Glycolysis-Related Genes, S100A8 and CXCL1, Participate in Acute Myocardial Infarction by Regulating Immune Cell Infiltration

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

This study aims to identify differentially expressed genes (DEGs) in neuroblastoma (NB) through comprehensive bioinformatics analysis and machine learning techniques. We seek to elucidate these DEGs' biological functions and associated signaling pathways. Furthermore, our objective extends to predicting upstream microRNAs (miRNAs) and relevant transcription factors of pivotal genes, with the ultimate goal of guiding clinical diagnostics and informing future treatment strategies for Neuroblastoma. In this study, we sourced datasets GSE49710 and TARGET from the GEO and UCSC-XENA databases, respectively. Differentially expressed genes (DEGs) were identified using the R language "limma" package. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of these DEGs were conducted using the "clusterProfiler" package. We employed Weighted Gene Co-expression Network Analysis (WGCNA) to isolate the most significant modules associated with death and MYCN amplification, specifically MEpink and MEbrown modules. These modules were then cross-referenced with the DEGs for further GO and KEGG pathway analyses. LASSO regression analysis, facilitated by the "glmnet" package, was utilized to pinpoint three hub genes. We performed differential analysis on these genes and constructed Receiver Operating Characteristic (ROC) curves for disease diagnosis purposes. Immune infiltration analysis was conducted using the "GSVA" package's ssGSEA function. Additionally, single-gene Gene Set Enrichment Analysis (GSEA) on the hub gene was carried out based on Reactome and KEGG databases. Upstream miRNA and transcription factors associated with the hub gene were predicted using RegNetwork, with visual representations created in Cytoscape. Furthermore, to validate the three identified markers in neuroblastoma tissues, quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) analysis was conducted. We identified 483 differentially expressed genes (DEGs) in neuroblastoma. These genes predominantly function in protein translation, membrane composition, and RNA transcription regulation, and are implicated in multiple signaling pathways relevant to neurodegenerative diseases. Utilizing LASSO regression analysis, we pinpointed three hub genes: VGF, DGKD, and C19orf52. The Receiver Operating Characteristic (ROC) curve analysis yielded Area Under Curve (AUC) values of 0.751 and 0.722 for VGF, 0.79 and 0.656 for DGKD, and 0.8 and 0.753 for C19orf52, respectively. Our immune infiltration analysis revealed significant correlations among monocytes, follicular helper T cells, and CD4+ T cells. Notably, in the death group, we observed heightened infiltration levels of activated CD4+ T cells, macrophages, and Th2 cells. C19orf52 exhibited a close association with the infiltration of monocytes, CD4+ T cells, and Th2 cells, with P-values less than 0.05. Furthermore, qRT-PCR analysis corroborated the upregulation of VGF in neuroblastoma tissues, further validating our findings. The hub genes (VGF, DGKD, and C19orf52) of neuroblastoma are screened. VGF, one of the hub genes, may have a high diagnostic value and is involved in the immune cell infiltration in neuroblastoma tissue, which may be used as a biomarker for the diagnosis of neuroblastoma and provides a new direction for clinical prognosis prediction and management improvement.

