Immune Infiltration Analysis Research Articles

Identification and Analysis of Potential Biomarkers Associated with Neutrophil Extracellular Traps in Cervicitis.

Early diagnosis of cervicitis is important. Previous studies have found that neutrophil extracellular traps (NETs) play pro-inflammatory and anti-inflammatory roles in many diseases, suggesting that they may be involved in the inflammation of the uterine cervix and NETs-related genes may serve as biomarkers of cervicitis. However, what NETs-related genes are associated with cervicitis remains to be determined. Transcriptome analysis was performed using samples of exfoliated cervical cells from 15 patients with cervicitis and 15 patients without cervicitis as the control group. First, the intersection of differentially expressed genes (DEGs) and neutrophil extracellular trap-related genes (NETRGs) were taken to obtain genes, followed by functional enrichment analysis. We obtained hub genes through two machine learning algorithms. We then performed Artificial Neural Network (ANN) and nomogram construction, confusion matrix, receiver operating characteristic (ROC), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Moreover, we constructed ceRNA network, mRNA-transcription factor (TF) network, and hub genes-drug network. We obtained 19 intersecting genes by intersecting 1398 DEGs and 136 NETRGs. 5 hub genes were obtained through 2 machine learning algorithms, namely PKM, ATG7, CTSG, RIPK3, and ENO1. Confusion matrix and ROC curve evaluation ANN model showed high accuracy and stability. A nomogram containing the 5 hub genes was established to assess the disease rate in patients. The correlation analysis revealed that the expression of ATG7 was synergistic with RIPK3. The GSEA showed that most of the hub genes were related to ECM receptor interactions. It was predicted that the ceRNA network contained 2 hub genes, 3 targeted miRNAs, and 27 targeted lnRNAs, and that 5 mRNAs were regulated by 28 TFs. In addition, 36 small molecule drugs that target hub genes may improve the treatment of cervicitis. In this study, five hub genes (PKM, ATG7, CTSG, RIPK3, ENO1) provided new directions for the diagnosis and treatment of patients with cervicitis.

Pathomics models for CD40LG expression and prognosis prediction in glioblastoma

Glioblastoma (GBM) is the most prevalent primary malignant tumor of the nervous system. In this study, we utilized pathomics analysis to explore the expression of CD40LG and its predictive value for the prognosis of GBM patients. We analyzed the expression differences of CD40LG in GBM tissue and normal brain tissue, along with performing survival prognosis analysis. Additionally, histopathological sections of GBM were used to screen for pathological features. Subsequently, SVM and LR pathomics models were constructed, and the models’ performance was evaluated. The pathomics model was employed to predict CD40LG expression and patient prognosis. Furthermore, we investigated the potential molecular mechanisms through enrichment analysis, WGCNA analysis, immune correlation analysis, and immune checkpoint analysis. The expression level of CD40LG was significantly increased in GBM. Multivariate analysis demonstrated that high expression of CD40LG is a risk factor for overall survival (OS) in GBM patients. Five pathological features were identified, and SVM and LR pathomics models were constructed. Model evaluation showed promising predictive effects, with an AUC value of 0.779 for the SVM model, and the Hosmer–Lemeshow test confirmed the model’s prediction probability consistency (P > 0.05). The LR model achieved an AUC value of 0.785, and the Hosmer–Lemeshow test indicated good agreement between the LR model’s predicted probabilities and the true value (P > 0.05). Immune infiltration analysis revealed a significant correlation between the pathomics score (PS) and the degree of infiltration of activated DC cells, T cells CD4 naïve, Macrophages M2, Macrophages M1, and T cells CD4 memory resting. Our results demonstrate that the pathomics model exhibits predictability for CD40LG expression and GBM patient survival. These findings can be utilized to assist neurosurgeons in selecting optimal treatment strategies in clinical practice.

