Identification of GSR and CBR1 as biomarkers in HIV-associated emphysema through transcriptomic analysis.

To investigate the roles of oxidative stress-related differentially expressed genes (OSRDEGs) in keloid formation and explore their potential value in diagnosis and treatment. Gene expression data from the GEO database, including GSE145725 and GSE44270 as training sets and GSE7890 as a validation set, were utilized. OSRDEGs were identified, followed by Weighted Gene Co-expression Network Analysis (WGCNA), GO/KEGG enrichment analysis, and Gene Set Enrichment Analysis (GSEA). Key genes were further screened through protein-protein interaction (PPI) network analysis and receiver operating characteristic (ROC) curve analysis. miRNA targets, transcription factors (TF), and potential drug targets of these genes were predicted. Immune cell infiltration analysis was performed to assess the association between OSRDEGs and immune cells, which was validated using GSE7890. Finally, the expression of key genes was experimentally validated using quantitative PCR (qPCR), immunohistochemistry (IHC), and hematoxylin-eosin (HE) staining. A total of 13 OSRDEGs were identified. WGCNA and functional enrichment analyses revealed that these genes were primarily involved in fibrosis and inflammatory processes in keloids, such as the MAPK signaling pathway, lymphocyte and monocyte proliferation, and inflammatory pathways involving IL-18 and IL-23. PPI network analysis, ROC analysis, and immune infiltration results identified Endothelin-1 (EDN1) and Neurotrophin-3(NTF3) as key genes with high sensitivity and specificity. These genes were positively and negatively correlated with activated mast cells, respectively, suggesting their dual regulatory roles in fibrosis and inflammation. External dataset validation, qPCR, correlation analysis, HE staining, and IHC results demonstrated that EDN1 and NTF3 were highly expressed in keloid tissues and were associated with excessive collagen deposition and immune cell infiltration. EDN1 and NTF3, as OSRDEGs, play critical roles in the pathogenesis and progression of keloids. They may contribute to fibrosis and inflammation through the regulation of oxidative stress, the MAPK signaling pathway, and mast cell activation. These findings highlight EDN1 and NTF3 as potential diagnostic biomarkers and therapeutic targets, providing novel insights into the pathogenesis and treatment strategies for keloids.

Acquired Immune Deficiency Syndrome (AIDS) is a chronic and life-threatening condition caused by the human immunodeficiency virus (HIV), which severely weakens the immune system. Despite advances in treatment, AIDS remains incurable. Understanding the molecular mechanisms underlying AIDS progression is crucial for developing effective therapeutic strategies. Therefore, this study aims to identify hub genes associated with AIDS susceptibility and progression, as well as to elucidate potential molecular mechanisms involved. We used the Gene Expression Omnibus (GEO) dataset GSE76246 for this study. Differentially expressed genes (DEGs) were screened, and Weighted Gene Co-expression Network Analysis (WGCNA) was employed to construct gene modules associated with HIV infection. Hub genes were identified using the CytoHubba plugin, and their expression profiles were assessed using box plots. The diagnostic potential of these genes was evaluated using receiver operating characteristic (ROC) analysis. Functional enrichment and Gene Set Enrichment Analysis (GSEA) were conducted to identify key biological pathways. Additionally, we analyzed immune cell infiltration and constructed drug-gene interaction, miRNA and transcription factor (TF) regulatory networks. 101 intersection genes were identified by combining DEGs, Oxidative stress genes and module genes from WGCNA. Functional enrichment analysis highlighted key pathways, including oxidative stress response and apoptotic signaling. A protein-protein interaction (PPI) network analysis identified 10 hub genes (TP53, AKT1, JUN, CTNNB1, PXDN, MAPK3, FOS, MMP9, FOXO1, STAT1), which showed strong diagnostic potential, as evidenced by ROC curve analysis. Immune infiltration analysis revealed significant associations between hub genes and various immune cell populations. Furthermore, drug-gene interaction analysis predicted several potential therapeutic compounds. Additionally, miRNA and TF regulatory networks were constructed, identifying critical regulatory elements influencing the expression of hub genes. This study identified ten hub genes (TP53, AKT1, JUN, CTNNB1, PXDN, MAPK3, FOS, MMP9, FOXO1, STAT1) that play crucial roles in HIV infection and progression. These genes serve as potential biomarkers for HIV diagnosis and therapeutic targets.

