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A total of 138 cDEGs were screened from mediastinal lymph nodes and peripheral whole blood. Among them, 6 hub cDEGs including CTSS, CYBB, FPR2, MNDA, TLR1 and TLR8 with elevated degree and betweenness levels were illustrated in protein-protein interaction network. In comparison to healthy controls, CTSS (1.61 vs. 1.05), CYBB (1.68 vs. 1.07), FPR2 (2.77 vs. 0.96), MNDA (2.14 vs. 1.23), TLR1 (1.56 vs. 1.09), and TLR8 (2.14 vs. 0.98) displayed notably elevated expression levels within pulmonary sarcoidosis PBMC samples (P < 0.0001 for FPR2 and P < 0.05 for others), echoing with prior mRNA microarray findings. The most significant functional pathways were immune response, inflammatory response, plasma membrane and extracellular exosome, with 6 hub cDEGs distributing along these pathways. CTSS, CYBB, FPR2, MNDA, TLR1, and TLR8 could be conducive to improving the diagnostic process and understanding the underlying mechanisms of pulmonary sarcoidosis. [ABSTRACT FROM AUTHOR]

Objective: Patients with rheumatoid arthritis (RA) have an increased risk of developing pulp and periapical disease (PAP), but the causal relationship and shared genetic factors between these conditions have not been explored. This study aimed to investigate the bidirectional causal relationship between RA and PAP and to analyze shared genes and pathogenic pathways. Methods: We utilized GWAS data from the IEU Open GWAS Project and employed five Mendelian randomization methods (MREgger, weighted median, inverse variance weighted, simple mode, and weighted mode) to investigate the bidirectional causal relationship between RA and PAP. Transcriptome data for RA and irreversible pulpitis (IRP) were obtained from the GEO database. Hub genes were identified through differential analysis, CytoHubba, machine learning (ML), and other methods. The immune infiltration of both diseases was analyzed using the ssGSEA method. Finally, we constructed a regulatory network for miRNAs, transcription factors, chemicals, diseases, and RNA-binding proteins based on the identified hub genes. Results: RA was significantly associated with an increased risk of PAP (OR = 1.1284, 95% CI 1.0674-1.1929, p < 0.001). However, there was insufficient evidence to support the hypothesis that PAP increased the risk of RA. Integrating datasets and differential analysis identified 84 shared genes primarily involved in immune and inflammatory pathways, including the IL-17 signaling pathway, Th17 cell differentiation, and TNF signaling pathway. Using CytoHubba and three ML methods, we identified three hub genes (HLA-DRA, ITGAX, and PTPRC) that are significantly correlated and valuable for diagnosing RA and IRP. We then constructed a comprehensive regulatory network using the miRDB, miRWalk, ChipBase, hTFtarget, CTD, MalaCards, DisGeNET, and ENCORI databases. Conclusion: RA may increase the risk of PAP. The three key genes, HLA-DRA, ITGAX, and PTPRC, have significant diagnostic value for both RA and IRP. [ABSTRACT FROM AUTHOR]

Background: Sleep disorders (SD) are known to have a profound impact on human health and quality of life although their exact pathogenic mechanisms remain poorly understood. Methods: The study first accessed SD datasets from the GEO and identified DEGs. These DEGs were then subjected to gene set enrichment analysis. Several advanced techniques, including the RF, SVM-RFE, PPI networks, and LASSO methodologies, were utilized to identify hub genes closely associated with SD. Additionally, the ssGSEA approach was employed to analyze immune cell infiltration and functional gene set scores in SD. DEGs were also scrutinized in relation to miRNA, and the DGIdb database was used to explore potential pharmacological treatments for SD. Furthermore, in an SD murine model, the expression levels of these hub genes were confirmed through RT-qPCR and Western Blot analyses. Results: The findings of the study indicate that DEGs are significantly enriched in functions and pathways related to immune cell activity, stress response, and neural system regulation. The analysis of immunoinfiltration demonstrated a marked elevation in the levels of Activated CD4+ T cells and CD8+ T cells in the SD cohort, accompanied by a notable rise in Central memory CD4 T cells, Central memory CD8 T cells, and Natural killer T cells. Using machine learning algorithms, the study also identified hub genes closely associated with SD, including IPO9, RAP2A, DDX17, MBNL2, PIK3AP1, and ZNF385A. Based on these genes, an SD diagnostic model was constructed and its efficacy validated across multiple datasets. In the SD murine model, the mRNA and protein expressions of these 6 hub genes were found to be consistent with the results of the bioinformatics analysis. Conclusion: In conclusion, this study identified 6 genes closely linked to SD, which may play pivotal roles in neural system development, the immune microenvironment, and inflammatory responses. Additionally, the key gene-based SD diagnostic model constructed in this study, validated on multiple datasets showed a high degree of reliability and accuracy, predicting its wide potential for clinical applications. However, limited by the range of data sources and sample size, this may affect the generalizability of the results. [ABSTRACT FROM AUTHOR]

Monocytes are pivotal immune cells in eliciting specific immune responses and can exert a significant impact on the progression, prognosis, and immunotherapy of intracranial aneurysms (IAs). The objective of this study was to identify monocyte/ macrophage (Mo/MF)-associated gene signatures to elucidate their correlation with the pathogenesis and immune microenvironment of IAs, thereby offering potential avenues for targeted therapy against IAs. Single-cell RNA-sequencing (scRNA-seq) data of IAs were acquired from the Gene Expression Synthesis (GEO) database. The significant infiltration of monocyte subsets in the parietal tissue of IAs was identified using single-cell RNA sequencing and high-dimensional weighted gene coexpression network analysis (hdWGCNA). The integration of six machine learning algorithms identified four crucial genes linked to these Mo/MF. Subsequently, we developed a multilayer perceptron (MLP) neural model for the diagnosis of IAs (independent external test AUC=1.0, sensitivity =100%, specificity =100%). Furthermore, we employed the CIBERSORT method and MCP counter to establish the correlation between monocyte characteristics and immune cell infiltration as well as patient heterogeneity. Our findings offer valuable insights into the molecular characterization of monocyte infiltration in IAs, which plays a pivotal role in shaping the immune microenvironment of IAs. Recognizing this characterization is crucial for comprehending the limitations associated with targeted therapies for IAs. Ultimately, the results were verified by real-time fluorescence quantitative PCR and Immunohistochemistry. [ABSTRACT FROM AUTHOR]

Background: Systemic lupus erythematosus (SLE) is a multi-organ chronic autoimmune disease. Inflammatory bowel disease (IBD) is a common chronic inflammatory disease of the gastrointestinal tract. Previous studies have shown that SLE and IBD share common pathogenic pathways and genetic susceptibility, but the specific pathogenic mechanisms remain unclear. Methods: The datasets of SLE and IBD were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified using the Limma package. Weighted gene coexpression network analysis (WGCNA) was used to determine co-expression modules related to SLE and IBD. Pathway enrichment was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis for co-driver genes. Using the Least AbsoluteShrinkage and Selection Operator (Lasso) regressionand Support Vector Machine-Recursive Feature Elimination (SVM-RFE), common diagnostic markers for both diseases were further evaluated. Then, we utilizedthe CIBERSORT method to assess the abundance of immune cell infiltration. Finally,we used the single-cell analysis to obtain the location of common diagnostic markers. Results: 71 common driver genes were identified in the SLE and IBD cohorts based on the DEGs and module genes. KEGG and GO enrichment results showed that these genes were closely associated with positive regulation of programmed cell death and inflammatory responses. By using LASSO regression and SVM, five hub genes (KLRF1, GZMK, KLRB1, CD40LG, and IL-7R) were ultimately determined as common diagnostic markers for SLE and IBD. ROC curve analysis also showed good diagnostic performance. The outcomes of immune cell infiltration demonstrated that SLE and IBD shared almost identical immune infiltration patterns. Furthermore, the majority of the hub genes were commonly expressed in NK cells by single-cell analysis. Conclusion: This study demonstrates that SLE and IBD share common diagnostic markers and pathogenic pathways. In addition, SLE and IBD show similar immune cellinfiltration microenvironments which provides newperspectives for future treatment. [ABSTRACT FROM AUTHOR]

Background: Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration. Methods: We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database.Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine--recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO)were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis. Results: Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 (RRM2), DLG-associated protein 5 (DLGAP5), and kinesin family member 11 (KIF11), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis. Conclusion: RRM2, DLGAP5, and KIF11 could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease (COVID-19), caused by SARS-CoV-2, has emerged as a infectious disease, coexisting with widespread seasonal and sporadic influenza epidemics globally. Individuals living with HIV, characterized by compromised immune systems, face an elevated risk of severe outcomes and increased mortality when affected by COVID-19. Despite this connection, the molecular intricacies linking COVID-19, influenza, and HIV remain unclear. Our research endeavors to elucidate the shared pathways and molecular markers in individuals with HIV concurrently infected with COVID-19 and influenza. Furthermore, we aim to identify potential medications that may prove beneficial in managing these three interconnected illnesses. Methods: Sequencing data for COVID-19 (GSE157103), influenza (GSE185576), and HIV (GSE195434) were retrieved from the GEO database. Commonly expressed differentially expressed genes (DEGs) were identified across the three datasets, followed by immune infiltration analysis and diagnostic ROC analysis on the DEGs. Functional enrichment analysis was performed using GO/KEGG and Gene Set Enrichment Analysis (GSEA). Hub genes were screened through a Protein-Protein Interaction networks (PPIs) analysis among DEGs. Analysis of miRNAs, transcription factors, drug chemicals, diseases, and RNAbinding proteins was conducted based on the identified hub genes. Finally, quantitative PCR (qPCR) expression verification was undertaken for selected hub genes. Results: The analysis of the three datasets revealed a total of 22 shared DEGs, with the majority exhibiting an area under the curve value exceeding 0.7. Functional enrichment analysis with GO/KEGG and GSEA primarily highlighted signaling pathways associated with ribosomes and tumors. The ten identified hub genes included IFI44L, IFI44, RSAD2, ISG15, IFIT3, OAS1, EIF2AK2, IFI27, OASL, and EPSTI1. Additionally, five crucial miRNAs (hsa-miR-8060, hsa-miR-6890-5p, hsa-miR-5003-3p, hsa-miR-6893-3p, and hsa-miR-6069), five essential transcription factors (CREB1, CEBPB, EGR1, EP300, and IRF1), and the top ten significant drug chemicals (estradiol, progesterone, tretinoin, calcitriol, fluorouracil, methotrexate, lipopolysaccharide, valproic acid, silicon dioxide, cyclosporine) were identified. Conclusion: This research provides valuable insights into shared molecular targets, signaling pathways, drug chemicals, and potential biomarkers for individuals facing the complex intersection of COVID-19, influenza, and HIV. These findings hold promise for enhancing the precision of diagnosis and treatment for individuals with HIV co-infected with COVID-19 and influenza. [ABSTRACT FROM AUTHOR]