Key genes and immune pathways in T-cell mediated rejection post-liver transplantation identified via integrated RNA-seq and machine learning

Liver transplantation is the definitive treatment for end-stage liver disease, yet T-cell mediated rejection (TCMR) remains a major challenge. This study aims to identify key genes associated with TCMR and their potential biological processes and mechanisms. The GSE145780 dataset was subjected to differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms to pinpoint key genes associated with TCMR. Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, and regulatory networks were constructed to ascertain the biological relevance of these genes. Expression validation was performed using single-cell RNA-seq (scRNA-seq) data and liver biopsy tissues from patients. We identified 5 key genes (ITGB2, FCER1G, IL-18, GBP1, and CD53) that are associated with immunological functions, such as chemotactic activity, antigen processing, and T cell differentiation. GSEA highlighted enrichment in chemokine signaling and antigen presentation pathways. A lncRNA-miRNA-mRNA network was delineated, and drug target prediction yielded 26 potential drugs. Evaluation of expression levels in non-rejection (NR) and TCMR groups exhibited significant disparities in T cells and myeloid cells. Tissue analyses from patients corroborated the upregulation of GBP1, IL-18, CD53, and FCER1G in TCMR cases. Through comprehensive analysis, this research has identified 4 genes intimately connected with TCMR following liver transplantation, shedding light on the underlying immune activation pathways and suggesting putative targets for therapeutic intervention.

Association of aging related genes and immune microenvironment with major depressive disorder

ObjectiveTo study the relationship between aging related genes (ARGs) and Major Depressive Disorder (MDD). MethodsThe datasets GSE98793, GSE52790 and GSE39653 for MDD were obtained from the GEO database, and ARGs were obtained from the Human Aging Genome Resources database. Differential expression genes (DEGs) screening and GO, KEGG enrichment analysis were performed to uncover the underlying mechanisms. To identify key ARGs associated with MDD (key ARG-DEGs), we employed machine learning methods such as LASSO, SVM, and Random Forest, as well as the plug-ins CytoHubba-MCC and MCODE methods. SsGSEA was used to analyze the immune infiltration of MDD and healthy controls. Furthermore, we created risk prediction nomograms model and ROC curves to assess not only the ability of key ARG-DEGs to diagnose MDD, but also predicted miRNAs and transcription factors (TFs) that might interact. Finally, a two-sample Mendelian randomization (MR) study was performed to confirm the association of identified key ARG-DEGs with depression. ResultsDEGs of ARGs between MDD and healthy controls led to the identification of eight ARG-DEGs. GO and KEGG analysis revealed that the pathways associated with these eight ARG-DEGs were primarily concentrated in Foxo pathway, JAK-STAT pathway, Pl3K-AKT pathway, and metabolic diseases. A comprehensive analysis further narrowed down the 8 ARG-DEGs to 4 key ARG-DEGs: MMP9, IL7R, S100B, and EGF. Immune infiltration analysis indicated significant differences in CD8(+) T cells, macrophages, neutrophils, Th2 cells, and TIL cells between MDD and control groups, correlating with these four key ARG-DEGs. Based on these four key ARG-DEGs, a risk prediction model for MDD was developed. The miRNA-TF-mRNA interaction network of the key ARG-DEGs highlights the complexity of the regulatory process, providing valuable insights for future related research. The MR study suggested a potential causal relationship between MMP9 and the risk of depression. ConclusionThe process of aging, immune dysregulation, and MDD are closely interconnected. MMP9, IL7R, S100B, and EGF may be used as novel diagnostic biomarkers and potential therapeutic targets for MDD, especially MMP9.

Identification of immune-related gene signature model for predicting lung cancer survival and response to immunotherapy.

