes associated with prognosis in breast cancer (BRCA), and to verify the relationship between the central gene and immune infiltration, so as to provide detailed and comprehensive information for the treatment of BRCA. Gene expression profiles (GSE10780, GSE21422, GSE61304) and the Cancer Genome Atlas (TCGA) BRCA data were used to identify differentially expressed genes (DEGs) and further functional enrichment analysis. STRING and Cytoscape were employed for the protein-protein interaction (PPI) network construction. TPX2 was viewed as the crucial prognostic gene by the Survival and Cox analysis. Furthermore, the connection between TPX2 expression and immune infiltrating cells and immune checkpoints in BRCA was also performed by the TIMER online database and R software. A total of 18 cell cycle-related DEGs were identified in this study. Subsequently, an intersection analysis based on TCGA-BRCA prognostic genes and the above DEGs identified three genes (TPX2, UBE2C, CCNE2) as crucial prognostic candidate biomarkers. Moreover, we also demonstrated that TPX2 is closely associated with immune infiltration in BRCA and a positive relation between TPX2 and PD-L1 expression was firstly detected. These results revealed that TPX2 is a potential prognostic biomarker and closely correlated with immune infiltration in BRCA, which could provide powerful and efficient strategies for breast cancer immunotherapy.

ITGAM is a critical gene in ischemic stroke.

Globally, ischemic stroke (IS) is ranked as the second most prevailing cause of mortality and is considered lethal to human health. This study aimed to identify genes and pathways involved in the onset and progression of IS. GSE16561 and GSE22255 were downloaded from the Gene Expression Omnibus (GEO) database, merged, and subjected to batch effect removal using the ComBat method. The limma package was employed to identify the differentially expressed genes (DEGs), followed by enrichment analysis and protein-protein interaction (PPI) network construction. Afterward, the cytoHubba plugin was utilized to screen the hub genes. Finally, a ROC curve was generated to investigate the diagnostic value of hub genes. Validation analysis through a series of experiments including qPCR, Western blotting, TUNEL, and flow cytometry was performed. The analysis incorporated 59 IS samples and 44 control samples, revealing 226 DEGs, of which 152 were up-regulated and 74 were down-regulated. These DEGs were revealed to be linked with the inflammatory and immune responses through enrichment analyses. Overall, the ROC analysis revealed the remarkable diagnostic potential of ITGAM and MMP9 for IS. Quantitative assessment of these genes showed significant overexpression in IS patients. ITGAM modulation influenced the secretion of critical inflammatory cytokines, such as IL-1β, IL-6, and TNF-α, and had a distinct impact on neuronal apoptosis. The inflammation and immune response were identified as potential pathological mechanisms of IS by bioinformatics and experiments. In addition, ITGAM may be considered a potential therapeutic target for IS.

Mining and exploration of rehabilitation nursing targets for colorectal cancer.

There are often subtle early symptoms of colorectal cancer, a common malignancy of the intestinal tract. However, it is not yet clear how MYC and NCAPG2 are involved in colorectal cancer. We obtained colorectal cancer datasets GSE32323 and GSE113513 from the Gene Expression Omnibus (GEO). After downloading, we identified differentially expressed genes (DEGs) and performed Weighted Gene Co-expression Network Analysis (WGCNA). We then undertook functional enrichment assay, gene set enrichment assay (GSEA) and immune infiltration assay. Protein-protein interaction (PPI) network construction and analysis were undertaken. Survival analysis and Comparative Toxicogenomics Database (CTD) analysis were conducted. A gene expression heat map was generated. We used TargetScan to identify miRNAs that are regulators of DEGs. 1117 DEGs were identified. Their predominant enrichment in activities like the cellular phase of the cell cycle, in cell proliferation, in nuclear and cytoplasmic localisation and in binding to protein-containing complexes was revealed by Gene Ontology (GO). When the enrichment data from GSE32323 and GSE113513 colon cancer datasets were merged, the primary enriched DEGs were linked to the cell cycle, protein complex, cell cycle control, calcium signalling and P53 signalling pathways. In particular, MYC, MAD2L1, CENPF, UBE2C, NUF2 and NCAPG2 were identified as highly expressed in colorectal cancer samples. Comparative Toxicogenomics Database (CTD) demonstrated that the core genes were implicated in the following processes: colorectal neoplasia, tumour cell transformation, inflammation and necrosis. High MYC and NCAPG2 expression has been observed in colorectal cancer, and increased MYC and NCAPG2 expression correlates with worse prognosis.