Background: Non-alcoholic fatty liver disease (NAFLD), which includes simple steatosis (SS) and non-alcoholic steatohepatitis (NASH), is a significant contributor to liver disease on a global scale. The change of immunity-related genes (IRGs) expression level leads to different immune infiltrations. However, the expression of IRGs and possible regulatory mechanisms involved in NAFLD remain unclear. The objective of our research is to investigate crucial genes linked to the development of NAFLD and the transition from SS to NASH. Methods: Dataset GSE89632, which includes healthy controls, SS patients, and NASH patients, was obtained using the GEO database. To examine the correlation between sets of genes and clinical characteristics, we employed weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Hub genes were extracted using a network of proteinprotein interactions (PPI) and three different machine learning algorithms. To validate the findings, another dataset that is publicly accessible and mice that were subjected to a high-fat diet (HFD) or MCD diet were utilized. Furthermore, the ESTIMATE algorithm and ssGSEA were employed to investigate the immune landscape in the normal versus SS group and SS versus NASH group, additionally, the relationship between immune infiltration and the expression of hub genes was also examined. Results: A total of 28 immune related key genes were selected. Most of these genes expressed reverse patterns in the initial and progressive stages of NAFLD. GO and KEGG analyses showed that they were focused on the cytokine related pathways and immune cell activation and chemotaxis. After screening by various algorithms, we obtained two hub genes, including JUN and CCL20. Validation of these findings was confirmed by analyzing gene expression patterns in both the validation dataset and the mouse model. Ultimately, two hub genes were discovered to have a significant correlation with the infiltration of immune cells. Conclusion: We proposed that there were dynamic changes in the expression levels of IRGs in different stages of NAFLD disease, which led to different immune landscapes in SS and NASH. The findings of our research could serve as a guide for the accurate management of various phases of NAFLD. [ABSTRACT FROM AUTHOR]

There is sufficient evidence indicating that keloid is strongly associated with atopic dermatitis (AD) across ethnic groups. However, the molecular mechanism underlying the association is not fully understood. The aim of this study is to discover the underlying mechanism of the association between keloid and AD by integrating comprehensive bioinformatics techniques and machine learning methods. The gene expression profiles of keloid and AD were downloaded from the Gene Expression Omnibus (GEO) database. A total of 449 differentially expressed genes (DEGs) were found to be shared in keloid and AD using the training datasets of GEO (GSE158395 and GSE121212). The hub genes were identified using the protein-protein interaction network and Cytoscape software. 20 of the most significant hub genes were selected, which were mainly involved in the regulation of the inflammatory and immune response. Through two machine learning algorithms of LASSO and SVM-RFE, CCR5 was identified as the most important key gene. Subsequently, upregulated CCR5 gene expression was confirmed in validation GEO datasets (GSE188952 and GSE32924) and clinical samples of keloid and AD. Immune infiltration analysis showed that T helper (Th) 1, 2 and 17 cells were significantly enriched in the microenvironment of both keloid and AD. Positive correlations were found between CCR5 and Th1, Th2 and Th17 cells. Finally, two TFs of CCR5, NR3C2 and YY1, were identified, both of which were downregulated in keloid and AD tissues. Our study firstly reveals that keloid and AD shared common inflammatory and immune pathways. Moreover, CCR5 plays a key role in the pathogenesis association between keloid and AD. The common pathways and key genes may shed light on further mechanism research and targeted therapy, and may provide therapeutic interventions of keloid with AD. [ABSTRACT FROM AUTHOR]

Background: Chronic spontaneous urticaria (CSU) is defined by the spontaneous occurrence of wheals and/or angioedema for >6 weeks. The pathogenesis involves skin mast cells, but the complex causes of their activation remain to be characterized in detail. Objectives: To explore disease-driving genes and biological pathways in CSU. Methods: Two microarray data sets, e.g., GSE57178 and GSE72540, with mRNA information of skin from CSU patients, were downloaded from the Gene Expression Omnibus (GEO) database. An integrated bioinformatics pipeline including identification of differentially expressed genes (DEGs), functional enrichment analysis, protein-protein interaction (PPI) network analysis, coexpression and drug prediction analysis, and immune and stromal cells deconvolution analyses were applied to identify hub genes and key drivers of CSU pathogenesis. Results: In total, we identified 92 up-regulated and 7 down-regulated genes in CSU lesions. These were significantly enriched in CSU-related pathways such as TNF, NF-kB, and JAK-STAT signaling. Based on PPI network modeling, four genes, i.e., IL-6, TLR-4, ICAM-1, and PTGS-2, were computationally identified as key pathogenic players in CSU. Immune infiltration analyses indicated that dendritic cells, Th2 cells, mast cells, megakaryocyte-erythroid progenitor, preadipocytes, and M1 macrophages were increased in lesional CSU skin. Conclusion: Our results offer new insights on the pathogenesis of CSU and suggest that TNF, NF-kB, JAK-STAT, IL-6, TLR-4, ICAM-1, and PTGS-2 may be candidate targets for novel CSU treatments. [ABSTRACT FROM AUTHOR]

Background: Postoperative systemic inflammatory dysregulation (PSID) is characterised by strongly interlinked immune and metabolic abnormalities. However, the hub genes responsible for the interconnections between these two systemic alterations remain to be identified. Methods: We analysed differentially expressed genes (DEGs) of individual peripheral blood nucleated cells in patients with PSID (n = 21, CRP > 250 mg/L) and control patients (n = 25, CRP < 75 mg/L) following major abdominal surgery, along with their biological functions. Correlation analyses were conducted to explore the interconnections of immune-related DEGs (irDEGs) and metabolismrelated DEGs (mrDEGs). Two methods were used to screen hub genes for irDEGs and mrDEGs: we screened for hub genes among DEGs via 12 algorithms using CytoHubba in Cytoscape, and also screened for hub immune-related and metabolic-related genes using weighted gene co-expression network analysis. The hub genes selected were involved in the interaction between changes in immunity and metabolism in PSID. Finally, we validated our results in mice with PSID to confirm the findings. Results: We identified 512 upregulated and 254 downregulated DEGs in patients with PSID compared with controls. Gene enrichment analysis revealed that DEGs were significantly associated with immune- and metabolism-related biological processes and pathways. Correlation analyses revealed a close association between irDEGs and mrDEGs. Fourteen unique hub genes were identified via 12 screening algorithms using CytoHubba in Cytoscape and via weighted gene co-expression network analysis. Among these, CD28, CD40LG, MAPK14, and S100A12 were identified as hub genes among both immune- and metabolismrelated genes; these genes play a critical role in the interaction between alterations in immunity and metabolism in PSID. The experimental results also showed that the expression of these genes was significantly altered in PSID mice. Conclusion: This study identified hub genes associated with immune and metabolic alterations in patients with PSID and hub genes that link these alterations. These findings provide novel insights into the mechanisms underlying immune and metabolic interactions and new targets for clinical treatment can be proposed on this basis. [ABSTRACT FROM AUTHOR]

Background: Systemic lupus erythematosus (SLE) and primary Sjögren's syndrome (pSS) are common systemic autoimmune diseases that share a wide range of clinical manifestations and serological features. This study investigates genes, signaling pathways, and transcription factors (TFs) shared between SLE and pSS. Methods: Gene expression profiles of SLE and pSS were obtained from the Gene Expression Omnibus (GEO). Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis were conducted to identify shared genes related to SLE and pSS. Overlapping genes were then subject to Gene Ontology (GO) and protein-protein interaction (PPI) network analyses. Cytoscape plugins cytoHubba and iRegulon were subsequently used to screen shared hub genes and predict TFs. In addition, gene set variation analysis (GSVA) and CIBERSORTx were used to calculate the correlations between hub genes and immune cells as well as related pathways. To confirm these results, hub genes and TFs were verified in microarray and single-cell RNA sequencing (scRNA-seq) datasets. Results: Following WGCNA and limma analysis, 152 shared genes were identified. These genes were involved in interferon (IFN) response and cytokine-mediated signaling pathway. Moreover, we screened six shared genes, namely IFI44L, ISG15, IFIT1, USP18, RSAD2 and ITGB2, out of which three genes, namely IFI44L, ISG15 and ITGB2 were found to be highly expressed in both microarray and scRNA-seq datasets. IFN response and ITGB2 signaling pathway were identified as potentially relevant pathways. In addition, STAT1 and IRF7 were identified as common TFs in both diseases. Conclusion: This study revealed IFI44L, ISG15 and ITGB2 as the shared genes and identified STAT1 and IRF7 as the common TFs of SLE and pSS. Notably, the IFN response and ITGB2 signaling pathway played vital roles in both diseases. Our study revealed common pathogenetic characteristics of SLE and pSS. The particular roles of these pivotal genes and mutually overlapping pathways may provide a basis for further mechanistic research. [ABSTRACT FROM AUTHOR]