The Uncertain Significance of Anti–Glomerular Basement Membrane Antibody Among HIV-Infected Persons With Kidney Disease

Hepatocellular carcinoma (HCC) is a prevalent and life-threatening form of cancer, with Shelian Capsule (SLC), a traditional Chinese medicine (TCM) formulation, being recommended for clinical treatment. However, the mechanisms underlying its efficacy remain elusive. This study sought to uncover the potential mechanisms of SLC in HCC treatment using bioinformatics methods. Bioactive components of SLC were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and HCC-related microarray chip data were sourced from the Gene Expression Omnibus (GEO) database. The selection criteria for components included OB ≧ 30% and DL ≧ 0.18. By integrating the results of differential expression analysis and weighted gene co-expression network analysis (WGCNA), disease-related genes were identified. Therapeutic targets were determined as shared items between candidate targets and disease genes. Protein-protein interaction (PPI) network analysis was conducted for concatenated genes, with core protein clusters identified using the MCODE plugin. Machine learning algorithms were applied to identify signature genes within therapeutic targets. Subsequently, immune cell infiltration analysis, single-cell RNA sequencing (sc-RNA seq) analysis, molecular docking, and ADME analysis were performed for the screened genes. A total of 153 SLC ingredients and 170 candidate targets were identified, along with 494 HCCrelated disease genes. Overlapping items between disease genes and drug candidates represented therapeutic genes, and PPI network analysis was conducted using concatenated genes. MCODE1 and MCODE2 cluster genes underwent Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Four signature genes (TOP2A, CYP1A2, CYP2B6, and IGFBP3) were identified from 28 therapeutic genes using 3 machine learning algorithms, with ROC curves plotted. Molecular docking validated the interaction modes and binding abilities between signature genes and corresponding compounds, with free binding energy all <-7 kcal/mol. Finally, ADME analysis revealed similarities between certain SLC components and the clinical drugs Sorafenib and Lenvatinib. In summary, our study revealed that the mechanism underlying the anti-HCC effects of SLC involves interactions at three levels: components (quercetin, beta-sitosterol, kaempferol, baicalein, stigmasterol, and luteolin), pathways (PI3K-Akt signaling pathway, TNF signaling pathway, and IL-17 signaling pathway), and targets (TOP2A, CYP1A2, CYP2B6, and IGFBP3). This study provides preliminary insights into the potential pharmacological mechanisms of SLC in HCC treatment, aiming to support its clinical application and serve as a reference for future laboratory investigations.

BackgroundSjögren’s syndrome (SS) is an autoimmune disorder impacting exocrine glands, while peripheral atherosclerosis (PA) demonstrates a close link to inflammation. Despite a notable rise in atherosclerosis risk among SS patients in prior investigations, the precise mechanisms remain elusive.MethodsA comprehensive analysis was conducted on seven microarray datasets (GSE7451, GSE23117, GSE143153, GSE28829, GSE100927, GSE159677, and GSE40611). The LIMMA package, in conjunction with weighted gene co-expression network analysis (WGCNA), provides a robust method for identifying differentially expressed genes (DEGs) associated with peripheral atherosclerosis (PA) in Sjögren’s syndrome (SS). Subsequently, machine learning algorithms and protein-protein interaction (PPI) network analysis were employed to further investigate potential predictive genes. These findings were utilized to construct a nomogram and a receiver operating characteristic (ROC) curve, which assessed the predictive accuracy of these genes in PA patients with SS. Additionally, extensive analyses of immune cell infiltration and single-sample gene set enrichment analysis (ssGSEA) were conducted to elucidate the underlying biological mechanisms.ResultsUsing the LIMMA package and WGCNA, 135 DEGs associated with PA in SS were identified. PPI network analysis revealed 17 candidate hub genes. The intersection of gene sets identified by three distinct machine learning algorithms highlighted CCL4, CSF1R, and MX1 as key DEGs. ROC analysis and nomogram construction demonstrated their high predictive accuracy (AUC: 0.971, 95% CI: 0.941–1.000). Analysis of immune cell infiltration showed a significant positive correlation between these hub genes and dysregulated immune cells. Additionally, ssGSEA provided critical biological insights into the progression of PA in SS.ConclusionThis study systematically identified three promising hub genes (CCL4, CSF1R, and MX1) and developed a nomogram for predicting PA in SS. Analysis of immune cell infiltration demonstrated that dysregulated immune cells significantly contribute to the progression of PA. Additionally, ssGSEA analysis offered important insights into the mechanisms by which SS leads to PA.

