Hypoxia-related gene expression in major depressive disorder: identifying diagnostic biomarkers and implications for immune cell infiltration.

Major depressive disorder (MDD) is a life-threatening, debilitating mental health condition. An important factor in the development of depression is endoplasmic reticulum stress (ERS). However, their roles in MDD have not yet been established. The goal of this study was to examine ERS and its underlying molecular mechanisms in MDD. We used data from two microarray datasets (GSE98793 and GSE39653) and the GeneCards database to examine the reticulum stress-related differentially expressed genes (ERSR-DEGs) associated with MDD. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were used to further investigate the function and mechanism of ERS in MDD. Moreover, we constructed protein-protein interaction (PPI) networks to identify hub genes as well as the regulatory network of microRNAs (miRNAs), transcription factors (TFs), and potential drugs related to ERSR-DEGs. CIBERSORT was then used to evaluate the immune activity of MDD samples and conduct a correlation analysis between the hub genes and immune cells. In total, 37 ERSR-DEGs and five hub genes were identified (NCF1, MAPK14, CASP1, CYBA, and TNF). Functional enrichment analysis revealed that ERSR-DEGs were predominantly enriched in inflammation-and immunity-related pathways, such as tumor necrosis factor signaling, NF-κB signaling, and Toll-like receptor signaling pathways. Additionally, 179 miRNAs, 25 TFs, and 15 potential drugs were tested for their interactions with the ERSR-DEGs. CIBERSORT found high proportions of Tregs, monocytes, and macrophages M0 in the MDD samples. Among these, hub genes showed a significant correlation with immune cell infiltration in patients with MDD. NCF1, MAPK14, CASP1, CYBA, and TNF are potential ERS-related biomarkers for the diagnosis of MDD. Our research has revealed a significant correlation between immune cells and ERS-related genes with MDD. Not only did our study contribute to a better understanding of the regulatory mechanisms of ERS in underlying MDD pathology, but it also established a paradigm for future studies on ERS.

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.

Extracorporeal membrane oxygenation (ECMO) is an important clinical treatment for acute myocardial infarction (AMI) combined with cardiogenic shock, but the role of programmed cell death (PCD)-related genes in prognostication has not yet been investigated. Therefore, we explored the key prognostic biomarkers and immune infiltration in ECMO treatment in AMI combined with cardiogenic shock. The GSE93101 dataset was analyzed from the Gene Expression Omnibus (GEO) database, and the expression levels of PCD-related genes in AMI under ECMO were identified. Differentially expressed PCD-related genes between successful and failed treatment samples were analyzed, and Least absolute shrinkage and selection operator (LASSO) logistic regression and random forest were used to screen PCD-related molecular markers for ECMO treatment in AMI combined with cardiogenic shock. Co-expressed regulatory network and enrichment functions of the hub PCD-related genes were performed. In addition, the single-sample gene set enrichment analysis (ssGSEA) algorithm was used to calculate the immune cell infiltration of the ECMO treatment samples. A total of 115 differentially expressed genes were identified from the GSE93101 dataset, and 76 genes were associated with PCD. Then, two hub PCD-related genes, Cell division cycle associated 7 (CDCA7), ankyrin repeat and SOCS box containing 13 (ASB13) were identified as prognostic markers of ECMO treatment in AMI combined with cardiogenic shock. The most significant Gene Ontology (GO) enriched terms of the co-expressed protein of ASB13 are related to post-translational protein modification, cullin-RING ubiquitin ligase complex, and cullin family protein binding, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that ubiquitin mediated proteolysis is the most enriched pathway. The results of GO and KEGG analysis in CDCA7 were mainly involved in DNA and cell cycle related activities and pathways. Moreover, we found that the successful treatment samples contained a lower proportion of nature killer T cells using immune infiltration analysis. Immune cell infiltration analysis revealed that ASB13 was positively correlated with natural killer cell (r = 0.591, p = 0.026), monocyte (r = 0.586, p = 0.028), and gamma delta T cell (r = 0.562, p = 0.036). The results of this study showed that ASB13 and CDCA7 may contribute to the occurrence and progression of AMI with cardiogenic shock under ECMO.