Novel insights into immune-related genes associated with type 2 diabetes mellitus-related cognitive impairment

BACKGROUND The cognitive impairment in type 2 diabetes mellitus (T2DM) is a multifaceted and advancing state that requires further exploration to fully comprehend. Neuroinflammation is considered to be one of the main mechanisms and the immune system has played a vital role in the progression of the disease. AIM To identify and validate the immune-related genes in the hippocampus associated with T2DM-related cognitive impairment. METHODS To identify differentially expressed genes (DEGs) between T2DM and controls, we used data from the Gene Expression Omnibus database GSE125387. To identify T2DM module genes, we used Weighted Gene Co-Expression Network Analysis. All the genes were subject to Gene Set Enrichment Analysis. Protein-protein interaction network construction and machine learning were utilized to identify three hub genes. Immune cell infiltration analysis was performed. The three hub genes were validated in GSE152539 via receiver operating characteristic curve analysis. Validation experiments including reverse transcription quantitative real-time PCR, Western blotting and immunohistochemistry were conducted both in vivo and in vitro . To identify potential drugs associated with hub genes, we used the Comparative Toxicogenomics Database (CTD). RESULTS A total of 576 DEGs were identified using GSE125387. By taking the intersection of DEGs, T2DM module genes, and immune-related genes, a total of 59 genes associated with the immune system were identified. Afterward, machine learning was utilized to identify three hub genes (H2-T24 , Rac3 , and Tfrc ). The hub genes were associated with a variety of immune cells. The three hub genes were validated in GSE152539. Validation experiments were conducted at the mRNA and protein levels both in vivo and in vitro , consistent with the bioinformatics analysis. Additionally, 11 potential drugs associated with RAC3 and TFRC were identified based on the CTD. CONCLUSION Immune-related genes that differ in expression in the hippocampus are closely linked to microglia. We validated the expression of three hub genes both in vivo and in vitro , consistent with our bioinformatics results. We discovered 11 compounds associated with RAC3 and TFRC . These findings suggest that they are co-regulatory molecules of immunometabolism in diabetic cognitive impairment.

Exploring the roles and potential therapeutic strategies of inflammation and metabolism in the pathogenesis of vitiligo: a mendelian randomization and bioinformatics-based investigation.

Vitiligo, a common autoimmune acquired pigmentary skin disorder, poses challenges due to its unclear pathogenesis. Evidence suggests inflammation and metabolism's pivotal roles in its onset and progression. This study aims to elucidate the causal relationships between vitiligo and inflammatory proteins, immune cells, and metabolites, exploring bidirectional associations and potential drug targets. Mendelian Randomization (MR) analysis encompassed 4,907 plasma proteins, 91 inflammatory proteins, 731 immune cell features, and 1400 metabolites. Bioinformatics analysis included Protein-Protein Interaction (PPI) network construction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Subnetwork discovery and hub protein identification utilized the Molecular Complex Detection (MCODE) plugin. Colocalization analysis and drug target exploration, including molecular docking validation, were performed. MR analysis identified 49 proteins, 39 immune cell features, and 59 metabolites causally related to vitiligo. Bioinformatics analysis revealed significant involvement in PPI, GO enrichment, and KEGG pathways. Subnetwork analysis identified six central proteins, with Interferon Regulatory Factor 3 (IRF3) exhibiting strong colocalization evidence. Molecular docking validated Piceatannol's binding to IRF3, indicating a stable interaction. This study comprehensively elucidates inflammation, immune response, and metabolism's intricate involvement in vitiligo pathogenesis. Identified proteins and pathways offer potential therapeutic targets, with IRF3 emerging as a promising candidate. These findings deepen our understanding of vitiligo's etiology, informing future research and drug development endeavors.

Identification of the Hub Genes Involved in Chikungunya Viral Infection.