The mechanisms underlying ferroptosis in neonatal hypoxic-ischemic brain damage (HIBD) remain unclear. Four microarray datasets were collected from the GEO database (three mRNA datasets GSE23317, GSE144456, and GSE112137, and one miRNA microarray dataset GSE184939). Weighted gene co-expression network analysis (WGCNA) was used to identify modules of HIBD-related genes. The ferroptosis-related genes were extracted from FerrDb, of which closely correlated to HIBD were obtained after the intersection with existing HIBD's DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, as well as protein-protein interaction (PPI) network analysis were subsequently conducted. Cytoscape was used to identify central genes. Immune cell infiltration analysis was performed by the CIBERSORT algorithm. Fifty-six ferroptosis-related differentially expressed genes (FRDEGs) were screened, mainly related to ferroptosis, autophagy, hypoxia response, metabolic pathways, and immune inflammation. The seven optimal hub FRDEGs were obtained by intersecting with key modules of WGCNA. Then, the expression levels of the seven optimal hub FRDEGs were validated in the GSE144456 and GSE112137 datasets, and the ferroptosis-related mRNA-miRNA network was established. In addition, this study revealed immune cell infiltration in the HIBD cerebral cortex and the interaction between immune cells. Moreover, notably, specific FRDEGs were strongly positively correlated with immune function. The mechanism of ferroptosis is intricate and closely related to neonatal HIBD. Therefore, targeting ferroptosis-related gene therapy and immunotherapy may have therapeutic prospects for neonatal HIBD.

Chronic obstructive pulmonary disease (COPD) is one of the most prevalent chronic respiratory diseases and the fourth cause of mortality globally. Neutrophilic inflammation has a vital role in the occurrence and progression of COPD. This study aimed to identify the novel hub genes involved in neutrophilic inflammation in COPD through bioinformatic prediction and experimental validation. Both the single-cell RNA sequencing (scRNA-seq) dataset (GSE173896) and the RNA sequencing (RNA-seq) dataset (GSE57148) were downloaded from the Gene Expression Omnibus (GEO) database. The Seurat package was used for quality control, dimensions reduction, and cell identification of scRNA-seq. The irGSEA package was used for scoring individual cells. The Monocle2 package was used for the trajectory analysis of neutrophils. The CIBERSORT algorithm was used for analysis of immune cell infiltration in the lungs of COPD patients and controls in RNA-seq dataset, and weighted gene co-expression network analysis (WGCNA) correlated gene modules with neutrophil infiltration. The Mendelian randomization (MR) analysis explored the causal relationship between feature DEGs and COPD. The protein-protein interaction (PPI) network of novel hub genes was constructed, and real-time quantitative polymerase chain reaction (qRT-PCR) was used to validate novel hub genes in clinical specimens. In scRNA-seq, the gene sets upregulated in COPD samples were related to the neutrophilic inflammatory response and TNF-α activation of the NF-κB signaling pathway. In RNA-seq, immune infiltration analysis showed neutrophils were upregulated in COPD lung tissue. We combined data from differential and modular genes and identified 51 differential genes associated with neutrophilic inflammation. Using MR analysis, 6 genes were explored to be causally associated with COPD. Meanwhile, 11 hub genes were identified by PPI network analysis, and all of them were upregulated. qRT-PCR experiments validated 9 out of 11 genes in peripheral blood leukocytes of COPD patients. Furthermore, 5 genes negatively correlated with lung function in COPD patients. Finally, a network of transcription factors for NAMPT and PTGS2 was constructed. This study identified nine novel hub genes related to the neutrophilic inflammation in COPD, and two genes were risk factors of COPD, which may serve as potential biomarkers for the clinical severity of COPD.