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.

Introduction: The morbidity and mortality of acute kidney injury (AKI) are increasing. Epigenetic regulation and immune cell infiltration are thought to be involved in AKI. However, the relationship between epigenetic regulation and immune cell infiltration in AKI has not been elucidated. This study was conducted to identify the differentially expressed genes (DEGs), differentially expressed RNA methylation genes (DEMGs), and infiltrated immune cells in the kidneys of ischemia-reperfusion induced-acute kidney injury (IRI-AKI) models and further explore their relationships in IRI-AKI. Methods: This is a bioinformatic analysis using R programming language in 3 selected IRI-AKI datasets from the Gene Expression Omnibus (GEO) database, including 16 IRI-AKI kidney tissues and 10 normal kidney tissues. The DEGs were screened, and enrichment pathways were analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The DEMGs and core DEMGs were identified using the R package. The ROC curve was plotted to predict disease occurrence of 7 core DEMGs. The correlation of 7 core DEMGs and other genes was analyzed using Pearson’s correlation test. The gene set enrichment analysis (GSEA) of each DEMG was conducted using the R package. The upstream miRNAs and transcript factors of 7 core DEMGs were predicted based on the RegNetwork database and Cytoscape software. The STITCH database was used to predict the possible binding compounds of the 7 core DEMGs. Immune cell infiltration in kidney tissues between the IRI-AKI group and control group was evaluated using the R package. Results: A total of 2,367 DEGs were obtained, including 1,180 upregulated and 1,187 downregulated genes in IRI-AKI kidney associated with the cell structure, proliferation, molecule binding/interaction, and signaling pathways such as the leukocyte migration and chemokine signaling pathways. Ten DEMGs were identified, with Ythdf1, Rbm15, Trmt6, Hnrnpc, and Dnmt1 being significantly upregulated, while Lrpprc, Cyfip2, Mettl3, Ncbp2, and Nudt7 were significantly downregulated in IRI-AKI tissues. The molecules interacting with 7 core DEMGs were identified. Significant changes in the infiltration of 8 types of immune cells were observed in IRI-AKI kidneys compared to normal controls. The significant correlation between 6 core DEMGs and the infiltration of immune cells was observed. Conclusion: IRI may induce AKI through RNA methylation to regulate the expression of genes involved in immune cell infiltration.

Osteoporosis (OP), marked by reduced bone density and structural decay, poses a heightened risk of fractures. Our study formulates a predictive diagnostic model for OP by analyzing differential gene expression, thereby improving early diagnosis and therapeutic approaches. Using GSE62402, GSE56815, and GSE35958 datasets from the Gene Expression Omnibus (GEO) database, we identified differentially expressed genes (DEGs) via R packages, and evaluated the underlying molecular mechanisms by network analysis. Immune checkpoint and drug sensitivity were analyzed to construct and validate diagnostic models. The single-sample gene-set enrichment analysis (ssGSEA) was used to assess immune cell infiltration; the CIBERSORT algorithm was used to evaluate immune cells within the different subtypes of OP. The study identified 1,297 DEGs, with 14 DEGs related to autophagy, osteogenesis, and adipogenesis (AP&OG&AGRDEGs) showing significant expression differences between OP and control groups, including seven upregulated and seven downregulated genes (p-value < 0.05). The analysis results from gene ontology (GO), gene set enrichment analysis (GSEA), and the Kyoto encyclopedia of genes and genomes (KEGG) indicated that oxidative stress and inflammation-related signaling pathways are closely connected to OP. Immune checkpoint analysis identified differential expression of eight genes between OP patients and controls (p-value < 0.05). The ssGSEA findings showed significant variations in immune cell infiltration levels, particularly of natural killer cells, Th2 cells, mast cells, and plasmacytoid dendritic cells (p-value < 0.05). The diagnostic model, developed utilizing logistic regression, support vector machine (SVM), and the least absolute shrinkage and selection operator (LASSO), pinpointed nine pivotal genes-AKT1, NFKB1, TNF, CTNNB1, LMNA, BHLHE40, BMP4, WNT1, and COPS3-and confirmed their diagnostic efficacy through validation. In further subgroup analysis, eight types of immune cells were found to be differentially expressed across various risk groups. Subtype analysis based on ConsensusClusterPlus revealed differential expression of six key genes in distinct subtypes of OP. This comprehensive study established a network of OP-associated genes, and provides insights into the molecular mechanisms involving immune responses in OP. It identified key diagnostic genes and analyzed immune cell infiltration to better understand OP pathogenesis. The study underscores the importance of personalized treatment and the potential role of immune modulation in managing OP.