Studies have shown that immune-related genes play a crucial role in tumor development and treatment. However, the specific roles and potential value of these genes in lung cancer patients are still not fully understood. Therefore, this study aims to establish a novel risk model based on immune-related genes for evaluating the prognostic risk and response to immune therapy in lung cancer patients. Gene expression and clinical data of lung cancer patients were retrieved from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, while immune-related genes were obtained from the ImmPort database. A risk signature model was developed using univariate Cox analysis and LASSO regression analysis. The prognostic value of the model and its response to immunotherapy were analyzed by survival analysis, immune infiltration analysis and immunotherapy response analysis. We have developed a risk signature model based on eight key immune-related genes, which can classify patients into high-risk and low-risk groups. The prognosis of the high-risk group was significantly lower than that of the low-risk group and was validated in multiple GEO datasets. The mutation frequency was lower in the low-risk group compared to the high-risk group (TP53: 55% vs 65%; TTN: 52% vs 60%; CSMD3: 34% vs 45%). Futhermore, CD274 expression was lower in the low-risk patients, and the high-risk patients in the IMvigor210 cohort had lower survival. Immune infiltration analyses showed that the high-risk group was negatively correlated with the infiltration level of B cells, CD4+ T cells, and NK cells. Importantly, patients in the low-risk group exhibit significantly lower TIDE scores, suggesting that they are more responsive to immunotherapy. Our study has established a novel and robust immune-related gene risk model that can assist in evaluating the prognostic risk and immune therapy response of lung cancer patients.

Identification of potential therapeutic targets for systemic lupus erythematosus based on GEO database analysis and Mendelian randomization analysis.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease. Current treatments mainly rely on immunosuppressants, which lack specificity and pose challenges during treatment. This study aims to deeply explore the molecular pathogenic mechanism of SLE through gene expression databases (GEO) and bioinformatics analysis methods, combined with Mendelian randomization analysis, to provide key clues for new therapeutic targets. In this study, the SLE-related gene chip dataset GSE65391 was selected from the GEO database, and the data were preprocessed and statistically analyzed using R language and bioinformatics tools. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), GO, and KEGG enrichment analysis were used to screen differentially expressed genes (DEGs) for functional annotation and pathway localization. Furthermore, Mendelian randomization analysis was conducted to identify core genes closely related to SLE risk, and immune cell infiltration analysis and compound molecular docking studies were performed on the core gene ISG15. The study successfully screened 3,456 DEGs and identified core gene modules highly related to SLE through WGCNA analysis, including key genes closely related to the pathogenesis of SLE, such as STAT1, DDX58, ISG15, IRF7, and IFIH1. In particular, this study found a significant positive correlation between the ISG15 gene and SLE, suggesting that it may be a potential risk factor for SLE. Additionally, through molecular docking technology, it was discovered that the ISG15 gene can effectively bind to two compounds, genistein, and flavopiridol, which have anti-inflammatory and immunosuppressive effects, respectively. This provides new potential drug targets for SLE treatment. As an immunomodulatory cytokine, ISG15 plays a crucial role in the pathogenesis of SLE. This study found that variations in the ISG15 gene may increase the risk of SLE and exacerbate inflammatory responses and tissue damage through multiple mechanisms. Furthermore, molecular docking revealed that genistein and flavopiridol can effectively bind to ISG15, offering a new approach for SLE treatment. These two compounds, with their anti-inflammatory and immunosuppressive properties, have the potential to slow the progression of SLE by influencing the expression and function of ISG15. Through comprehensive bioinformatics analysis and Mendelian randomization analysis, this study deeply explored the molecular pathogenic mechanism of SLE and successfully identified ISG15 as a potential therapeutic target for SLE. Simultaneously, molecular docking technology revealed that two compounds, genistein and flavopiridol, have potential therapeutic effects with ISG15, providing new potential drugs for SLE treatment. These discoveries not only enhance our understanding of the pathogenesis of SLE but also provide important clues for developing new treatment strategies.

Machine learning-derived peripheral blood transcriptomic biomarkers for early lung cancer diagnosis: Unveiling tumor-immune interaction mechanisms.