BackgroundSevere ischemia-reperfusion (II/R) injury of the intestines is a leading cause of death and disability. According to earlier research, modulating mitochondrial function is the primary mechanism by which II/R injury is ameliorated. In order to further molecular diagnostics and discover possible treatment targets, it is essential to find biomarkers of mitochondria in II/R injury.MethodsThe datasets GSE96733 and GSE37013, along with mitochondrial-related genes (MRGs), were obtained from the Gene Expression Omnibus (GEO) database and MitoCarta3.0, respectively. GSE96733 conducted differential expression gene (DEGs) analysis and weighted gene co-expression network analysis (WGCNA) module screening. In order to find MRGs that were expressed differently, we got their intersection (DEMRGs) and gene enrichment analysis was carried out. The hub genes were screened using machine learning approaches, protein-protein interaction (PPI) network analysis, and Molecular Complex Detection (MCODE). A nomogram was developed for diagnostic evaluation. Furthermore, the relationship between hub gene expression profiles and immune infiltration landscapes was interrogated through immune cell infiltration analysis. The expression patterns of the hub genes were further validated in the II/R injury model through dataset validation and qRT-PCR assays. The procedure concluded in a gene-related hub network, DSigDB prediction of prospective therapeutic compounds, and molecular docking simulations of the drugs’ binding affinity with important target proteins.ResultsHub genes have been found in five different DEMRGs: Pdk4, Yrdc, Bcl2l11, Bcl2a1d and Pmaip1. The nomogram model was beneficial for diagnosis. Dendritic cells (DC) and M2 macrophages are strongly linked to the 5 Hub genes, according to immune cell infiltration research. Afterwards, the regulatory network showed that hub genes and miRNAs had a complicated connection. Additionally, securinine and ABT-737 were anticipated to be possible therapeutic agents for II/R injury. The validation results for the four hub genes (Pdk4, Yrdc, Bcl2l11, and Pmaip1), obtained from both independent datasets and qRT-PCR, were consistent with the initial bioinformatics analysis.ConclusionPdk4, Yrdc, Bcl2l11, and Pmaip1 have been identified as hub genes closely associated with mitochondrial function in eraly II/R injury, thereby providing a theoretical basis for the diagnosis and treatment of eraly II/R injury.