BackgroundAcute myocardial infarction (AMI) is a fatal disease that causes high morbidity and mortality. It has been reported that AMI is associated with immune cell infiltration. Now, we aimed to identify the potential diagnostic biomarkers of AMI and uncover the immune cell infiltration profile of AMI.MethodsFrom the Gene Expression Omnibus (GEO) data set, three data sets (GSE48060, GSE60993, and GSE66360) were downloaded. Differentially expressed genes (DEGs) from AMI and healthy control samples were screened. Furthermore, DEGs were performed via gene ontology (GO) functional and kyoto encyclopedia of genes and genome (KEGG) pathway analyses. The Gene set enrichment analysis (GSEA) was used to analyze GO terms and KEGG pathways. Utilizing the Search Tool for Retrieval of Interacting Genes/Proteins (STRING) database, a protein–protein interaction (PPI) network was constructed, and the hub genes were identified. Then, the receiver operating characteristic (ROC) curves were constructed to analyze the diagnostic value of hub genes. And, the diagnostic value of hub genes was further validated in an independent data set GSE61144. Finally, CIBERSORT was used to represent the compositional patterns of the 22 types of immune cell fractions in AMI.ResultsA total of 71 DEGs were identified. These DEGs were mainly enriched in immune response and immune-related pathways. Toll-like receptor 2 (TLR2), interleukin-1B (IL1B), leukocyte immunoglobulin-like receptor subfamily B2 (LILRB2), Fc fragment of IgE receptor Ig (FCER1G), formyl peptide receptor 1 (FPR1), and matrix metalloproteinase 9 (MMP9) were identified as diagnostic markers with the value of p < 0.05. Also, the immune cell infiltration analysis indicated that TLR2, IL1B, LILRB2, FCER1G, FPR1, and MMP9 were correlated with neutrophils, monocytes, resting natural killer (NK) cells, gamma delta T cells, and CD4 memory resting T cells. The fractions of monocytes and neutrophils were significantly higher in AMI tissues than in control tissues.ConclusionTLR2, IL1B, LILRB2, FCER1G, FPR1, and MMP9 are involved in the process of AMI, which can be used as molecular biomarkers for the screening and diagnosis of AMI. In addition, the immune system plays a vital role in the occurrence and progression of AMI.

Comprehensive Analysis of angiogenesis associated genes and tumor microenvironment infiltration characterization in cervical cancer

An association study of clock genes with major depressive disorder

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.