Background: COVID-19, a serious respiratory disease that has the potential to affect numerous organs, is a serious threat to the health of people around the world. The objective of this article is to investigate the potential biological targets and mechanisms by which SARS-CoV-2 affects benign prostatic hyperplasia (BPH) and related symptoms. Methods: We downloaded the COVID-19 datasets (GSE157103 and GSE166253) and the BPH datasets (GSE7307 and GSE132714) from the Gene Expression Omnibus (GEO) database. In GSE157103 and GSE7307, differentially expressed genes (DEGs) were found using the “Limma” package, and the intersection was utilized to obtain common DEGs. Further analyses followed, including those using Protein-Protein Interaction (PPI), Gene Ontology (GO) function enrichment analysis, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Potential hub genes were screened using three machine learning methods, and they were later verified using GSE132714 and GSE166253. The CIBERSORT analysis and the identification of transcription factors, miRNAs, and drugs as candidates were among the subsequent analyses. Results: We identified 97 common DEGs from GSE157103 and GSE7307. According to the GO and KEGG analyses, the primary gene enrichment pathways were immune-related pathways. Machine learning methods were used to identify five hub genes (BIRC5, DNAJC4, DTL, LILRB2, and NDC80). They had good diagnostic properties in the training sets and were validated in the validation sets. According to CIBERSORT analysis, hub genes were closely related to CD4 memory activated of T cells, T cells regulatory and NK cells activated. The top 10 drug candidates (lucanthone, phytoestrogens, etoposide, dasatinib, piroxicam, pyrvinium, rapamycin, niclosamide, genistein, and testosterone) will also be evaluated by the P value, which is expected to be helpful for the treatment of COVID-19-infected patients with BPH.Conclusion: Our findings reveal common signaling pathways, possible biological targets, and promising small molecule drugs for BPH and COVID-19. This is crucial to understand the potential common pathogenic and susceptibility pathways between them. [ABSTRACT FROM AUTHOR]

Background: There is still a lack of specific indicators to diagnose idiopathic pulmonary fibrosis (IPF). And the role of immune responses in IPF is elusive. In this study, we aimed to identify hub genes for diagnosing IPF and to explore the immune microenvironment in IPF. Methods: We identified differentially expressed genes (DEGs) between IPF and control lung samples using the GEO database. Combining LASSO regression and SVM-RFE machine learning algorithms, we identified hub genes. Their differential expression were further validated in bleomycin-induced pulmonary fibrosis model mice and a meta-GEO cohort consisting of five merged GEO datasets. Then, we used the hub genes to construct a diagnostic model. All GEO datasets met the inclusion criteria, and verification methods, including ROC curve analysis, calibration curve (CC) analysis, decision curve analysis (DCA) and clinical impact curve (CIC) analysis, were performed to validate the reliability of the model. Through the Cell Type Identification by Estimating Relative Subsets of RNA Transcripts algorithm (CIBERSORT), we analyzed the correlations between infiltrating immune cells and hub genes and the changes in diverse infiltrating immune cells in IPF. Results: A total of 412 DEGs were identified between IPF and healthy control samples, of which 283 were upregulated and 129 were downregulated. Through machine learning, three hub genes (ASPN, SFRP2, SLCO4A1) were screened. We confirmed their differential expression using pulmonary fibrosis model mice evaluated by qPCR, western blotting and immunofluorescence staining and analysis of the meta-GEO cohort. There was a strong correlation between the expression of the three hub genes and neutrophils. Then, we constructed a diagnostic model for diagnosing IPF. The areas under the curve were 1.000 and 0.962 for the training and validation cohorts, respectively. The analysis of other external validation cohorts, as well as the CC analysis, DCA, and CIC analysis, also demonstrated strong agreement. There was also a significant correlation between IPF and infiltrating immune cells. The frequencies of most infiltrating immune cells involved in activating adaptive immune responses were increased in IPF, and a majority of innate immune cells showed reduced frequencies. Conclusion: Our study demonstrated that three hub genes (ASPN, SFRP2, SLCO4A1) were associated with neutrophils, and the model constructed with these genes showed good diagnostic value in IPF. There was a significant correlation between IPF and infiltrating immune cells, indicating the potential role of immune regulation in the pathological process of IPF. [ABSTRACT FROM AUTHOR]

Background: Idiopathic Pulmonary Fibrosis (IPF) can be described as a debilitating lung disease that is characterized by the complex interactions between various immune cell types and signaling pathways. Chromatin-modifying enzymes are significantly involved in regulating gene expression during immune cell development, yet their role in IPF is not well understood. Methods: In this study, differential gene expression analysis and chromatin-modifying enzyme-related gene data were conducted to identify hub genes, common pathways, immune cell infiltration, and potential drug targets for IPF. Additionally, a murine model was employed for investigating the expression levels of candidate hub genes and determining the infiltration of different immune cells in IPF. Results: We identified 33 differentially expressed genes associated with chromatin-modifying enzymes. Enrichment analyses of these genes demonstrated a strong association with histone lysine demethylation, Sin3-type complexes, and protein demethylase activity. Protein-protein interaction network analysis further highlighted six hub genes, specifically KDM6B, KDM5A, SETD7, SUZ12, HDAC2, and CHD4. Notably, KDM6B expression was significantly increased in the lungs of bleomycin-induced pulmonary fibrosis mice, showing a positive correlation with fibronectin and α-SMA, two essential indicators of pulmonary fibrosis. Moreover, we established a diagnostic model for IPF focusing on KDM6B and we also identified 10 potential therapeutic drugs targeting KDM6B for IPF treatment. Conclusion: Our findings suggest that molecules related to chromatin-modifying enzymes, primarily KDM6B, play a critical role in the pathogenesis and progression of IPF. [ABSTRACT FROM AUTHOR]

Background: The occurrence and progression of hepatic fibrosis (HF) is accompanied by inflammatory damage. Immune genes play a pivotal role in fibrogenesis and inflammatory damage in HF by regulating immune cell infiltration. However, the immune mechanisms of HF are inadequately studied. Therefore, this research aims to identify the immune genes and biological pathway which involved in fibrosis formation and inflammatory damage in HF and explore immune target-based therapeutics for HF. Methods: The expression dataset GSE84044 of HF was downloaded from the GEO database. The crucial module genes for HF were screened according to weighted gene co-expression network analysis (WGCNA). The crucial module genes were mapped to immune-related genes obtained from the ImmPort database to obtain the hepatic fibrosis immune genes (HFIGs). In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed on HFIGs. Then, the protein-protein interaction (PPI) network was conducted on HFIGs and hub genes were identified from the PPI network. Moreover, immune infiltration analysis was performed to identified correlation between hub gene and immune cell infiltration. To verify the reliability of the GSE84044 expression profile data analysis, a rat model of CCl4-induced HF was established, followed by transcriptome sequencing and immunofluorescence analysis and quantitative reverse transcription (q-PCR) experiments were performed in HF rats and normal rat liver tissues. Finally, CMAP platform was used to explore immune target-based therapeutics for HF. Results: In the bioinformatics analysis of GSE84044 data, 98 HFIGs were screened. These genes were mainly involved in inflammation-related biological pathways such as NOD-like receptor signaling pathway, NF-kappa B signaling pathway, Toll-like receptor signaling pathway and PI3K-Akt signaling pathway. From the PPI network, 10 hub genes were identified, including CXCL8, IL18, CXCL10, CD8A, IL7, PTPRC, CCL5, IL7R, CXCL9 and CCL2. Immune infiltration analysis showed that immune cells like neutrophils, natural killer (NK) cells, macrophages M1 and macrophages M2 were significantly correlated with the hepatic fibrosis process and hub gene expression was significantly correlated with these immune cells. Notably, most of the biological pathways HFIGs riched and all the hub gene expression except CXCL8 were validated in subsequent transcriptome and qRCR experiments. Finally, 15 small molecule compounds with the potential to reverse the high expression of hub genes were screen out as potential therapeutic agents for HF. Conclusion: The immune genes CXCL8, IL18, CXCL10, CD8A, IL7, PTPRC, CCL5, IL7R, CXCL9 and CCL2 may play an essential role in the fibrosis formation and inflammatory damage in HF. The outcomes of this research provide a basis for the study of the immune mechanisms of HF and contribute to the diagnosis and prevention and treatment of HF in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Scleritis is a serious inflammatory eye disease that can lead to blindness. The etiology and pathogenesis of scleritis remain unclear, and increasing evidence indicates that some specific genes and proteins are involved. This study aimed to identify pivotal genes and drug targets for scleritis, thus providing new directions for the treatment of this disease. Methods: We screened candidate genes and proteins associated with scleritis by text-mining the PubMed database using Python, and assessed their functions by using the DAVID database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to identify the functional enrichment of these genes and proteins. Then, the hub genes were identified with CytoHubba and assessed by protein-protein interaction (PPI) network analysis. And the serum from patients with active scleritis and healthy subjects were used for the validation of hub genes. Finally, the DGIdb database was used to predict targeted drugs for the hub genes for treating scleritis. Results: A total of 56 genes and proteins were found to be linked to scleritis, and 65 significantly altered pathways were identified in the KEGG analysis (FDR < 0.05). Most of the top five pathways involved the categories "Rheumatoid arthritis," "Inflammatory bowel disease", "Type I diabetes mellitus," and "Graftversus-host disease". TNF and IL6 were considered to be the top 2 hub genes through CytoHubba. Based on our serum samples, hub genes are expressed at high levels in active scleritis. Five scleritis-targeting drugs were found among 88 identified drugs. Conclusions: This study provides key genes and drug targets related to scleritis through bioinformatics analysis. TNF and IL6 are considered key mediators and possible drug targets of scleritis. Five drug candidates may play an important role in the diagnosis and treatment of scleritis in the future, which is worthy of the further experimental and clinical study. [ABSTRACT FROM AUTHOR]

Background: Drug-induced acute kidney damage (DI-AKI) is a clinical phenomenon of rapid loss of kidney function over a brief period of time as a consequence of the using of medicines. The lack of a specialized treatment and the instability of traditional kidney injury markers to detect DI-AKI frequently result in the development of chronic kidney disease. Thus, it is crucial to continue screening for DI-AKI hub genes and specific biomarkers. Methods: Differentially expressed genes (DEGs) of group iohexol, cisplatin, and vancomycin's were analyzed using Limma package, and the intersection was calculated. DEGs were then put into String database to create a network of protein-protein interactions (PPI). Ten algorithms are used in the Cytohubba plugin to find the common hub genes. Three DI-AKI models' hub gene expression was verified in vivo and in vitro using PCR and western blot. To investigate the hub gene's potential as a biomarker, protein levels of mouse serum and urine were measured by ELISA kits. The UUO, IRI and aristolochic acid I-induced nephrotoxicity (AAN) datasets in the GEO database were utilized for external data verification by WGCNA and Limma package. Finally, the Elisa kit was used to identify DI-AKI patient samples. Results: 95 up-regulated common DEGs and 32 down-regulated common DEGs were obtained using Limma package. A PPI network with 84 nodes and 24 edges was built with confidence >0.4. Four hub genes were obtained by Algorithms of Cytohubba plugin, including TLR4, AOC3, IRF4 and TNFAIP6. Then, we discovered that the protein and mRNA levels of four hub genes were significantly changed in the DI-AKI model in vivo and in vitro. External data validation revealed that only the AAN model, which also belonged to DI-AKI model, had significant difference in these hub genes, whereas IRI and UUO did not. Finally, we found that plasma TLR4 levels were higher in patients with DI-AKI, especially in vancomycin-induced AKI. Conclusion: The immune system and inflammation are key factors in DI-AKI. We discovered the immunological and inflammatory-related genes TLR4, AOC3, IRF4, and TNFAIP6, which may be promising specific biomarkers and essential hub genes for the prevention and identification of DI-AKI. [ABSTRACT FROM AUTHOR]