BackgroundOsteoarthritis (OA) is a common degenerative disorder characterized primarily by articular cartilage degradation and chronic inflammation. Although direct evidence elucidating the specific mechanisms underlying the coagulation-immune axis in OA remains limited, emerging studies have suggested a potential link.MethodsFour microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database. Then, differentially expressed genes (DEGs) (|log₂FC| ≥ 1, P < 0.05) were identified. Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on these DEGs. Molecular Signatures Database (MsigDB) coagulation genes were intersected with DEGs to identify coagulation-related DEGs. Then, hub genes were determined using multiple Machine learning (ML) algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest (RF). Diagnostic performance of these genes was evaluated via a nomogram and ROC analysis (AUC). Immune cell infiltration was assessed with CIBERSORT. The expression of hub genes was validated in vitro via real-time qPCR and Western blot (WB).ResultsBased on 103 samples across four datasets, 294 DEGs were identified. Gene set enrichment analyses (GSEA, GO, KEGG) revealed significant enrichment of these genes in immune- and coagulation-related pathways in OA. Intersecting MsigDB coagulation genes with DEGs yielded nine coagulation-associated DEGs. Based on four distinct ML algorithms, six hub genes were selected: Fibroblast activation protein (FAP), Cathepsin H (CTSH), matrix metalloproteinase 1 (MMP1), matrix metalloproteinase 9 (MMP9), Complement component 6 (C6), MAF Basic Leucine Zipper Transcription Factor F (MAFF). These hub genes demonstrated high diagnostic accuracy according to ROC analysis. Immune infiltration analysis showed significant differences between OA and normal samples. M0 macrophages, plasma cells, and γδ T cells were elevated in OA, while activated mast cells and resting memory CD4⁺ T cells were decreased. The qPCR and WB results corroborated the ML findings: in the interleukin-1β (IL-1β)-treated group, FAP, MMP1, MMP9, and CTSH were significantly upregulated, while MAFF and C6 were markedly downregulated.ConclusionsThis study, based on publicly available GEO datasets, identified six potential diagnostic biomarkers for OA: FAP, CTSH, MMP1, MMP9, C6, and MAFF. These findings highlight the potential involvement of coagulation-immune interactions in OA pathogenesis and offer novel insights into the molecular mechanisms and diagnostic strategies for the disease.

Major depressive disorder (MDD) is a severe psychiatric disorder characterized by complex etiology, with genetic determinants that are not fully understood. The objective of this study was to investigate the pathogenesis of MDD and to explore its association with the immune system by identifying hub biomarkers using bioinformatics analyses and examining immune infiltrates in human autopsy samples. Gene microarray data were obtained from the Gene Expression Omnibus (GEO) datasets GSE32280, GSE76826, GSE98793, and GSE39653. Our approach included differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis to identify hub genes associated with MDD. Subsequently, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape plugin CluGO, and Gene Set Enrichment Analysis (GSEA) were utilized to identify immune-related genes. The final selection of immune-related hub genes was determined through the least absolute shrinkage and selection operator (Lasso) regression analysis and PPI analysis. Immune cell infiltration in MDD patients was analyzed using CIBERSORT, and correlation analysis was performed between key immune cells and genes. The diagnostic accuracy of the identified hub genes was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, we conducted a study involving 10 MDD patients and 10 healthy controls (HCs) meeting specific criteria to assess the expression levels of these hub genes in their peripheral blood mononuclear cells (PBMCs). The Herbal Ingredient Target Database (HIT) was employed to screen for herbal components that target these genes, potentially identifying novel therapeutic agents. A total of 159 down-regulated and 51 up-regulated genes were identified for further analysis. WGCNA revealed 12 co-expression modules, with modules "darked", "darkurquoise" and "light yellow" showing significant positive associations with MDD. Functional enrichment pathway analysis indicated that these differential genes were associated with immune functions. Integration of differential and immune-related gene analysis identified 21 common genes. The Lasso algorithm confirmed 4 hub genes as potential biomarkers for MDD. GSEA analysis suggested that these genes may be involved in biological processes such as protein export, RNA degradation, and fc gamma r mediated cytotoxis. Pathway enrichment analysis identified three highly enriched immune-related pathways associated with the 4 hub genes. ROC curve analysis indicated that these hub genes possess good diagnostic value. Quantitative reverse transcription-polymerase chain reaction (RT-qPCR) demonstrated significant expression differences of these hub genes in PBMCs between MDD patients and HCs. Immune infiltration analysis revealed significant correlations between immune cells, including Mast cells resting, T cells CD8, NK cells resting, and Neutrophils, which were significantly correlated with the hub genes expression. HIT identified one herb target related to IL7R and 14 targets related to TLR2. The study identified four immune-related hub genes (TLR2, RETN, HP, and IL7R) in MDD that may impact the diagnosis and treatment of the disorder. By leveraging the GEO database, our findings contribute to the understanding of the relationship between MDD and immunity, presenting potential therapeutic targets.