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection and a comprehensive understanding of tumor-immune interactions are crucial for improving patient outcomes. This study aimed to develop a novel biomarker panel utilizing peripheral blood transcriptomics and machine learning algorithms for early lung cancer diagnosis, while simultaneously providing insights into tumor-immune crosstalk mechanisms. Leveraging a training cohort (GSE135304), we employed multiple machine learning algorithms to formulate a Lung Cancer Diagnostic Score (LCDS) based on peripheral blood transcriptomic features. The LCDS model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) in multiple validation cohorts (GSE42834, GSE157086, and an in-house dataset). Peripheral blood samples were obtained from 20 lung cancer patients and 10 healthy control subjects, representing an in-house cohort recruited at the Sixth People's Hospital of Chengdu. We employed advanced bioinformatics techniques to explore tumor-immune interactions through comprehensive immune infiltration and pathway enrichment analyses. Initial screening identified 844 differentially expressed genes, which were subsequently refined to 87 genes using the Boruta feature selection algorithm. The random forest (RF) algorithm demonstrated the highest accuracy in constructing the LCDS model, yielding a mean AUC of 0.938. Lower LCDS values were significantly associated with elevated immune scores and increased CD4+ and CD8+ T-cell infiltration, indicative of enhanced antitumor-immune responses. Higher LCDS scores correlated with activation of hypoxia, peroxisome proliferator-activated receptor (PPAR), and Toll-like receptor (TLR) signaling pathways, as well as reduced DNA damage repair pathway scores. Our study presents a novel, machine learning-derived peripheral blood transcriptomic biomarker panel with potential applications in early lung cancer diagnosis. The LCDS model not only demonstrates high accuracy in distinguishing lung cancer patients from healthy individuals but also offers valuable insights into tumor-immune interactions and underlying cancer biology. This approach may facilitate early lung cancer detection and contribute to a deeper understanding of the molecular and cellular mechanisms underlying tumor-immune crosstalk. Furthermore, our findings on the relationship between LCDS and immune infiltration patterns may have implications for future research on therapeutic strategies targeting the immune system in lung cancer.

Identification of immune feature genes and intercellular profiles in diabetic cardiomyopathy

BACKGROUND Diabetic cardiomyopathy (DCM) is a multifaceted cardiovascular disorder in which immune dysregulation plays a pivotal role. The immunological molecular mechanisms underlying DCM are poorly understood. AIM To examine the immunological molecular mechanisms of DCM and construct diagnostic and prognostic models of DCM based on immune feature genes (IFGs). METHODS Weighted gene co-expression network analysis along with machine learning methods were employed to pinpoint IFGs within bulk RNA sequencing (RNA-seq) datasets. Single-sample gene set enrichment analysis (ssGSEA) facilitated the analysis of immune cell infiltration. Diagnostic and prognostic models for these IFGs were developed and assessed in a validation cohort. Gene expression in the DCM cell model was confirmed through real time-quantitative polymerase chain reaction and western blotting techniques. Additionally, single-cell RNA-seq data provided deeper insights into cellular profiles and interactions. RESULTS The overlap between 69 differentially expressed genes in the DCM-associated module and 2483 immune genes yielded 7 differentially expressed immune-related genes. Four IFGs showed good diagnostic and prognostic values in the validation cohort: Proenkephalin (Penk) and retinol binding protein 7 (Rbp7), which were highly expressed, and glucagon receptor and inhibin subunit alpha, which were expressed at low levels in DCM patients (all area under the curves > 0.9). SsGSEA revealed that IFG-related immune cell infiltration primarily involved type 2 T helper cells. High expression of Penk (P < 0.0001) and Rbp7 (P = 0.001) was detected in cardiomyocytes and interstitial cells and further confirmed in a DCM cell model in vitro . Intercellular events and communication analysis revealed abnormal cellular phenotype transformation and signaling communication in DCM, especially between mesenchymal cells and macrophages. CONCLUSION The present study identified Penk and Rbp7 as potential DCM biomarkers, and aberrant mesenchymal-immune cell phenotype communication may be an important aspect of DCM pathogenesis.

Mitophagy related diagnostic biomarkers for coronary in-stent restenosis identified using machine learning and bioinformatics