Background: IgAV, the most common systemic vasculitis in childhood, is an immunoglobulin A-associated immune complex-mediated disease and its underlying molecular mechanisms are not fully understood. This study attempted to identify differentially expressed genes (DEGs) and find dysregulated immune cell types in IgAV to find the underlying pathogenesis for IgAVN. Methods: GSE102114 datasets were obtained from the Gene Expression Omnibus (GEO) database to identify DEGs. Then, the protein-protein interaction (PPI) network of the DEGs was constructed using the STRING database. And key hub genes were identified by cytoHubba plug-in, performed functional enrichment analyses and followed by verification using PCR based on patient samples. Finally, the abundance of 24 immune cells were detected by Immune Cell Abundance Identifier (ImmuCellAI) to estimate the proportions and dysregulation of immune cell types within IgAVN. Result: A total of 4200 DEGs were screened in IgAVN patients compared to Health Donor, including 2004 upregulated and 2196 downregulated genes. Of the top 10 hub genes from PPI network, STAT1, TLR4, PTEN, UBB, HSPA8, ATP5B, UBA52, and CDC42 were verified significantly upregulated in more patients. Enrichment analyses indicated that hub genes were primarily enriched in Tolllike receptor (TLR) signaling pathway, nucleotide oligomerization domain (NOD)-like receptor signaling pathway, and Th17 signaling pathways. Moreover, we found a diversity of immune cells in IgAVN, consisting mainly of T cells. Finally, this study suggests that the overdifferentiation of Th2 cells, Th17 cells and Tfh cells may be involved in the occurrence and development of IgAVN. Conclusion: We screened out the key genes, pathways and maladjusted immune cells and associated with the pathogenesis of IgAVN. The unique characteristics of IgAV-infiltrating immune cell subsets were confirmed, providing new insights for future molecular targeted therapy and a direction for immunological research on IgAVN. [ABSTRACT FROM AUTHOR]

Background: Hepatic ischemia-reperfusion (I/R) injury is an unavoidable pathological process that occurs after liver transplantation. However, the immune-related molecular mechanism still remains unclear. This study aims to further explore the biological mechanisms of immune-related genes in hepatic I/ R injury. Methods: Gene microarray data was downloaded from the Gene Expression Omnibus (GEO) expression profile database and the differentially expressed genes (DEGs) were taken for intersection. After identifying common DEGs, functional annotation, protein-protein interaction (PPI) network, and modular construction were performed. The immune-related hub genes were obtained, which their upstream transcription factors and non-RNAs were predicted. Validation of the hub genes expression and immune infiltration were performed in a mouse model of hepatic I/R injury. Results: A total of 71 common DEGs were obtained from three datasets (GSE12720, GSE14951, GSE15480). The GO and KEGG enrichment analysis results indicated that immune and inflammatory response played an important role in hepatic I/R injury. Finally, 9 immune-related hub genes were identified by intersecting cytoHubba with immune-related genes, including SOCS3, JUND, CCL4, NFKBIA, CXCL8, ICAM1, IRF1, TNFAIP3, and JUN. Conclusion: Our study revealed the importance of the immune and inflammatory response in I/R injury following liver transplantation and provided new insights into the therapeutic of hepatic I/R injury. [ABSTRACT FROM AUTHOR]

BackgroundHepatic ischemia-reperfusion (I/R) injury is an unavoidable pathological process that occurs after liver transplantation. However, the immune-related molecular mechanism still remains unclear. This study aims to further explore the biological mechanisms of immune-related genes in hepatic I/R injury.MethodsGene microarray data was downloaded from the Gene Expression Omnibus (GEO) expression profile database and the differentially expressed genes (DEGs) were taken for intersection. After identifying common DEGs, functional annotation, protein-protein interaction (PPI) network, and modular construction were performed. The immune-related hub genes were obtained, which their upstream transcription factors and non-RNAs were predicted. Validation of the hub genes expression and immune infiltration were performed in a mouse model of hepatic I/R injury.ResultsA total of 71 common DEGs were obtained from three datasets (GSE12720, GSE14951, GSE15480). The GO and KEGG enrichment analysis results indicated that immune and inflammatory response played an important role in hepatic I/R injury. Finally, 9 immune-related hub genes were identified by intersecting cytoHubba with immune-related genes, including SOCS3, JUND, CCL4, NFKBIA, CXCL8, ICAM1, IRF1, TNFAIP3, and JUN.ConclusionOur study revealed the importance of the immune and inflammatory response in I/R injury following liver transplantation and provided new insights into the therapeutic of hepatic I/R injury.

Background: Coronavirus disease 2019 (COVID-19) and inflammatory bowel disease (IBD) are both caused by a disordered immune response and have direct and profound impacts on health care services. In this study, we implemented transcriptomic and single-cell analysis to detect common molecular and cellular intersections between COVID-19 and IBD that help understand the linkage of COVID-19 to the IBD patients. Methods: Four RNA-sequencing datasets (GSE147507, GSE126124, GSE9686 and GSE36807) from Gene Expression Omnibus (GEO) database are extracted to detect mutual differentially expressed genes (DEGs) for IBD patients with the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to find shared pathways, candidate drugs, hub genes and regulatory networks. Two single-cell RNA sequencing (scRNA-eq) datasets (GSE150728, PRJCA003980) are used to analyze the immune characteristics of hub genes and the proportion of immune cell types, so as to find common immune responses between COVID-19 and IBD. Results: A total of 121 common DEGs were identified among four RNA-seq datasets, and were all involved in the functional enrichment analysis related to inflammation and immune response. Transcription factors-DEGs interactions, miRNAs-DEGs coregulatory networks, and protein-drug interactions were identified based on these datasets. Protein-protein interactions (PPIs) was built and 59 hub genes were identified. Moreover, scRNA-seq of peripheral blood monocyte cells (PBMCs) from COVID-19 patients revealed a significant increase in the proportion of CD14+ monocytes, in which 38 of 59 hub genes were highly enriched. These genes, encoding inflammatory cytokines, were also highly expressed in inflammatory macrophages (IMacrophage) of intestinal tissues of IBD patients. Conclusions: We conclude that COVID-19 may promote the progression of IBD through cytokine storms. The candidate drugs and DEGs-regulated networks may suggest effective therapeutic methods for both COVID-19 and IBD. [ABSTRACT FROM AUTHOR]

Background: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a common sinonasal inflammatory disorder with high heterogeneity. Increasing evidence have indicated that the infiltration of macrophages especially M2 macrophages play pivotal roles in the pathogenesis of CRSwNP, but the underlying mechanisms remain undetermined. This study sought to identify potential biomarkers related to M2 macrophages in CRSwNP. Methods: The expression datasets of GSE136825 and GSE179265 were download from Gene Expression Omnibus (GEO) database and merged. Then, CIBERSORT and weighted gene co-expression network analysis (WGCNA) algorithms were applied to identify M2 macrophage-related gene modules. Thereafter, differentially expressed genes (DEGs) related to M2 macrophages were selected to perform functional enrichment analyses. A protein-protein interaction (PPI) network was built to identify hub genes and quantitative real-time reverse transcriptions PCR was used to verify the bioinformatics results. Results: A total of 92 DEGs associated with M2 macrophages were identified for further analysis. The results of Gene ontology (GO) and Kyoto Encyclopedia of genes and genomes (KEGG) analyses illustrated that M2 macrophageassociated DEGs primarily enriched in immune responses and extracellular matrix structure. PPI network analysis identified 18 hub genes related to M2 macrophages that might be pivotal in the pathogenesis of CRSwNP. After verification, AIF1, C1QA, C1QB, C3AR1, CCR1, CD163, CD4, CD53, CD86, CSF1R, CYBB, FCER1G, FCGR3A, IL10RA, ITGB2, LAPTM5, PLEK, TYROBP were identified as potential M2 macrophage-related biomarkers for CRSwNP. Conclusion: These findings yield new insights into the hub genes and mechanisms related to M2 macrophages in the pathogenesis of CRSwNP. Further studies of these hub genes would help better understand the disease progression and identify potential treatment targets. [ABSTRACT FROM AUTHOR]