Background Chikungunya virus (CHIKV) infection poses a significant global health threat, necessitating a deeper understanding of its molecular mechanisms for effective management and treatment. This study aimed to understand the molecular and geneticmechanisms of CHIKV infection by analyzing microarray expression data. Methodology National Center for Biotechnology Information (NCBI) GEO2R with an adjusted p-value cut-off of <0.05 and |log2FC ≥ 1.5| was used to identify the differentially expressed genes involved in CHIKV infection using microarray data from the Gene Expression Omnibus (GEO) database, followed by enrichment analysis, protein-protein interaction (PPI) network construction, and, finally, hub gene identification. Results Analysis of the microarray dataset revealed 25 highly significant differentially expressed genes (DEGs), including 21 upregulated and four downregulated genes. PPI network analysis elucidated interactions among these DEGs, with hub genes such as ACTB and CTNNB1 exhibiting central roles. Enrichment analysis identified crucial pathways, including leukocyte transendothelial migration, regulation of actin cytoskeleton, and thyroid hormone signaling, implicating their involvement in CHIKV infection. Furthermore, the study highlights potential therapeutic targets such as ACTB and CTNNB1, which showed significant upregulation in infected cells. Conclusions These findings underscore the complex interplay between viral infection and host cellular processes, shedding light on novel avenues for diagnostic marker discovery and advancing antiviral strategies. In this study, we shed light on the molecular and genetic mechanisms of CHIKV infection and the potential role of ACTB and CTNNB1 genes.

The Molecular Landscape of Lung Metastasis in Primary Head and Neck Squamous Cell Carcinomas.

Background Lung metastasis in head and neck cancer (HNC) patients is a critical concern, often indicating an advanced disease stage and a poor prognosis. This study explores the molecular complexities of such metastases, identifying specific genes and pathways that may serve as valuable targets for diagnosis and treatment. The findings underscore the potential for significantly improved patient outcomes through targeted therapeutic strategies. Methodology In this research, we systematically collected raw gene expression data from head and neck squamous cell carcinoma (HNSCC) and lung squamous cell carcinoma (LSCC). By comparing tumorous and normal gene expression profiles from paired patient samples, we identified differentially expressed genes (DEGs). Network analysis helped visualize protein interactions and pinpoint crucial hub genes. Through validation and comparison across several datasets, we identified common DEGs. Additionally, we employed Kaplan-Meier analysis and log-rank testing to examine the relationship between gene expression patterns and patient survival. Result The study identified 145 overlapping DEGs in both HNSCC and LSCC, which are crucial for cancer progression and linked to lung metastasis, offering vital targets for personalized therapy by identifying key genes affecting disease development and patient survival. Pathway analyses linked these to lung metastasis, while protein-protein interaction network construction and hub gene identification highlighted genes crucial for development and patient survival, offering targets for personalized therapy. Conclusion Identifying key genes and pathways in lung metastasis from HNC, this study highlights potential targets for enhanced diagnosis and therapy. It underscores the crucial role of molecular insights in driving forward personalized treatment approaches and improving patient outcomes.

Glucometabolic-Related Genes as Diagnostic Biomarkers and Therapeutic Targets for Alzheimer's Disease and Type 2 Diabetes Mellitus: A Bioinformatics Analysis.

Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are two widespread chronic disorders characterized by shared risk factors and molecular pathways. Glucose metabolism, pivotal for cellular homeostasis and energy supply, plays a critical role in these diseases. Its disturbance has been linked to the pathogenesis of both AD and T2DM. However, a comprehensive investigation into the specific roles of glucometabolic genes in the onset and progression of AD and T2DM has yet to be conducted. By analyzing microarray datasets from the Gene Expression Omnibus (GEO) repository, we identified differentially expressed glucometabolic genes (DEGs) in AD and T2DM cohorts. A range of bioinformatics tools were employed for functional annotation, pathway enrichment, protein interaction network construction, module analysis, ROC curve assessment, correlation matrix construction, gene set enrichment analysis, and gene-drug interaction mapping of these DEGs. Key genes were further validated using quantitative real-time polymerase chain reaction (qRT-PCR) in AD and T2DM murine models. Our investigation identified 41 glucometabolic-related DEGs, with six prominent genes (G6PD, PKM, ENO3, PFKL, PGD, and TALDO1) being common in both AD and T2DM cohorts. These genes play crucial roles in metabolic pathways including glycolysis, pentose phosphate pathway, and amino sugar metabolism. Their diagnostic potential was highlighted by area under curve (AUC) values exceeding 0.6 for AD and 0.8 for T2DM. Further analysis explored the interactions, pathway enrichments, regulatory mechanisms, and potential drug interactions of these key genes. In the AD murine model, quantitative real-time polymerase chain reaction (qRT-PCR) analysis revealed significant upregulation of G6pd, Eno3, and Taldo1. Similarly, in the T2DM murine model, elevated expression levels of G6pd, Pfkl, Eno3, and Pgd were observed. Our rigorous research sheds light on the molecular interconnections between AD and T2DM from a glucometabolic perspective, revealing new opportunities for pharmacological innovation and therapeutic approaches. This study appears to be the first to extensively investigate glucometabolic-associated DEGs and key genes in both AD and T2DM, utilizing multiple datasets. These insights are set to enhance our understanding of the complex pathophysiology underlying these widespread chronic diseases.