Type A aortic dissection (TAAD) is a lethal cardiovascular disease characterized by the separation of the layers within the aortic wall. The underlying pathological mechanisms of TAAD requires further elucidation to develop effective prevention and pharmacological treatment strategies. Inflammation plays a crucial role in TAAD pathogenesis. Disulfidptosis, an emerging type of cell death, may shed light on disease mechanisms. This study investigates the role of disulfidptosis-related genes in immune infiltration in TAAD. TAAD gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. Immune cell infiltration analysis assessed immune cell dysregulation in TAAD. Differentially expressed genes (DEGs) between TAAD samples and controls were identified and intersected with known disulfidptosis-related gene sets to obtain relevant DEGs. Hub genes were identified using machine learning algorithms. A diagnostic model was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) regression on 25 TAAD samples. Consensus clustering classified TAAD samples based on disulfidptosis-related gene expression. Functional enrichment analyses, including Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, elucidated associated biological processes and pathways. A total of 13,316 DEGs were identified, among which 11 disulfidptosis-related genes were screened: INF2, CD2AP, PDLIM1, ACTN4, MYH10, MYH9, FLNA, FLNB, TLN1, MYL6, ACTB, CAPZB, DSTN, and IQGAP1. Most of these genes exhibited lower expression levels in TAAD samples, except CAPZB, and were correlated with immune cell infiltration. Cluster-specific DEGs were found in one cluster, involving several immune response processes. Co-clustering analysis based on disulfidptosis-related genes classified TAAD samples into two clusters, with higher gene expression levels observed in cluster C2 compared to cluster C1. Three key hub genes were identified, and potential therapeutic mechanisms for TAAD were explored. Immuno-infiltration results revealed significant differences in immune profiles, with higher immunological scores and more extensive immune infiltration in TAAD. Disulfidptosis occurs in TAAD and is associated with immune cell infiltration and metabolic activity, influencing immune cell function and responses. These findings suggest that disulfidptosis may promote TAAD progression through the induction of immune responses and metabolic activities. This research provides new insights into the pathogenesis and identifies potential therapeutic targets for TAAD.

Laryngeal cancer (LC) is a malignant tumor that occurs in the head and neck. Laryngeal cancer is one of the most common cancers of the neck and head, and its prognosis has always been poor. The incidence of LC increased gradually and showed an early rising trend. Laryngeal cancer is rarely studied in relation to immunity, Malignant tumors will change the state of the human body in various ways to adapt to their own survival and avoid the immune system. This study aims to explore the immune molecular mechanism of laryngeal cancer through bioinformatics analysis. The gene expression data was downloaded for 3 microarray datasets: GSE27020, GSE59102, and GSE51985. CIBERSORT algorithm was performed to evaluate immune cell infiltration in tissues between LC and healthy control (HC). Differentially expressed genes (DEGs) were screened. Functional correlation of DEGs were analyzed by Gene Ontology, Gene Set Enrichment Analysis and Kyoto encyclopedia of genes and genomes. Candidate biomarkers were identified by cytoHubba of Cytoscape. Spearman correlations between the above biomarkers and infiltrating immune cells were explored using R software analysis. The immune cell types of LC and HC were significantly different. Twenty-one DEGs were obtained by cross-screening. The function of DEGs is closely related to the number of immune cells. Five central genes (TNNT3, TNNI2, Desmin, matrix metallopeptidase 9 and cytotoxic T lymphocyte antigen 4) were screened. The HUB gene was demonstrated to have the ability to diagnose LC and HC with good specificity and sensitivity. The correlation between immune cells and biomarkers showed that hub gene was positively correlated with macrophages and dendritic cells, and negatively correlated with CD4 + T cell. TNNT3, TNNI2, Desmin, matrix metallopeptidase 9 and cytotoxic T lymphocyte antigen 4 can be used as diagnostic biomarker for LC. Macrophages, dendritic cells and CD4 + T cell may participate in the occurrence and development of LC.