Background: The severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2), which has resulted in the most devastating pandemic in modern history. Human immunodeficiency virus (HIV) destroys immune system cells and weakens the body’s ability to resist daily infections and diseases. Furthermore, HIV-infected individuals had double COVID-19 mortality risk and experienced worse COVID-related outcomes. However, the existing research still lacks the understanding of the molecular mechanism underlying crosstalk between COVID-19 and HIV. The aim of our work was to illustrate blood transcriptome crosstalk between COVID-19 and HIV and to provide potential drugs that might be useful for the treatment of HIV-infected COVID-19 patients. Methods: COVID-19 datasets (GSE171110 and GSE152418) were downloaded from Gene Expression Omnibus (GEO) database, including 54 whole-blood samples and 33 peripheral blood mononuclear cells samples, respectively. HIV dataset (GSE37250) was also obtained from GEO database, containing 537 whole-blood samples. Next, the “Deseq2” package was used to identify differentially expressed genes (DEGs) between COVID-19 datasets (GSE171110 and GSE152418) and the “limma” package was utilized to identify DEGs between HIV dataset (GSE37250). By intersecting these two DEG sets, we generated common DEGs for further analysis, containing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis, protein-protein interaction (PPI) analysis, transcription factor (TF) candidate identification, microRNAs (miRNAs) candidate identification and drug candidate identification. Results: In this study, a total of 3213 DEGs were identified from the merged COVID-19 dataset (GSE171110 and GSE152418), and 1718 DEGs were obtained from GSE37250 dataset. Then, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets. GO and KEGG enrichment analysis indicated that common DEGs were mainly gathered in chromosome-related and cell cycle-related signal pathways. Top ten hub genes (CCNA2, CCNB1, CDC20, TOP2A, AURKB, PLK1, BUB1B, KIF11, DLGAP5, RRM2) were ranked according to their scores, which were screened out using degree algorithm on the basis of common DEGs. Moreover, top ten drug candidates (LUCANTHONE, Dasatinib, etoposide, Enterolactone, troglitazone, testosterone, estradiol, calcitriol, resveratrol, tetradioxin) ranked by their P values were screened out, which maybe be beneficial for the treatment of HIV-infected COVID-19 patients. Conclusion: In this study, we provide potential molecular targets, signaling pathways, small molecular compounds, and promising biomarkers that contribute to worse COVID-19 prognosis in patients with HIV, which might contribute to precise diagnosis and treatment for HIV-infected COVID-19 patients. [ABSTRACT FROM AUTHOR]

Background: Systemic lupus erythematosus (SLE) is one of the most prevalent systemic autoimmune diseases, and metabolic syndrome (MetS) is the most common metabolic disorder that contains hypertension, dyslipidemia, and obesity. Despite clinical evidence suggested potential associations between SLE and MetS, the underlying pathogenesis is yet unclear. Methods: The microarray data sets of SLE and MetS were obtained from the Gene Expression Omnibus (GEO) database. To identify the shared genes between SLE and MetS, the Differentially Expressed Genes (DEGs) analysis and the weighted gene co-expression network analysis (WGCNA) were conducted. Then, the GO and KEGG analyses were performed, and the protein-protein interaction (PPI) network was constructed. Next, Random Forest and LASSO algorithms were used to screen shared hub genes, and a diagnostic model was built using the machine learning technique XG-Boost. Subsequently, CIBERSORT and GSVA were used to estimate the correlation between shared hub genes and immune infiltration as well as metabolic pathways. Finally, the significant hub genes were verified using single-cell RNA sequencing (scRNA-seq) data. Results: Using limma and WGCNA, we identified 153 shared feature genes, which were enriched in immune- and metabolic-related pathways. Further, 20 shared hub genes were screened and successfully used to build a prognostic model. Those shared hub genes were associated with immunological and metabolic processes in peripheral blood. The scRNA-seq results verified that TNFSF13B and OAS1, possessing the highest diagnostic efficacy, were mainly expressed by monocytes. Additionally, they showed positive correlations with the pathways for the metabolism of xenobiotics and cholesterol, both of which were proven to be active in this comorbidity, and shown to be concentrated in monocytes. Conclusion: This study identified shared hub genes and constructed an effective diagnostic model in SLE and MetS. TNFSF13B and OAS1 had a positive correlation with cholesterol and xenobiotic metabolism. Both of these two biomarkers and metabolic pathways were potentially linked to monocytes, which provides novel insights into the pathogenesis and combined therapy of SLE comorbidity with MetS. [ABSTRACT FROM AUTHOR]

Background: Although increasing evidence has reported an increased risk of atherosclerosis (AS) in rheumatoid arthritis (RA), the communal molecular mechanism of this phenomenon is still far from being fully elucidated. Hence, this article aimed to explore the pathogenesis of RA complicated with AS. Methods: Based on the strict inclusion/exclusion criteria, four gene datasets were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the communal differentially expressed genes (DEGs) and hub genes, comprehensive bioinformatics analysis, including functional annotation, coexpression analysis, expression validation, drug-gene prediction, and TFmRNA-miRNA regulatory network construction, was conducted. Moreover, the immune infiltration of RA and AS was analyzed and compared based on the CIBERSORT algorithm, and the correlation between hub genes and infiltrating immune cells was evaluated in RA and AS respectively. Results: A total of 54 upregulated and 12 downregulated communal DEGs were screened between GSE100927 and GSE55457, and functional analysis of these genes indicated that the potential pathogenesis lies in immune terms. After the protein-protein interaction (PPI) network construction, a total of six hub genes (CCR5, CCR7, IL7R, PTPRC, CD2, and CD3D) were determined as hub genes, and the subsequent comprehensive bioinformatics analysis of the hub genes re-emphasized the importance of the immune system in RA and AS. Additionally, three overlapping infiltrating immune cells were found between RA and AS based on the CIBERSORT algorithm, including upregulated memory B cells, follicular helper T cells and gdT cells. Conclusions: Our study uncover the communal central genes and commonness in immune infiltration between RA and AS, and the analysis of six hub genes and three immune cells profile might provide new insights into potential pathogenesis therapeutic direction of RA complicated with AS. [ABSTRACT FROM AUTHOR]

Background: Although increasing evidence has supported that Hirschsprung disease (HSCR) is the risk factor for children developing Crohn’s disease (CD), the common mechanism of its co-occurrence remains unknown. The purpose of this study is to further explore the underlying mechanism and biomarkers for the co-occurrence of HSCR and CD. Methods: The Gene Expression Omnibus (GEO) database was used to obtain gene expression profiles for CD (GSE95095) and HSCR (GSE98502). Following the identification of the shared differentially expressed genes (DEGs) of CD and HSCR, functional annotation, protein–protein interaction (PPI) network creation, and module assembly were performed to discover hub genes. RTqPCR was performed to validate the expression of the hub genes in HSCR samples. The receiver operating characteristic (ROC) curve was utilized to assess the accuracy of the hub genes as biomarkers in predicting CD in both the training dataset and test dataset. Results: A total of 103 common DEGs (50 downregulated genes and 53 upregulated genes) were chosen for further investigation. The importance of chemokines and cytokines in these two disorders is highlighted by functional analysis. MCODE plug identified three important modules, which functionally enriched the immune system process. Finally, nine hub genes were identified using cytoHubba, including IL1B, IL10, CXCL10, ICAM1, EGR1, FCGR3A, S100A12, S100A9, and FPR1. The nine hub genes were mainly enriched in immune- and inflammation-related pathways. External data profiles and RTqPCR confirmed the expression of the nine hub genes in HSCR and CD. ROC analysis revealed that the nine hub genes had a strong diagnostic value. Conclusion: Our study reveals the common pathogenesis of HSCR and CD. These hub genes and diagnostic models may provide novel insight for the diagnosis and treatment of HSCR complicated with CD. [ABSTRACT FROM AUTHOR]

A total of 138 cDEGs were screened from mediastinal lymph nodes and peripheral whole blood. Among them, 6 hub cDEGs including CTSS, CYBB, FPR2, MNDA, TLR1 and TLR8 with elevated degree and betweenness levels were illustrated in protein-protein interaction network. In comparison to healthy controls, CTSS (1.61 vs. 1.05), CYBB (1.68 vs. 1.07), FPR2 (2.77 vs. 0.96), MNDA (2.14 vs. 1.23), TLR1 (1.56 vs. 1.09), and TLR8 (2.14 vs. 0.98) displayed notably elevated expression levels within pulmonary sarcoidosis PBMC samples (P < 0.0001 for FPR2 and P < 0.05 for others), echoing with prior mRNA microarray findings. The most significant functional pathways were immune response, inflammatory response, plasma membrane and extracellular exosome, with 6 hub cDEGs distributing along these pathways. CTSS, CYBB, FPR2, MNDA, TLR1, and TLR8 could be conducive to improving the diagnostic process and understanding the underlying mechanisms of pulmonary sarcoidosis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yao, Min, Zhao, Chen, Weng and Zhou.)

Objective: Patients with rheumatoid arthritis (RA) have an increased risk of developing pulp and periapical disease (PAP), but the causal relationship and shared genetic factors between these conditions have not been explored. This study aimed to investigate the bidirectional causal relationship between RA and PAP and to analyze shared genes and pathogenic pathways., Methods: We utilized GWAS data from the IEU Open GWAS Project and employed five Mendelian randomization methods (MR Egger, weighted median, inverse variance weighted, simple mode, and weighted mode) to investigate the bidirectional causal relationship between RA and PAP. Transcriptome data for RA and irreversible pulpitis (IRP) were obtained from the GEO database. Hub genes were identified through differential analysis, CytoHubba, machine learning (ML), and other methods. The immune infiltration of both diseases was analyzed using the ssGSEA method. Finally, we constructed a regulatory network for miRNAs, transcription factors, chemicals, diseases, and RNA-binding proteins based on the identified hub genes., Results: RA was significantly associated with an increased risk of PAP (OR = 1.1284, 95% CI 1.0674-1.1929, p < 0.001). However, there was insufficient evidence to support the hypothesis that PAP increased the risk of RA. Integrating datasets and differential analysis identified 84 shared genes primarily involved in immune and inflammatory pathways, including the IL-17 signaling pathway, Th17 cell differentiation, and TNF signaling pathway. Using CytoHubba and three ML methods, we identified three hub genes (HLA-DRA, ITGAX, and PTPRC) that are significantly correlated and valuable for diagnosing RA and IRP. We then constructed a comprehensive regulatory network using the miRDB, miRWalk, ChipBase, hTFtarget, CTD, MalaCards, DisGeNET, and ENCORI databases., Conclusion: RA may increase the risk of PAP. The three key genes, HLA-DRA, ITGAX, and PTPRC, have significant diagnostic value for both RA and IRP., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wu, Wang and Qiu.)