Application of network pharmacology and molecular docking to explore the mechanism of Danggui Liuhuang Tang against hyperthyroidism.

To investigate the mechanism of Danggui Liuhuang Tang (DGLHT) in the treatment of hyperthyroidism (HT), we explored the multi-component, multi-target, and multi-pathway mechanism based on the network pharmacology method of traditional Chinese medicine. To take DGLHT into the blood components as the research object, we used GeneCards, Drungbank, Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man (OMIM), Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB), and other databases to predict the potential target of the components. Then, it was integrated with the predicted targets of HT disease to obtain the potential targets of DGLHT in the treatment of HT. We used String database and Cytoscape software for protein-protein interaction network (PPI) construction, and DAVID platform for Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation, the Cytoscape software was used to construct a "component-target-pathway" network; the AutoDock Vina platform was used to conduct molecular docking between the blood entry components and key targets. According to the analysis, a total of 93 active ingredients, 348 disease-related targets, and 36 potential targets were screened out. Among them, key targets such as MAPK1, CCND1, AKT1, and TNF exert curative effects, and the main pathways are the HIF-1 signaling pathway, FoxO signaling pathway, Chemokine signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, T cell receptor signaling pathway, Jak-STAT signaling pathway, and other pathways. Molecular docking results verified the interaction between active ingredients and key targets, among which rustication and quercetin had high docking affinity with key target proteins MAPK1 and CCND1. This study preliminary revealed that DGLHT has the characteristics of multi-component, multi-target, and multi-pathway in the treatment of HT, and it established a scientific foundation for a more detailed investigation of DGLHT's molecular mechanism in the treatment of HT.

Unveiling the role of HIST2H2AC in stroke through single-cell and transcriptome analysis.

Stroke is a leading cause of death and disability, and genetic risk factors play a significant role in its development. Unfortunately, effective therapies for stroke are currently limited. Early detection and diagnosis are critical for improving outcomes and developing new treatment strategies. In this study, we aimed to identify potential biomarkers and effective prevention and treatment strategies for stroke by conducting transcriptome and single-cell analyses. Our analysis included screening for biomarkers, functional enrichment analysis, immune infiltration, cell-cell communication, and single-cell metabolism. Through differential expression analysis, enrichment analysis, and protein-protein interaction (PPI) network construction, we identified HIST2H2AC as a potential biomarker for stroke. Our study also highlighted the diagnostic role of HIST2H2AC in stroke, its relationship with immune cells in the stroke environment, and our improved understanding of metabolic pathways after stroke. Overall, our research provided important insights into the pathogenesis of stroke, including potential biomarkers and treatment strategies that can be explored further to improve outcomes for stroke patients.

An integrated analysis of the anticarcinogenic role of forkhead box protein 1 in oesophageal squamous cell carcinoma.