Crohn's disease (CD) is an immune-mediated disorder characterized by immune cell infiltration that induces persistent chronic inflammation of the gastrointestinal tract. Programmed cell death (PCD) plays a critical role in the pathogenesis of CD. This study identified vital PCD-related genes in CD based on immune infiltration using bioinformatic analysis. We obtained two CD datasets from the Gene Expression Omnibus (GEO) database and examined immune cell infiltration to investigate immune cell dysregulation in CD. PCD-related genes were retrieved from the GeneCards database. Based on the differentially expressed genes (DEGs) and PCD gene sets, PCD-related DEGs were identified. Candidate hub genes were identified using a protein-protein interaction (PPI) network, and their diagnostic effectiveness was predicted using receiver operating characteristic (ROC) curve analysis. Functional enrichment and immune infiltration analyses were used to assess the distinct roles of the hub genes. Finally, the miRWalk and ENCORI databases were used to predict which microRNAs (miRNAs) regulated the hub genes and to verify gene expression in CD colonic tissues via transcriptome sequencing. A total of 335 PCD-related DEGs and 3 hub genes (MMP1, SAA1, and PLAU) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses indicated the enrichment of these genes in the immune response. Infiltration analysis of immune cells showed abundant endothelial cells, plasma cells, dendritic cells, and monocytes in the CD samples. Based on the correlation analysis, the three hub genes were positively correlated with monocytes and negatively correlated with CD8 naïve T-cells. MMP1, SAA1, and PLAU correlated with the pathogenicity of CD and had good diagnostic value for CD. The three hub genes were highly expressed in the CD tissues, as confirmed using transcriptome sequencing. This study identified MMP1, SAA1, and PLAU as hub genes involved in PCD in patients with CD. These genes regulate immune cell function and their expression levels are closely related to immune cell infiltration. These findings provide novel insights into the mechanisms underlying CD pathogenesis. The identified PCD genes and regulatory miRNAs are potential biomarkers and therapeutic targets for CD.

BackgroundThe 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.MethodsThe 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.ResultsIn 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.ConclusionThe 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.

Recent studies have shown that dysfunction in chromatin regulators (CRs) may be an important mechanism of myocardial infarction (MI). They are thus expected to become a new target in the diagnosis and treatment of MI. However, the diagnostic value of CRs in MI and the mechanisms are not clear. CRs-related differentially expressed genes (DEGs) were screened between healthy controls and patients with MI via GSE48060, GSE60993, and GSE66360 datasets. DEGs were further analyzed for enrichment analysis. Hub genes were screened by least absolute shrinkage and selection operator (LASSO) regression and weighted gene co-expression network analysis (WGCNA). GSE61144 datasets were further used to validate hub genes. RT-qPCR examined peripheral blood mononuclear cells (PBMCs) to verify expressions of hub genes. In addition, a correlation between hub genes and immune cell infiltration was identified by CIBERSORT and single-sample gene set enrichment analysis (ssGSEA). Finally, we constructed a diagnostic nomogram and ceRNA network and found possible therapeutic medicines which were based on hub genes. Firstly, 16 CR-related DEGs were identified. Next, Dual-specificity phosphatase 1 (<i>DUSP1</i>), growth arrest and DNA damage-inducible 45 (GADD45A), and transcriptional regulator Jun dimerization protein 2 (<i>JDP2</i>) were selected as hub genes by LASSO and WGCNA. Receiver operating characteristic curves in the training and test data sets verified the reliability of hub genes. Results of RT-qPCR confirmed the upregulation of hub genes in MI. Subsequently, the immune infiltration analysis indicated that <i>DUSP1</i>, GADD45A</i>, and <i>JDP2</i> were correlated with plasmacytoid dendritic cells, natural killer cells, eosinophils, effector memory CD4 T cells, central memory CD4 T cells, activated dendritic cells, and activated CD8 T cells. Furthermore, a nomogram that included <i>DUSP1</i>, GADD45A</i>, and <i>JDP2</i> was created. The calibration curve, decision curve analysis, and the clinical impact curve indicated that the nomogram could predict the occurrence of MI with high efficacy. The results of the ceRNA network suggest that hub genes may be cross-regulated by various lncRNAs and miRNAs. In addition, 10 drugs, including 2H-1-benzopyran, Nifuroxazide, and Bepridil, were predicted to be potential therapeutic agents for MI. Our study identifies three promising genes associated with the progression of chromatin regulators (CRs)-related myocardial infarction (MI) and immune cell infiltration, including Dual-specificity phosphatase 1 (DUSP1), growth arrest and DNA damage-inducible 45 (GADD45A), and Jun dimerization protein 2 (JDP2), which might be worthy of further study.