ObjectiveOsteonecrosis of the femoral head (ONFH) is a common orthopedic condition that will prompt joint dysfunction, significantly impacting patients’ quality of life. However, the specific pathogenic mechanisms underlying this disease remain elusive. The objective of this study is to examine the differentially expressed messenger RNAs (DE mRNAs) and key genes linked to ONFH, concurrently investigating the immune cell infiltration features in ONFH patients through the application of the CIBERSORT algorithm.MethodsMicroarray was applied to scrutinize mRNA expression profiles in both ONFH patients and healthy controls, with data integration sourced from the GEO database. DE mRNAs were screened using the Limma method. The biological functions of DE mRNAs were explored through the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, Gene Ontology (GO) functional analysis, and Gene Set Enrichment Analysis (GSEA). Additionally, support vector machine–recursive feature elimination (SVM-RFE) and the least absolute shrinkage and selection operator (LASSO) were employed to discern diagnostic biomarkers associated with the disease. Receiver operating characteristic (ROC) analysis was utilized to assess the statistical performance of the feature genes. The validation of key genes was performed using qRT-PCR in bone tissues obtained from ONFH patients and healthy controls. Osteogenic differentiation of BMSC was then performed and detected by alkaline phosphatase staining (ALP) and qRT-PCR to verify the correlation between key genes and osteogenic differentiation. Finally, immune cell infiltration analysis was executed to evaluate immune cell dysregulation in ONFH, concurrently exploring the correlation between the infiltration of immune cells and key genes.ResultsAfter consolidating the datasets, the Limma method revealed 107 DEGs, comprising 76 downregulated and 31 upregulated genes. Enrichment analysis revealed close associations of these DE mRNAs with functions such as cell migration, osteoblast differentiation, cartilage development and extracellular region. Machine learning algorithms further identified APOD, FBXO43 and LRP12 as key genes. ROC curves demonstrated the high diagnostic efficacy of these genes. The results of qRT-PCR showed that the expression levels of key genes were consistent with those of microarray analysis. In addition, the results of in vitro experiments showed that APOD was closely related to osteogenic differentiation of BMSC. Immune infiltration analysis suggested a close correlation between ONFH and imbalances in levels of Neutrophils, Monocytes, Macrophages M2, Dendritic cells activated and Dendritic cells resting.ConclusionAPOD is closely related to osteogenic differentiation of BMSCs and can be used as a diagnostic marker of ONFH. Immune cell infiltration significantly differs between controls and ONFH patients.

AimsHigh immune cell infiltration in gliomas establishes an immunosuppressive tumor microenvironment, which in turn promotes resistance to immunotherapy. Hence, it is important to identify novel targets associated with high immune cell infiltration in gliomas. Our previous study showed that serum levels of beta‐2 microglobulin (B2M) in lower‐grade glioma patients were lower than those in glioblastoma patients. In the present study, we focused on exploring the roles of B2M in glioma immune infiltration.MethodsA large cohort of patients with gliomas from the TCGA, CGGA, and Gravendeel databases was included to explore differential expression patterns and potential roles of B2M in gliomas. A total of 103 glioma tissue samples were collected to determine the distributions of B2M protein levels by immunofluorescent assays. Kaplan‐Meier survival analysis and meta‐analysis were used for survival analysis. GO(Gene‐ontology) enrichment analysis, co‐expression analysis, KEGG(Kyoto Encyclopedia of Genes and Genomes) pathway analysis, and immune infiltration analysis were performed to explore roles and related mechanisms of B2M in glioma.ResultsWe found that both B2M mRNA and protein levels were abnormally upregulated in glioma samples compared with those from normal brain tissue. B2M expression was correlated with tumor grade and was downregulated in IDH1 mutant samples. Furthermore, B2M was a moderately sensitive indicator for predicting the mesenchymal molecular subtype of gliomas. Interestingly, glioma patients with lower B2M expression had remarkably longer survival times than those with higher B2M expression. Moreover, meta‐analysis showed that B2M was an independent predictive marker in glioma patients. The results of GO enrichment analysis revealed that B2M contributed to immune cell infiltration in glioma patients. In addition, results of KEGG pathway analysis and co‐expression analysis suggested that B2M may mediate glioma immune infiltration via chemokines.ConclusionsWe conclude that B2M levels are critical for the survival times of glioma patients, at least in part due to mediating high immune infiltration.

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.