Forkhead box protein 1 (FOXP1) serves as a tumour promoter or suppressor depending on different cancers, but its effect in oesophageal squamous cell carcinoma has not been fully elucidated. This study investigated the role of FOXP1 in oesophageal squamous cell carcinoma through bioinformatics analysis and experimental verification. We determined through public databases that FOXP1 expresses low in oesophageal squamous cell carcinoma compared with normal tissues, while high expression of FOXP1 indicates a better prognosis. We identified potential target genes regulated by FOXP1, and explored the potential biological processes and signalling pathways involved in FOXP1 in oesophageal squamous cell carcinoma through GO and KEGG enrichment, gene co-expression analysis, and protein interaction network construction. We also analysed the correlation between FOXP1 and tumour immune infiltration levels. We further validated the inhibitory effect of FOXP1 on the proliferation of oesophageal squamous cell carcinoma cells through CCK-8, colony formation and subcutaneous tumour formation assays. This study revealed the anticarcinogenic effect of FOXP1 in oesophageal squamous cell carcinoma, which may serve as a novel biological target for the treatment of tumour.

Systems and Computational Screening identifies SRC and NKIRAS2 as Baseline Correlates of Risk (CoR) for Live Attenuated Oral Typhoid Vaccine (TY21a) associated Protection

AimWe investigated the molecular underpinnings of variation in immune responses to the live attenuated typhoid vaccine (Ty21a) by analyzing the baseline immunological profile. We utilized gene expression datasets obtained from the Gene Expression Omnibus (GEO) database (accession number: GSE100665) before and after immunization. We then employed two distinct computational approaches to identify potential baseline biomarkers associated with responsiveness to the Ty21a vaccine. Main methodsThe first pipeline (knowledge-based) involved the retrieval of differentially expressed genes (DEGs), functional enrichment analysis, protein-protein interaction network construction, and topological network analysis of post-immunization datasets before gauging their pre-vaccination expression levels. The second pipeline utilized an unsupervised machine learning algorithm for data-driven feature selection on pre-immunization datasets. Supervised machine-learning classifiers were employed to computationally validate the identified biomarkers. Key findingsBaseline activation of NKIRAS2 (a negative regulator of NF-kB signalling) and SRC (an adaptor for immune receptor activation) was negatively associated with Ty21a vaccine responsiveness, whereas LOC100134365 exhibited a positive association. The Stochastic Gradient Descent (SGD) algorithm accurately distinguished vaccine responders and non-responders, with 88.8%, 70.3%, and 85.1% accuracy for the three identified genes, respectively. SignificanceThis dual-pronged novel analytical approach provides a comprehensive comparison between knowledge-based and data-driven methods for the prediction of baseline biomarkers associated with Ty21a vaccine responsiveness. The identified genes shed light on the intricate molecular mechanisms that influence vaccine efficacy from the host perspective while pushing the needle further towards the need for development of precise enteric vaccines and on the importance of pre-immunization screening.

Investigation of autophagy‑related genes and immune infiltration in calcific aortic valve disease: A bioinformatics analysis and experimental validation.

The present study aimed to elucidate the role of autophagy-related genes (ARGs) in calcific aortic valve disease (CAVD) and their potential interactions with immune infiltration via experimental verification and bioinformatics analysis. A total of three microarray datasets (GSE12644, GSE51472 and GSE77287) were obtained from the Gene Expression Omnibus database, and gene set enrichment analysis was performed to identify the relationship between autophagy and CAVD. After differentially expressed genes and differentially expressed ARGs (DEARGs) were identified using CAVD samples and normal aortic valve samples, a functional analysis was performed, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, protein-protein interaction network construction, hub gene identification and validation, immune infiltration and drug prediction. The results of the present study indicated a significant relationship between autophagy and CAVD. A total of 46 DEARGs were identified. GO and pathway enrichment analyses revealed the complex roles of DEARGs in regulating CAVD, including multiple gene functions and pathways. A total of 10 hub genes were identified, with three (SPP1, CXCL12 and CXCR4) consistently upregulated in CAVD samples compared with normal aortic valve samples in multiple datasets and experimental validation. Immune infiltration analyses demonstrated significant differences in immune cell proportions between CAVD samples and normal aortic valve samples, thus showing the crucial role of immune infiltration in CAVD development. Furthermore, therapeutic drugs were predicted that could target the identified hub genes, including bisphenol A, resveratrol, progesterone and estradiol. In summary, the present study illuminated the crucial role of autophagy in CAVD development and identified key ARGs as potential therapeutic targets. In addition, the observed immune cell infiltration and predicted autophagy-related drugs suggest promising avenues for future research and novel CAVD treatments.