BackgroundIntestinal ischemia-reperfusion (II/R) injury is a serious condition characterized by high morbidity and mortality rates. Research has shown that II/R injury is closely linked to autophagy and immune dysregulation. This study aims to investigate the potential correlations between autophagy-related genes and infiltrating immune cells in II/R injury.MethodsGSE96733, GSE37013, and autophagy-related genes were obtained from the Gene Expression Omnibus (GEO) and the Human Autophagy Database, respectively. Subsequently, the biological functions of the differentially expressed genes (DEGs) were explored through DEGs analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Ontology (GO) analysis. Using R software, human autophagy-related genes were converted to their mouse homologous autophagy-related genes (ARGs). The DEGs were then intersected with ARGs to obtain differentially expressed autophagy-related genes (DEARGs). To identify hub genes, protein-protein interaction (PPI) network analysis, Lasso regression, and random forest methods were employed. A nomogram model was constructed to assess its diagnostic value. Following this, immune infiltration analysis was performed to evaluate the potential correlation between Hub genes and immune cell infiltration. Additionally, a hub gene-related network was constructed, and potential drugs targeting hub genes for the treatment of II/R injury were predicted. Finally, the expression levels of hub genes in a mouse model of II/R injury were validated through dataset verification and quantitative real-time polymerase chain reaction (qRT-PCR).ResultsOur analysis identified 11 DEARGs. Among these, 5 DEARGs (Myc, Hif1a, Zfyve1, Sqstm1, and Gabarapl1) were identified as hub genes. The nomogram model demonstrated excellent diagnostic value. Immune cell infiltration analysis indicated that these 5 hub genes are closely associated with dendritic cells and M2.Macrophage. Furthermore, the regulatory network illustrated a complex relationship between microRNAs (miRNAs) and the hub genes. Additionally, trigonelline and niacinamide were predicted as potential therapeutic agents for II/R injury. In both dataset validation and qRT-PCR validation, the four hub genes (Myc, Hif1a, Sqstm1, and Gabarapl1) showed consistency with the results of the bioinformatics analysis.ConclusionMyc, Hif1a, Sqstm1, and Gabarapl1 have been identified as ARGs closely associated with immune infiltration in II/R injury. These hub genes may represent potential therapeutic targets for II/R injury.

Colorectal cancer (CRC) is one of the most common malignancies worldwide; however, the potentially possible molecular biological mechanism of CRC is still not completely comprehended. This study aimed to confirm candidate key hub genes involved in the growth and development of CRC and their connection with immune infiltration as well as the related pathways. Gene expression data were selected from the GEO dataset. Hub genes for CRC were identified on the basis of differential expression analysis, weighted gene co-expression network analysis (WGCNA), and LASSO regression. Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA) were applied to reveal possible functions of the differential genes. Single-sample GSEA (ssGSEA) was implemented to identify the relationship between immune cells infiltration and hub genes. Two hundred and sixty-two differentially expressed genes (DEGs) were identified. Three modules were acquired based on WGCNA, and the blue module presented the highest relevance with CRC. Ten hub genes (AQP8, B3GALT5, CDH3, CEMIP, CPM, FOXQ1, PLAC8, SCNN1B, SPINK5, and SST) were acquired with LASSO analysis as underlying biomarkers for CRC. Compared with normal tissues, CRC tissues presented significantly higher numbers of CD4 T cells, CD8 T cells, B cells, natural regulatory T (Treg) cells, and monocytes. The functional enrichment analyses demonstrated that hub genes were primarily enriched in metabolic process, inflammatory-related, and immune-related response. Ten hub genes were identified to be involved in the occurrence and development of CRC and may be deemed as novel biomarkers for clinical diagnosis and treatment.