Patients with chronic rhinosinusitis with nasal polyps (CRSwNP) and asthma comorbidity (ACRSwNP) present severe symptoms and are more likely to relapse. However, the pathogenesis of ACRSwNP is not fully understood. The aim of this study was to explore the underlying pathogenesis of ACRSwNP using bioinformatics approaches. ACRSwNP-related differentially expressed genes (DEGs) were identified by the analysis of the GSE23552 dataset. The clusterProfiler R package was used to carry out functional and pathway enrichment analysis. A protein-protein interaction (PPI) network was built using the STRING database to explore key genes in the pathogenesis of ACRSwNP. The bioinformatics analysis results were verified through qRTPCR. The Connectivity Map (CMap) database was used to predict potential drugs for the treatment of ACRSwNP. A total of 36 DEGs were identified, which were mainly enriched in terms of regulation of immune response and detection sensory perception of taste. Thirteen hub genes including AZGP1, AQP9, GAPT, PIP, and PRR4 were identified as potential hub genes in ACRSwNP from the PPI network. Analysis of the GSE41861 dataset showed that upregulation of CST1 in nasal mucosa was associated with asthma. qRT-PCR detection confirmed the bioinformatics analysis results. Tacrolimus and spaglumic acid were identified as potential drugs for the treatment of ACRSwNP from the CMap database. The findings of this study provide insights into the pathogenesis of ACRSwNP and may provide a basis for the discovery of effective therapeutic modalities for ACRSwNP. [ABSTRACT FROM AUTHOR]

Background: IgA nephropathy (IgAN) is an autoimmune disease that affects people of any age and is an important cause of end-stage renal disease. However, the pathogenesis and pathophysiology of IgAN is not clear. This article aimed to explore the immune-mediated inflammation and genetic mechanisms in IgAN. Methods: The transcriptome sequencing data of IgAN glomeruli in the Gene Expression Omnibus database were downloaded. Single-sample gene set enrichment analysis was used to estimate the immune microenvironment of the merged microarray data and GSE141295. IgAN samples were divided into two clusters by cluster analysis. "limma" and "DEseq2" package in R were used to identify differentially expressed genes (DEGs). The weighted gene coexpression network analysis (WGCNA) was used to identify the co-expression modules related to inflammation in IgAN. R software package "clusterProfiler" was used for enrichment analysis, whereas Short Time-Series Expression Miner (STEM) analysis was used to identify the trend of gene expression. Machinelearn (ML) was performed using the shiny app. Finally, Drug Signatures Database (DSigDB) was used to identify potential molecules for treating IgAN. Results: The infiltration of macrophages in IgAN glomeruli was increased, whereas CD4+ T cells, especially inducedregulatory T cells (iTregs) were decreased. A total of 1,104 common DEGs were identified from the merged data and GSE141295. Brown module was identified to have the highest inflammatory correlation with IgAN using WGCNA, and 15 hub genes were screened from this module. Among these 15 hub genes, 14 increased with the severity of IgAN inflammation based on STEM analysis. Neural network (nnet) is considered as the best model to predict the severity of IgAN. Fucose identified from DSigDB has a potential biological activity to treat IgAN. Conclusion: The increase of macrophages and the decrease of iTregs in glomeruli represent the immune-mediated inflammation of IgAN, and fucose may be a potential therapeutic molecule against IgAN because it affects genes involved in the severe inflammation of IgAN. [ABSTRACT FROM AUTHOR]

Background: Accumulating evidence has revealed that the prevalence of Coronavirus 2019 (COVID-19) was significantly higher in patients with primary Sjogren's syndrome (pSS) compared to the general population. However, the mechanism remains incompletely elucidated. This study aimed to further investigate the molecular mechanisms underlying the development of this complication. Methods: The gene expression profiles of COVID-19 (GSE157103) and pSS (GSE40611) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) for pSS and COVID-19, functional annotation, protein-protein interaction (PPI) network, module construction and hub gene identification were performed. Finally, we constructed transcription factor (TF)-gene regulatory network and TF-miRNA regulatory network for hub genes. Results: A total of 40 common DEGs were selected for subsequent analyses. Functional analyses showed that cellular components and metabolic pathways collectively participated in the development and progression of pSS and COVID-19. Finally, 12 significant hub genes were identified using the cytoHubba plugin, including CMPK2, TYMS, RRM2, HERC5, IFI44L, IFI44, IFIT2, IFIT1, IFIT3, MX1, CDCA2 and TOP2A, which had preferable values as diagnostic markers for COVID-19 and pSS. Conclusions: Our study reveals common pathogenesis of pSS and COVID-19. These common pathways and pivotal genes may provide new ideas for further mechanistic studies. [ABSTRACT FROM AUTHOR]

Background: Accumulating evidence supports the predisposition of systemic lupus erythematosus (SLE) to atherosclerosis (AS). However, the common pathogenesis of these two diseases remains unclear. This study aimed to explore the mechanisms of SLE complicated by AS. Methods: Gene expression profiles of SLE (GSE50772) and AS (GSE100927) were downloaded from the Gene Expression Omnibus. We analyzed differentially expressed genes (DEGs) of SLE and AS and performed enrichment analyses separately. After analyzing the common DEGs (CDEGs), we performed functional enrichment analysis, protein-protein interaction (PPI) network analysis, and hub genes (HGs) identification of CDEGs. Then, we performed a co-expression analysis of HGs and verified their expression and diagnostic value. We further explored immune cell infiltration and analyzed the correlation between HGs and infiltrating immune cells (IICs). Finally, we verified the reliability of the screening pathway. Results: We obtained 530 DEGs from the GSE50772 dataset and 448 DEGs from the GSE100927 dataset. The results of the enrichment analysis showed that there were many similar immune- and inflammation-related processes between the two diseases. We analyzed 26 CDEGs (two downregulated genes and 24 upregulated genes) and enrichment analysis highlighted the important role of the IL-17 signaling pathway. We identified five HGs (CCR1, CD163, IL1RN, MMP9, and SIGLEC1) using the CytoHubba plugin and HGs validation showed that the five HGs screened were reliable. Co-expression networks showed that five HGs can affect mononuclear cell migration. Immune cell infiltration analysis indicated monocytes in SLE and M0 macrophages in AS accounted for a high proportion of all IICs, and the difference in infiltration was obvious. We also found a significant positive correlation between CCR1, CD163, IL1RN, and MMP9 and monocytes in SLE, and a significant positive correlation between CCR1, IL1RN, MMP9, and SIGLEC1 and M0 macrophages in AS. Pathway validation also demonstrated that the IL-17 signaling pathway was a key pathway for the differentiation of monocytes into macrophages. Conclusions: The five HGs may promote the differentiation of monocytes into macrophages by influencing the IL-17 signaling pathway, leading to SLE complicated by AS. Our study provides insights into the mechanisms of SLE complicated by AS. [ABSTRACT FROM AUTHOR]

Sertoli cell-only syndrome (SCOS) is the most severe and common pathological type of non-obstructive azoospermia. The etiology of SCOS remains largely unknown to date despite a handful of studies reported in this area. According to the gene expression of testicular tissue samples in six datasets from the Gene Expression Omnibus, we detected 1441 differentially expressed genes (DEGs) between SCOS and obstructive azoospermia (OA) testicular tissue samples. Enriched GO terms and KEGG pathways for the downregulated genes included various terms and pathways related to cell cycle and reproduction, while the enrichment for the upregulated genes yielded many inflammation-related terms and pathways. In accordance with the protein-protein interaction (PPI) network, all genes in the most critical module belonged to the downregulated DEGs, and we obtained nine hub genes, including CCNB1, AURKA, CCNA2, BIRC5, TYMS, UBE2C, CDC20, TOP2A, and OIP5. Among these hub genes, six were also found in the most significant SCOS-specific module obtained from consensus module analysis. In addition, most of SCOS-specific modules did not have a consensus counterpart. Based on the downregulated genes, transcription factors (TFs) and kinases within the upstream regulatory network were predicted. Then, we compared the difference in infiltrating levels of immune cells between OA and SCOS samples and found a significantly higher degree of infiltration for most immune cells in SCOS than OA samples. Moreover, CD56bright natural killer cell was significantly associated with six hub genes. Enriched hallmark pathways in SCOS had remarkably more upregulated pathways than the downregulated ones. Collectively, we detected DEGs, significant modules, hub genes, upstream TFs and kinases, enriched downstream pathways, and infiltrated immune cells that might be specifically implicated in the pathogenesis of SCOS. These findings provide new insights into the pathogenesis of SCOS and fuel future advances in its theranostics. [ABSTRACT FROM AUTHOR]

Purpose: To identify biomarkers associated with CD8+ T cells in coronary artery disease (CAD) and initially explore their potential role in the tumor immune microenvironment. Materials and Methods: CAD-related datasets GSE12288, GSE34198, and GSE66360, were downloaded from the GEO database. First, GSVA was performed based on the GSE12288 dataset. Then WGCNA analysis was performed to identify the most relevant module and candidate hub gene for CD8+ T cells, followed by GO and KEGG analysis of this module. Secondly, the relationship between candidate hub genes and CD8+ T cells was verified using GSE34198 and GSE66360, which led to the identification of hub genes. The relationship of hub genes with CD8+ T cells in cancer was analyzed using the TIMER database. Methylation analysis of hub genes was performed using the DiseaseMeth database. CAD, pan-cancer, pan-cell lines, and pan-normal tissues, correlations between hub genes. In addition, potential drugs and TFs associated with hub genes were predicted, and the ceRNA network was constructed. Finally, GSEA was performed separately for hub genes. Results: CAD was shown to be associated with immune response by GSVA analysis. WGCNA identified the blue module as most related to CD8+ T cells and identified nine candidate hub genes. The relevance of CAD to immunity was further confirmed by GO and KEGG analysis of the module. Two additional datasets validated and identified three hub genes (FBXO7, RAD23A, and MKRN1) that significantly correlated with CD8+ T cells. In addition, we found that hub genes were positively associated with CD8+ T cells in TGCT, THCA, and KICH cancers by our analysis. Moreover, the hub gene was differentially methylated. We also analyzed the correlation between hub genes in CAD, different cancers, different cell lines, and different normal tissues. The results of all the analyses showed a positive correlation between them. Finally, we successfully constructed hub gene-associated TF-gene and ceRNA networks and predicted 11 drugs associated with hub genes. GSEA suggests that hub genes are related to multiple immune response processes. Conclusion: FBXO7, RAD23A, and MKRN1 are significantly associated with CD8+ T cells in CAD and multiple cancers and may act through immune responses in CAD and cancer. [ABSTRACT FROM AUTHOR]

Sertoli cell-only syndrome (SCOS) is the most severe and common pathological type of non-obstructive azoospermia. The etiology of SCOS remains largely unknown to date despite a handful of studies reported in this area. According to the gene expression of testicular tissue samples in six datasets from the Gene Expression Omnibus, we detected 1441 differentially expressed genes (DEGs) between SCOS and obstructive azoospermia (OA) testicular tissue samples. Enriched GO terms and KEGG pathways for the downregulated genes included various terms and pathways related to cell cycle and reproduction, while the enrichment for the upregulated genes yielded many inflammation-related terms and pathways. In accordance with the protein-protein interaction (PPI) network, all genes in the most critical module belonged to the downregulated DEGs, and we obtained nine hub genes, including CCNB1, AURKA, CCNA2, BIRC5, TYMS, UBE2C, CDC20, TOP2A, and OIP5. Among these hub genes, six were also found in the most significant SCOS-specific module obtained from consensus module analysis. In addition, most of SCOS-specific modules did not have a consensus counterpart. Based on the downregulated genes, transcription factors (TFs) and kinases within the upstream regulatory network were predicted. Then, we compared the difference in infiltrating levels of immune cells between OA and SCOS samples and found a significantly higher degree of infiltration for most immune cells in SCOS than OA samples. Moreover, CD56bright natural killer cell was significantly associated with six hub genes. Enriched hallmark pathways in SCOS had remarkably more upregulated pathways than the downregulated ones. Collectively, we detected DEGs, significant modules, hub genes, upstream TFs and kinases, enriched downstream pathways, and infiltrated immune cells that might be specifically implicated in the pathogenesis of SCOS. These findings provide new insights into the pathogenesis of SCOS and fuel future advances in its theranostics.