Objective: This study aims to identify clinically relevant lactylation-related biomarkers in chronic obstructive pulmonary disease (COPD) and investigate their potential mechanistic roles in COPD pathogenesis. Methods: Differentially expressed genes (DEGs) were identified from the GSE21359 dataset, followed by weighted gene co-expression network analysis (WGCNA) to detect COPD-associated modules. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine–recursive feature elimination (SVM–RFE) algorithms were applied to screen lactylation-related biomarkers, with diagnostic performance evaluated through the ROC curve. Candidates were validated in the GSE76925 dataset for expression and diagnostic robustness. Immune cell infiltration patterns were exhibited using EPIC deconvolution. Single-cell transcriptomics (from GSE173896) were processed via the ‘Seurat’ package encompassing quality control, dimensionality reduction, and cell type annotation. Cell-type-specific markers and intercellular communication networks were delineated using the ‘FindAllMarkers’ package and the ‘CellChat’ R package, respectively. In vitro validation was conducted using a cigarette smoke extract (CSE)-induced COPD model. Results: Integrated transcriptomic approaches and multi-algorithm screening (LASSO/Boruta/SVM–RFE) revealed carbonyl reductase 1 (CBR1) and peroxiredoxin 1 (PRDX1) as core COPD biomarkers enriched in oxidation–reduction and inflammatory pathways, with high diagnostic accuracy (AUC > 0.85). Immune profiling and scRNA-seq delineated macrophage and cancer-associated fibroblasts (CAFs) infiltration with oxidative-redox transcriptional dominance in COPD. CBR1 was significantly upregulated in T cells, neutrophils, and mast cells; and PRDX1 showed significant upregulation in endothelial, macrophage, and ciliated cells. Experimental validation in CSE-induced models confirmed significant upregulation of both biomarkers via transcription PCR (qRT-PCR) and immunofluorescence. Conclusions: CBR1 and PRDX1 are lactylation-associated diagnostic markers, with lactylation-driven redox imbalance implicated in COPD progression.

Gastrointestinal Disease in Simian Immunodeficiency Virus-Infected Rhesus Macaques Is Characterized by Proinflammatory Dysregulation of the Interleukin-6-Janus Kinase/Signal Transducer and Activator of Transcription3 Pathway

Sepsis refers to a dysregulated host immune response to infection. It carries a high risk of morbidity and mortality, and its pathogenesis has yet to be fully elucidated. The main aim of this study was to identify prognostic hub genes for sepsis and to predict a competitive endogenous RNA (ceRNA) network that regulates the hub genes. Six transcriptome datasets from the peripheral blood of septic patients were retrieved from the Gene Expression Omnibus (GEO) database. The robust rank aggregation (RRA) method was used to screen differentially expressed genes (DEGs) across these datasets. A comprehensive bioinformatics investigation was conducted, encompassing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using the "clusterProfiler" package in R, as well as gene set enrichment analysis (GSEA) to further elucidate the biological functions and pathways associated with the DEGs. Weighted gene co-expression network analysis (WGCNA) was performed to identify a module significantly associated with sepsis. Integration of this module with protein-protein interaction (PPI) network analysis facilitated the identification of five hub genes. These hub genes were subsequently validated using an independent dataset and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis of peripheral blood samples from septic patients. The prognostic values of these hub genes were assessed via receiver operating characteristic (ROC) curve analysis. Finally, a ceRNA network regulating the prognostic hub genes was constructed by integrating data from a literature review as well as five online databases. RRA analysis identified 164 DEGs across six training cohorts. Bioinformatics analyses revealed concurrent hyperinflammation and immunosuppression in sepsis patients. Five hub genes were identified via WGCNA and PPI network analysis, and their differential expression was verified by the validation dataset (GSE28750) and RT-qPCR analysis in the peripheral blood of septic patients. ROC analysis confirmed four hub genes with prognostic value, and a ceRNA network was predicted to elucidate their regulatory mechanisms. This study identified four hub genes (CLEC4D, GPR84, S100A12, and HK3) with significant prognostic value in sepsis and predicted a ceRNA network (NEAT1-hsa-miR-495-3p-ELF1) regulating their expression. The integrated analysis reconfirmed the concurrent presence of hyperinflammation and immunosuppression in hospitalized sepsis patients. These findings enhance the understanding of sepsis pathogenesis and identify potential therapeutic targets.

SINUSITIS IN HIV INFECTION

Background Smoking is one of the risk factors of coronary heart disease (CHD), while its underlying mechanism is less well defined. Purpose To identify and testify 6 key genes of CHD related to smoking through weighted gene coexpression network analysis (WGCNA), protein-protein interaction (PPI) network analysis, and pathway analysis. Methods CHD patients' samples were first downloaded from Gene Expression Omnibus (GEO). Then, genes of interest were obtained after analysis of variance (ANOVA). Thereafter, 23 coexpressed modules that were determined after genes with similar expression were incorporated via WGCNA. The biological functions of genes in the modules were researched by enrichment analysis. Pearson correlation analysis and PPI network analysis were used to screen core genes related to smoking in CHD. Results The violet module was the most significantly associated with smoking (r = −0.28, p = 0.006). Genes in this module mainly participated in biological functions related to the heart. Altogether, 6 smoking-related core genes were identified through bioinformatics analyses. Their expressions in animal models were detected through the animal experiment. Conclusion This study identified 6 core genes to serve as underlying biomarkers for monitoring and predicting smoker's CHD risk.