Hepatitis B virus-related acute-on-chronic liver failure (ACHBLF) is a severe condition associated with short-term mortality without liver transplantation. Substantial evidence indicates that necroptosis and immune infiltration play critical roles in ACHBLF development. Therefore, the identification of necroptosis-related biomarkers may be beneficial for prognostic evaluations and may shed light on potential therapeutic targets for ACHBLF. In this study, we used integrated bioinformatics analysis and machine learning algorithms to investigate the correlation between necroptosis and immune infiltration using peripheral blood mononuclear cells from ACHBLF patients. First, after GSE168048 and GSE248217 were obtained from the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) between ACHBLF patients and normal controls were identified. With the help of the weighted gene coexpression network, 211 necroptosis-related DEGs were identified by intersecting the DEGs with necroptosis-related genes (NRGs). The functional characterization of these NRG-related DEGs was subsequently performed using GO, KEGG, and gene set enrichment analysis. Hub genes were identified through the integration of LASSO regression, support vector machines recursive feature elimination, and random forest. Immune-related functions were explored by analyzing the correlations between hub gene expression and immune cells infiltration. Furthermore, mRNA-miRNA-lncRNA interaction network, RNA-binding proteins (RBPs) and transcription factors were predicted using the miRDB, starBase and hTFtarget databases. Finally, target drugs were predicted using a connectivity map. A total of 7461 DEGs were identified between the ACHBLF and normal groups; 3123 genes were upregulated and 4338 genes were downregulated. A total of 211 NRG-related DEGs and 5 hub genes (FCRL3, CDC14A, KLHL22, RALY, and MAP4K1) were obtained. The hub genes were enriched in the T cell receptor signaling pathway, ubiquitin-mediated proteolysis, and the MAPK signaling pathway and were correlated with immune cell infiltration. A lncRNA XIST-miR-424-5p-CDC14A regulatory network was constructed, and 20RBPs and 2 transcription factors (GATA1 and CEBPB) were identified. In addition, 10 candidate drugs were predicted to target MAP4K1. The 5 hub genes could serve as biomarkers for predicting the prognosis of ACHBLF patients and provide clues for new potential therapeutic targets.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-21273-2.

Age-related macular degeneration (AMD) is a prevalent source of visual impairment among the elderly population, and its incidence has risen in tandem with the increasing longevity of humans. Despite the progress made with anti-VEGF therapy, clinical outcomes have proven to be unsatisfactory. We obtained differentially expressed genes (DEGs) of AMD patients and healthy controls from the GEO database. GO and KEGG analyses were used to enrich the DEGs. Weighted gene coexpression network analysis (WGCNA) was used to identify modules related to AMD. SVM, random forest, and least absolute shrinkage and selection operator (LASSO) were employed to screen hub genes. Gene set enrichment analysis (GSEA) was used to explore the pathways in which these hub genes were enriched. CIBERSORT was utilized to analyze the relationship between the hub genes and immune cell infiltration. Finally, Western blotting and RT‒PCR were used to explore the expression of hub genes in AMD mice. We screened 1084 DEGs in GSE29801, of which 496 genes were upregulated. These 1084 DEGs were introduced into the WGCNA, and 94 genes related to AMD were obtained. Seventy-nine overlapping genes were obtained by the Venn plot. These 79 genes were introduced into three machine-learning methods to screen the hub genes, and the genes identified by the three methods were TNC, FAP, SREBF1, and TGF-β2. We verified their diagnostic function in the GSE29801 and GSE103060 datasets. Then, the hub gene co-enrichment pathways were obtained by GO and KEGG analyses. CIBERSORT analysis showed that these hub genes were associated with immune cell infiltration. Finally, we found increased expression of TNC, FAP, SREBF1, and TGF-β2 mRNA and protein in the retinas of AMD mice. We found that four hub genes, namely, FAP, TGF-β2, SREBF1, and TNC, have diagnostic significance in patients with AMD and are related to immune cell infiltration. Finally, we determined that the mRNA and protein expression of these hub genes was upregulated in the retinas of AMD mice.