PurposeTo identify biomarkers associated with CD8+ T cells in coronary artery disease (CAD) and initially explore their potential role in the tumor immune microenvironment.Materials and MethodsCAD-related datasets GSE12288, GSE34198, and GSE66360, were downloaded from the GEO database. First, GSVA was performed based on the GSE12288 dataset. Then WGCNA analysis was performed to identify the most relevant module and candidate hub gene for CD8+ T cells, followed by GO and KEGG analysis of this module. Secondly, the relationship between candidate hub genes and CD8+ T cells was verified using GSE34198 and GSE66360, which led to the identification of hub genes. The relationship of hub genes with CD8+ T cells in cancer was analyzed using the TIMER database. Methylation analysis of hub genes was performed using the DiseaseMeth database. CAD, pan-cancer, pan-cell lines, and pan-normal tissues, correlations between hub genes. In addition, potential drugs and TFs associated with hub genes were predicted, and the ceRNA network was constructed. Finally, GSEA was performed separately for hub genes.ResultsCAD was shown to be associated with immune response by GSVA analysis. WGCNA identified the blue module as most related to CD8+ T cells and identified nine candidate hub genes. The relevance of CAD to immunity was further confirmed by GO and KEGG analysis of the module. Two additional datasets validated and identified three hub genes (FBXO7, RAD23A, and MKRN1) that significantly correlated with CD8+ T cells. In addition, we found that hub genes were positively associated with CD8+ T cells in TGCT, THCA, and KICH cancers by our analysis. Moreover, the hub gene was differentially methylated. We also analyzed the correlation between hub genes in CAD, different cancers, different cell lines, and different normal tissues. The results of all the analyses showed a positive correlation between them. Finally, we successfully constructed hub gene-associated TF-gene and ceRNA networks and predicted 11 drugs associated with hub genes. GSEA suggests that hub genes are related to multiple immune response processes.ConclusionFBXO7, RAD23A, and MKRN1 are significantly associated with CD8+ T cells in CAD and multiple cancers and may act through immune responses in CAD and cancer.

Monocytes are pivotal immune cells in eliciting specific immune responses and can exert a significant impact on the progression, prognosis, and immunotherapy of intracranial aneurysms (IAs). The objective of this study was to identify monocyte/macrophage (Mo/MΦ)-associated gene signatures to elucidate their correlation with the pathogenesis and immune microenvironment of IAs, thereby offering potential avenues for targeted therapy against IAs. Single-cell RNA-sequencing (scRNA-seq) data of IAs were acquired from the Gene Expression Synthesis (GEO) database. The significant infiltration of monocyte subsets in the parietal tissue of IAs was identified using single-cell RNA sequencing and high-dimensional weighted gene co-expression network analysis (hdWGCNA). The integration of six machine learning algorithms identified four crucial genes linked to these Mo/MΦ. Subsequently, we developed a multilayer perceptron (MLP) neural model for the diagnosis of IAs (independent external test AUC=1.0, sensitivity =100%, specificity =100%). Furthermore, we employed the CIBERSORT method and MCP counter to establish the correlation between monocyte characteristics and immune cell infiltration as well as patient heterogeneity. Our findings offer valuable insights into the molecular characterization of monocyte infiltration in IAs, which plays a pivotal role in shaping the immune microenvironment of IAs. Recognizing this characterization is crucial for comprehending the limitations associated with targeted therapies for IAs. Ultimately, the results were verified by real-time fluorescence quantitative PCR and Immunohistochemistry., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Xu, Guo, Wang, Sun, Wang, Li, Cui, Zhang and Feng.)

Background: Sleep disorders (SD) are known to have a profound impact on human health and quality of life although their exact pathogenic mechanisms remain poorly understood., Methods: The study first accessed SD datasets from the GEO and identified DEGs. These DEGs were then subjected to gene set enrichment analysis. Several advanced techniques, including the RF, SVM-RFE, PPI networks, and LASSO methodologies, were utilized to identify hub genes closely associated with SD. Additionally, the ssGSEA approach was employed to analyze immune cell infiltration and functional gene set scores in SD. DEGs were also scrutinized in relation to miRNA, and the DGIdb database was used to explore potential pharmacological treatments for SD. Furthermore, in an SD murine model, the expression levels of these hub genes were confirmed through RT-qPCR and Western Blot analyses., Results: The findings of the study indicate that DEGs are significantly enriched in functions and pathways related to immune cell activity, stress response, and neural system regulation. The analysis of immunoinfiltration demonstrated a marked elevation in the levels of Activated CD4+ T cells and CD8+ T cells in the SD cohort, accompanied by a notable rise in Central memory CD4 T cells, Central memory CD8 T cells, and Natural killer T cells. Using machine learning algorithms, the study also identified hub genes closely associated with SD, including IPO9, RAP2A, DDX17, MBNL2, PIK3AP1, and ZNF385A. Based on these genes, an SD diagnostic model was constructed and its efficacy validated across multiple datasets. In the SD murine model, the mRNA and protein expressions of these 6 hub genes were found to be consistent with the results of the bioinformatics analysis., Conclusion: In conclusion, this study identified 6 genes closely linked to SD, which may play pivotal roles in neural system development, the immune microenvironment, and inflammatory responses. Additionally, the key gene-based SD diagnostic model constructed in this study, validated on multiple datasets showed a high degree of reliability and accuracy, predicting its wide potential for clinical applications. However, limited by the range of data sources and sample size, this may affect the generalizability of the results., Competing Interests: The authors declare that the editorial was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Lin, Liu and Hu.)

BackgroundAlthough more and more evidence has supported psoriasis is prone to atherosclerosis, the common mechanism of its occurrence is still not fully elucidated. The purpose of this study is to further explore the molecular mechanism of the occurrence of this complication.MethodsThe gene expression profiles of psoriasis (GSE30999) and atherosclerosis (GSE28829) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) of psoriasis and atherosclerosis, three kinds of analyses were performed, namely functional annotation, protein‐protein interaction (PPI) network and module construction, and hub gene identification and co-expression analysis.ResultsA total of 94 common DEGs (24 downregulated genes and 70 upregulated genes) was selected for subsequent analyses. Functional analysis emphasizes the important role of chemokines and cytokines in these two diseases. In addition, lipopolysaccharide-mediated signaling pathway is closely related to both. Finally, 16 important hub genes were identified using cytoHubba, including LYN, CSF2RB, IL1RN, RAC2, CCL5, IRF8, C1QB, MMP9, PLEK, PTPRC, FYB, BCL2A1, LCP2, CD53, NCF2 and TLR2.ConclusionsOur study reveals the common pathogenesis of psoriasis and atherosclerosis. These common pathways and hub genes may provide new ideas for further mechanism research.

Background: Although more and more evidence has supported psoriasis is prone to atherosclerosis, the common mechanism of its occurrence is still not fully elucidated. The purpose of this study is to further explore the molecular mechanism of the occurrence of this complication. Methods: The gene expression profiles of psoriasis (GSE30999) and atherosclerosis (GSE28829) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) of psoriasis and atherosclerosis, three kinds of analyses were performed, namely functional annotation, protein‐protein interaction (PPI) network and module construction, and hub gene identification and co-expression analysis. Results: A total of 94 common DEGs (24 downregulated genes and 70 upregulated genes) was selected for subsequent analyses. Functional analysis emphasizes the important role of chemokines and cytokines in these two diseases. In addition, lipopolysaccharide-mediated signaling pathway is closely related to both. Finally, 16 important hub genes were identified using cytoHubba, including LYN, CSF2RB, IL1RN, RAC2, CCL5, IRF8, C1QB, MMP9, PLEK, PTPRC, FYB, BCL2A1, LCP2, CD53, NCF2 and TLR2. Conclusions: Our study reveals the common pathogenesis of psoriasis and atherosclerosis. These common pathways and hub genes may provide new ideas for further mechanism research. [ABSTRACT FROM AUTHOR]

Insights into the potential candidate hub genes may facilitate the generation of safe and effective immunity against seasonal influenza as well as the development of personalized influenza vaccines for the elderly at high risk of influenza virus infection. This study aimed to identify the potential hub genes related to the immune induction process of the 2018/19 seasonal quadrivalent inactivated influenza vaccines (QIVs) in the elderly ≥60 years by using weighted gene co-expression network analysis (WGCNA). From 63 whole blood samples from16 elderly individuals, a total of 13,345 genes were obtained and divided into eight co-expression modules, with two modules being significantly correlated with vaccine-induced immune responses. After functional enrichment analysis, genes under GO terms of vaccine-associated immunity were used to construct the sub-network for identification and functional validation of hub genes. MCEMP1 and SPARC were confirmed as the hub genes with an obvious effect on QIVs-induced immunity. The MCEMP1 expression was shown to be negatively correlated with the QIVs-associated reactogenicity within 7 days after vaccination, which could be suppressed by the CXCL 8/IL-8 and exacerbated by the Granzyme-B cytotoxic mediator. Meanwhile, the SPARC expression was found to increase the immune responses to the QIVs and contribute to the persistence of protective humoral antibody titers. These two genes can be used to predict QIVs-induced adverse reaction, the intensity of immune responses, and the persistence of humoral antibody against influenza. This work has shed light on further research on the development of personalized QIVs with appropriate immune responses and long-lasting immunity against the forthcoming seasonal influenza.