BackgroundHuman aortic valve stenosis (AS) and insufficiency (AI) are common diseases in aging population. Identifying the molecular regulatory networks of AS and AI is expected to offer novel perspectives for AS and AI treatment.MethodsHighly correlated modules with the progression of AS and AI were identified by weighted genes co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by the clusterProfiler program package. Differentially expressed genes (DEGs) were identified by the DESeqDataSetFromMatrix function of the DESeq2 program package. The protein-protein interaction (PPI) network analyses were implemented using the STRING online tool and visualized with Cytoscape software. The DEGs in AS and AI groups were overlapped with the top 30 genes with highest connectivity to screen out ten hub genes. The ten hub genes were verified by analyzing the data in high throughput RNA-sequencing dataset and real-time PCR assay using AS and AI aortic valve samples.ResultsBy WGCNA algorithm, 302 highly correlated genes with the degree of AS, degree of AI, and heart failure were identified from highly correlated modules. GO analyses showed that highly correlated genes had close relationship with collagen fibril organization, extracellular matrix organization and extracellular structure organization. KEGG analyses also manifested that protein digestion and absorption, and glutathione metabolism were probably involved in AS and AI pathological courses. Moreover, DEGs were picked out for 302 highly correlated genes in AS and AI groups relative to the normal control group. The PPI network analyses indicated the connectivity among these highly correlated genes. Finally, ten hub genes (CD74, COL1A1, TXNRD1, CCND1, COL5A1, SERPINH1, BCL6, ITGA10, FOS, and JUNB) in AS and AI were found out and verified.ConclusionOur study may provide the underlying molecular targets for the mechanism research, diagnosis, and treatment of AS and AI in the future.

BackgroundAbnormal glycolytic metabolism plays a significant role in pulmonary vascular remodeling in idiopathic pulmonary arterial hypertension (IPAH), yet the specific mechanisms remain unclear. The primary objective of this study is to investigate the key regulatory mechanisms of glycolysis in IPAH.MethodsBulk and single-cell sequencing data obtained from IPAH patient tissue samples were downloaded from the GEO database. scMetabolism and AUCcell analyses of the IPAH single-cell sequencing data were carried out to quantify the glycolytic metabolic activity and identify the main cell types regulating glycolysis, respectively. The ssGSEA method was used to assess the glycolytic activity in each bulk sample within the bulk sequencing data. Differential analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis were conducted to identify key genes associated with glycolysis in IPAH samples. Single-cell sequencing and a monocrotaline (MCT)-induced model of PH in rats were utilized to validate the expression of these key genes.ResultsSingle-cell sequencing data indicated that IPAH patients displayed increased glycolytic activity, which was primarily regulated by fibroblasts. Similarly, bulk transcriptomic data revealed a significant increase in glycolytic activity in IPAH patients. Differential analysis, WGCNA, PPI network analysis, and integrated single-cell analysis further identified insulin-like growth factor-1 (IGF1), lysyl-tRNA synthetase (KARS), caspase-3 (CASP3), and cyclin-dependent kinase inhibitor 2 A (CDKN2A) as key genes associated with fibroblast-mediated glycolysis in IPAH patients. Differential expression of IGF1, KARS, CASP3, and CDKN2A was also observed in our in vivo model of PH.ConclusionOur study identifies IGF1, KARS, CASP3, and CDKN2A as key regulatory genes in glycolysis in IPAH, which provides the basis for the development of targeted therapies.