Spinal cord injury (SCI) is a common traumatic central nervous system disorder characterized by a complex microenvironment after injury, leading to severe neurological dysfunction. Increasing attention has been paid to the role of inflammatory cells in SCI. Pyroptosis, a form of programmed inflammatory cell death, plays a significant role in SCI; however, its underlying mechanisms remain unclear. This study utilized bioinformatics methods to identify pyroptosis-related genes (PRGs) and investigate their roles in inflammatory responses. Additionally, we explored the potential interactions between differentially expressed PRGs (DEPRGs) and miRNAs(microRNAs), lncRNAs(Long non-coding RNAs), and circRNAs(Circular RNAs). RNA expression profiles, including mRNA(Messenger RNA), miRNA, lncRNA, and circRNA, were obtained from the Gene Expression Omnibus (GEO) database. A total of 78 PRGs were identified through a literature search in PubMed. DEPRGs, miRNAs, lncRNAs, and circRNAs were screened using R software. Functional enrichment analyses of DEPRGs were performed, and a protein-protein interaction (PPI) network was constructed. Weighted Gene Co-expression Network Analysis (WGCNA) was conducted, and external validation was performed using an independent dataset to identify hub genes and their correlations with immune cells. Western blotting was used to validate hub gene expression. Finally, a competing endogenous RNA (ceRNA) network was constructed based on differentially expressed miRNAs (DEmiRNAs), lncRNAs (DElncRNAs), and circRNAs (DEcircRNAs). Eleven DEPRGs were identified in SCI, including ten upregulated and one downregulated gene. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that DEPRGs were mainly enriched in processes such as the regulation of interleukin-1 beta production, NLRP3 inflammasome complex, and NOD-like receptor signaling pathway. Two hub genes, IL18 and GSDMD, were identified using Cytoscape and WGCNA. External dataset validation revealed significant differences in hub gene expression between SCI and control groups (P < 0.05), with AUC(Area Under Curve) values > 0.75. Immune infiltration analysis indicated significant positive correlations between hub genes and M1 macrophages, CD8 + T cells, and resting dendritic cells. Four downregulated DEmiRNAs were identified as potential regulators of hub genes. Additionally, 29 DEmiRNAs, 55 upregulated DElncRNAs, and 2 upregulated DEcircRNAs were identified and used to construct the ceRNA network. Bioinformatics analyses revealed a strong association between pyroptosis and SCI, identifying two potential hub genes, IL18 and GSDMD, linked to pyroptosis and immune cell infiltration. These findings provide insights into the potential mechanisms of pyroptosis in SCI and offer a basis for further studies.

Background: The deadly cardiovascular condition known as Stanford type A aortic dissection (TAAD) carries a high risk of morbidity and mortality. One important step in the pathophysiology of the condition is the influx of immune cells into the aorta media, which causes medial degeneration. The purpose of this work was to investigate the potential pathogenic significance of immune cell infiltration in TAAD and to test for associated biomarkers. Methods: The National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database provided the RNA sequencing microarray data (GSE153434, GPL20795, GSE52093). Immune cell infiltration abundance was predicted using ImmuCellAI. GEO2R was used to select differentially expressed genes (DEGs), which were then processed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Additionally, hub genes linked to immune infiltration were found using functional and pathway enrichment, least absolute shrinkage and selection operator (LASSO), weighted gene co-expression network analysis (WGCNA), and differential expression analysis. Lastly, hub genes were validated and assessed using receiver operating characteristic (ROC) curves in the microarray dataset GSE52093. The hub gene expression and its connection to immune infiltration in TAAD were confirmed using both animal models and clinic data. Results: We identified the most important connections between macrophages, T helper cell 17 (Th17), iTreg cells, B cells, natural killer cells and TAAD. And screened seven hub genes associated with immune cell infiltration: ABCG2, FAM20C, ELL2, MTHFD2, ANKRD6, GLRX, and CDCP1. The diagnostic model in TAAD diagnosis with the area under ROC (AUC) was 0.996, and the sensitivity was 99.21%, the specificity was 98.67%, which demonstrated a surprisingly strong diagnostic power of TAAD in the validation datasets. The expression pattern of four hub DEGs (ABCG2, FAM20C, MTHFD2, CDCP1) in clinic samples and animal models matched bioinformatics analysis, and ABCG2, FAM20C, MTHFD2 up-regulation, and the of CDCP1 down-regulation were also linked to poor cardiovascular function. Conclusions: This study developed and verified an effective diagnostic model linked to immune infiltration in TAAD, providing new approaches to studying the potential pathogenesis of TAAD and discovering new medication intervention targets.