Background: Systemic lupus erythematosus (SLE) is a multi-organ chronic autoimmune disease. Inflammatory bowel disease (IBD) is a common chronic inflammatory disease of the gastrointestinal tract. Previous studies have shown that SLE and IBD share common pathogenic pathways and genetic susceptibility, but the specific pathogenic mechanisms remain unclear., Methods: The datasets of SLE and IBD were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified using the Limma package. Weighted gene coexpression network analysis (WGCNA) was used to determine co-expression modules related to SLE and IBD. Pathway enrichment was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis for co-driver genes. Using the Least AbsoluteShrinkage and Selection Operator (Lasso) regressionand Support Vector Machine-Recursive Feature Elimination (SVM-RFE), common diagnostic markers for both diseases were further evaluated. Then, we utilizedthe CIBERSORT method to assess the abundance of immune cell infiltration. Finally,we used the single-cell analysis to obtain the location of common diagnostic markers., Results: 71 common driver genes were identified in the SLE and IBD cohorts based on the DEGs and module genes. KEGG and GO enrichment results showed that these genes were closely associated with positive regulation of programmed cell death and inflammatory responses. By using LASSO regression and SVM, five hub genes (KLRF1, GZMK, KLRB1, CD40LG, and IL-7R) were ultimately determined as common diagnostic markers for SLE and IBD. ROC curve analysis also showed good diagnostic performance. The outcomes of immune cell infiltration demonstrated that SLE and IBD shared almost identical immune infiltration patterns. Furthermore, the majority of the hub genes were commonly expressed in NK cells by single-cell analysis., Conclusion: This study demonstrates that SLE and IBD share common diagnostic markers and pathogenic pathways. In addition, SLE and IBD show similar immune cellinfiltration microenvironments which provides newperspectives for future treatment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Sun, Zhang and Shen.)

Background: Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration., Methods: We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis., Results: Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 ( RRM2 ), DLG-associated protein 5 ( DLGAP5 ), and kinesin family member 11 ( KIF11 ), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis., Conclusion: RRM2 , DLGAP5 , and KIF11 could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wu, Liu, Liu, Wu and Xie.)

Background: Coronavirus disease (COVID-19), caused by SARS-CoV-2, has emerged as a infectious disease, coexisting with widespread seasonal and sporadic influenza epidemics globally. Individuals living with HIV, characterized by compromised immune systems, face an elevated risk of severe outcomes and increased mortality when affected by COVID-19. Despite this connection, the molecular intricacies linking COVID-19, influenza, and HIV remain unclear. Our research endeavors to elucidate the shared pathways and molecular markers in individuals with HIV concurrently infected with COVID-19 and influenza. Furthermore, we aim to identify potential medications that may prove beneficial in managing these three interconnected illnesses., Methods: Sequencing data for COVID-19 (GSE157103), influenza (GSE185576), and HIV (GSE195434) were retrieved from the GEO database. Commonly expressed differentially expressed genes (DEGs) were identified across the three datasets, followed by immune infiltration analysis and diagnostic ROC analysis on the DEGs. Functional enrichment analysis was performed using GO/KEGG and Gene Set Enrichment Analysis (GSEA). Hub genes were screened through a Protein-Protein Interaction networks (PPIs) analysis among DEGs. Analysis of miRNAs, transcription factors, drug chemicals, diseases, and RNA-binding proteins was conducted based on the identified hub genes. Finally, quantitative PCR (qPCR) expression verification was undertaken for selected hub genes., Results: The analysis of the three datasets revealed a total of 22 shared DEGs, with the majority exhibiting an area under the curve value exceeding 0.7. Functional enrichment analysis with GO/KEGG and GSEA primarily highlighted signaling pathways associated with ribosomes and tumors. The ten identified hub genes included IFI44L , IFI44 , RSAD2 , ISG15 , IFIT3 , OAS1 , EIF2AK2 , IFI27 , OASL , and EPSTI1 . Additionally, five crucial miRNAs (hsa-miR-8060, hsa-miR-6890-5p, hsa-miR-5003-3p, hsa-miR-6893-3p, and hsa-miR-6069), five essential transcription factors (CREB1, CEBPB, EGR1, EP300, and IRF1), and the top ten significant drug chemicals (estradiol, progesterone, tretinoin, calcitriol, fluorouracil, methotrexate, lipopolysaccharide, valproic acid, silicon dioxide, cyclosporine) were identified., Conclusion: This research provides valuable insights into shared molecular targets, signaling pathways, drug chemicals, and potential biomarkers for individuals facing the complex intersection of COVID-19, influenza, and HIV. These findings hold promise for enhancing the precision of diagnosis and treatment for individuals with HIV co-infected with COVID-19 and influenza., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhang, Jin, Zhang, Bai, Zheng, Tian, Deng, Mao, Qiu and Huang.)

Background: Non-alcoholic fatty liver disease (NAFLD), which includes simple steatosis (SS) and non-alcoholic steatohepatitis (NASH), is a significant contributor to liver disease on a global scale. The change of immunity-related genes (IRGs) expression level leads to different immune infiltrations. However, the expression of IRGs and possible regulatory mechanisms involved in NAFLD remain unclear. The objective of our research is to investigate crucial genes linked to the development of NAFLD and the transition from SS to NASH., Methods: Dataset GSE89632, which includes healthy controls, SS patients, and NASH patients, was obtained using the GEO database. To examine the correlation between sets of genes and clinical characteristics, we employed weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Hub genes were extracted using a network of protein-protein interactions (PPI) and three different machine learning algorithms. To validate the findings, another dataset that is publicly accessible and mice that were subjected to a high-fat diet (HFD) or MCD diet were utilized. Furthermore, the ESTIMATE algorithm and ssGSEA were employed to investigate the immune landscape in the normal versus SS group and SS versus NASH group, additionally, the relationship between immune infiltration and the expression of hub genes was also examined., Results: A total of 28 immune related key genes were selected. Most of these genes expressed reverse patterns in the initial and progressive stages of NAFLD. GO and KEGG analyses showed that they were focused on the cytokine related pathways and immune cell activation and chemotaxis. After screening by various algorithms, we obtained two hub genes, including JUN and CCL20. Validation of these findings was confirmed by analyzing gene expression patterns in both the validation dataset and the mouse model. Ultimately, two hub genes were discovered to have a significant correlation with the infiltration of immune cells., Conclusion: We proposed that there were dynamic changes in the expression levels of IRGs in different stages of NAFLD disease, which led to different immune landscapes in SS and NASH. The findings of our research could serve as a guide for the accurate management of various phases of NAFLD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 He, Guan, Zhao, Yu and Cui.)

Insights into the potential candidate hub genes may facilitate the generation of safe and effective immunity against seasonal influenza as well as the development of personalized influenza vaccines for the elderly at high risk of influenza virus infection. This study aimed to identify the potential hub genes related to the immune induction process of the 2018/19 seasonal quadrivalent inactivated influenza vaccines (QIVs) in the elderly ≥60 years by using weighted gene co-expression network analysis (WGCNA). From 63 whole blood samples from16 elderly individuals, a total of 13,345 genes were obtained and divided into eight co-expression modules, with two modules being significantly correlated with vaccine-induced immune responses. After functional enrichment analysis, genes under GO terms of vaccine-associated immunity were used to construct the sub-network for identification and functional validation of hub genes. MCEMP1 and SPARC were confirmed as the hub genes with an obvious effect on QIVs-induced immunity. The MCEMP1 expression was shown to be negatively correlated with the QIVs-associated reactogenicity within 7 days after vaccination, which could be suppressed by the CXCL 8/IL-8 and exacerbated by the Granzyme-B cytotoxic mediator. Meanwhile, the SPARC expression was found to increase the immune responses to the QIVs and contribute to the persistence of protective humoral antibody titers. These two genes can be used to predict QIVs-induced adverse reaction, the intensity of immune responses, and the persistence of humoral antibody against influenza. This work has shed light on further research on the development of personalized QIVs with appropriate immune responses and long-lasting immunity against the forthcoming seasonal influenza. [ABSTRACT FROM AUTHOR]

There is sufficient evidence indicating that keloid is strongly associated with atopic dermatitis (AD) across ethnic groups. However, the molecular mechanism underlying the association is not fully understood. The aim of this study is to discover the underlying mechanism of the association between keloid and AD by integrating comprehensive bioinformatics techniques and machine learning methods. The gene expression profiles of keloid and AD were downloaded from the Gene Expression Omnibus (GEO) database. A total of 449 differentially expressed genes (DEGs) were found to be shared in keloid and AD using the training datasets of GEO (GSE158395 and GSE121212). The hub genes were identified using the protein-protein interaction network and Cytoscape software. 20 of the most significant hub genes were selected, which were mainly involved in the regulation of the inflammatory and immune response. Through two machine learning algorithms of LASSO and SVM-RFE, CCR5 was identified as the most important key gene. Subsequently, upregulated CCR5 gene expression was confirmed in validation GEO datasets (GSE188952 and GSE32924) and clinical samples of keloid and AD. Immune infiltration analysis showed that T helper (Th) 1, 2 and 17 cells were significantly enriched in the microenvironment of both keloid and AD. Positive correlations were found between CCR5 and Th1, Th2 and Th17 cells. Finally, two TFs of CCR5, NR3C2 and YY1, were identified, both of which were downregulated in keloid and AD tissues. Our study firstly reveals that keloid and AD shared common inflammatory and immune pathways. Moreover, CCR5 plays a key role in the pathogenesis association between keloid and AD. The common pathways and key genes may shed light on further mechanism research and targeted therapy, and may provide therapeutic interventions of keloid with AD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhou, Zhou, Liu, Dong, Peng, Wang, Yang, Suo and Tao.)