BackgroundSystemic lupus erythematosus (SLE) is a multisystemic, chronic inflammatory disease characterized by destructive systemic organ involvement, which could cause the decreased functional capacity, increased morbidity and mortality. Previous studies show that SLE is characterized by autoimmune, inflammatory processes, and tissue destruction. Some seriously-ill patients could develop into lupus nephritis. However, the cause and underlying molecular events of SLE needs to be further resolved.MethodsThe expression profiles of GSE144390, GSE4588, GSE50772 and GSE81622 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between SLE and healthy samples. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by metascape etc. online analyses. The protein–protein interaction (PPI) networks of the DEGs were constructed by GENEMANIA software. We performed Gene Set Enrichment Analysis (GSEA) to further understand the functions of the hub gene, Weighted gene co‐expression network analysis (WGCNA) would be utilized to build a gene co‐expression network, and the most significant module and hub genes was identified. CIBERSORT tools have facilitated the analysis of immune cell infiltration patterns of diseases. The receiver operating characteristic (ROC) analyses were conducted to explore the value of DEGs for SLE diagnosis.ResultsIn total, 6 DEGs (IFI27, IFI44, IFI44L, IFI6, EPSTI1 and OAS1) were screened, Biological functions analysis identified key related pathways, gene modules and co‐expression networks in SLE. IFI27 may be closely correlated with the occurrence of SLE. We found that an increased infiltration of moncytes, while NK cells resting infiltrated less may be related to the occurrence of SLE.ConclusionIFI27 may be closely related pathogenesis of SLE, and represents a new candidate molecular marker of the occurrence and progression of SLE. Moreover immune cell infiltration plays important role in the progession of SLE.

Alzheimer's disease (AD) is a condition that affects the nervous system and that requires considerably more in-depth study. Abnormal Nicotinamide Adenine Dinucleotide (NAD+) metabolism and disulfide levels have been demonstrated in AD. This study investigated novel hub genes for disulfide levels and NAD+ metabolism in relation to the diagnosis and therapy of AD. Data from the gene expression omnibus (GEO) database were analyzed. Hub genes related to disulfide levels, NAD+ metabolism, and AD were identified from overlapping genes for differentially expressed genes (DEGs), genes in the NAD+ metabolism or disulfide gene sets, and module genes obtained by weighted gene co-expression network analysis (WGCNA). Pathway analysis of these hub genes was performed by Gene Set Enrichment Analysis (GSEA). A diagnostic model for AD was constructed based on the expression level of hub genes in brain samples. CIBERSORT was used to evaluate immune cell infiltration and immune factors correlating with hub gene expression. The DrugBank database was also used to identify drugs that target the hub genes. We identified 3 hub genes related to disulfide levels in AD and 9 related to NAD+ metabolism in AD. Pathway analysis indicated these 12 genes were correlated with AD. Stepwise regression analysis revealed the area under the curve (AUC) for the predictive model based on the expression of these 12 hub genes in brain tissue was 0.935, indicating good diagnostic performance. Additionally, analysis of immune cell infiltration showed the hub genes played an important role in AD immunity. Finally, 33 drugs targeting 10 hub genes were identified using the DrugBank database. Some of these have been clinically approved and may be useful for AD therapy. Hub genes related to disulfide levels and NAD+ metabolism are promising biomarkers for the diagnosis of AD. These genes may contribute to a better understanding of the pathogenesis of AD, as well as to improved drug therapy.