Percutaneous coronary intervention (PCI) combined with stent implantation is currently one of the most effective treatments for coronary artery disease (CAD). However, in-stent restenosis (ISR) significantly compromises its long-term efficacy. Mitophagy plays a crucial role in vascular homeostasis, yet its role in ISR remains unclear. This study aims to identify mitophagy-related biomarkers for ISR and explore their underlying molecular mechanisms. Through differential gene expression analysis between ISR and Control samples in the combined dataset, 169 differentially expressed genes (DEGs) were identified. Twenty-three differentially expressed mitophagy-related genes (DEMRGs) were identified by intersecting with mitophagy-related genes (MRGs) from the GeneCards, and functional enrichment analysis indicated their significant involvement in mitophagy-related biological processes. Using Weighted Gene Co-expression Network Analysis (WGCNA) and three machine learning algorithms (Logistic-LASSO, RF, and SVM-RFE), LRRK2, and ANKRD13A were identified as mitophagy-related biomarkers for ISR. The nomogram based on these two genes also exhibited promising diagnostic performance for ISR. Gene Set Enrichment Analysis (GSEA) as well as immune infiltration analyses showed that these two genes were closely associated with immune and inflammatory responses in ISR. Furthermore, potential small molecule compounds with therapeutic implications for ISR were predicted using the connectivity Map (cMAP) database. This study systematically investigated mitophagy-related biomarkers for ISR and their potential biological functions, providing new insights into early diagnosis and precision treatment strategies for ISR.

CTPAD: an interactive web application for comprehensive transcriptomic profiling in allergic diseases

BackgroundAllergic diseases are systemic chronic inflammatory diseases associated with multiorgan damage and complex pathogenesis. Several studies have revealed the association of gene expression abnormalities with the development of allergic diseases, but the biomedical field still lacks a public platform for comprehensive analysis and visualization of transcriptomic data of allergic diseases.ObjectiveThe aim of the study is to provide a comprehensive web tool for multiple analysis in allergic diseases.MethodsWe retrieved and downloaded human and mouse gene expression profile data associated with allergic diseases from the Gene Expression Omnibus (GEO) database and standardized the data uniformly. We used gene sets obtained from the MSigDB database for pathway enrichment analysis and multiple immune infiltration algorithms for the estimation of immune cell proportion. The basic construction of the web pages was based on the Shiny framework. Additionally, more convenient features were added to the server to improve the efficiency of the web pages, such as jQuery plugins and a comment box to collect user feedback.ResultsWe developed CTPAD, an interactive R Shiny application that integrates public databases and multiple algorithms to explore allergic disease-related datasets and implement rich transcriptomic visualization capabilities, including gene expression analysis, pathway enrichment analysis, immune infiltration analysis, correlation analysis, and single-cell RNA sequencing analysis. All functional modules offer customization options and can be downloaded in PDF format with high-resolution images.ConclusionsCTPAD largely facilitates the work of researchers without bioinformatics background to enable them to better explore the transcriptomic features associated with allergic diseases. CTPAD is available at https://smuonco.shinyapps.io/CTPAD/.

Integrated machine learning and Mendelian randomization reveal PALMD as a prognostic biomarker for nonspecific orbital inflammation

Background: Nonspecific Orbital Inflammation (NSOI) remains a perplexing enigma among proliferative inflammatory disorders. Its etiology is idiopathic, characterized by distinctive and polymorphous lymphoid infiltration within the orbital region. Preliminary investigations suggest that PALMD localizes within the cytosol, potentially playing a crucial role in cellular processes, including plasma membrane dynamics and myogenic differentiation. The potential of PALMD as a biomarker for NSOI warrants meticulous exploration. Methods: PALMD was identified through the intersection analysis of common DEGs from datasets GSE58331 and GSE105149 from the GEO database, alongside immune-related gene lists from the ImmPort database, using Lasso regression and SVM-RFE analysis. GSEA and GSVA were conducted with gene sets co-expressed with PALMD. To further investigate the correlation between PALMD and immune-related biological processes, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate immune microenvironment characteristics of each sample. The expression levels of PALMD were subsequently validated using GSE105149. Results: Among the 314 DEGs identified, several showed significant differences. Lasso and SVM-RFE algorithms pinpointed 15 hub genes. Functional analysis of PALMD emphasized its involvement in cell-cell adhesion, leukocyte migration, and leukocyte-mediated immunity. Enrichment analysis revealed that gene sets positively correlated with PALMD were enriched in immune-related pathways. Immune infiltration analysis indicated that resting dendritic cells, resting mast cells, activated NK cells, and plasma cells positively associate with PALMD expression. Conversely, naive B cells, activated dendritic cells, M0 and M1 macrophages, activated mast cells, activated CD4 memory T cells, and naive CD4 T cells showed a negative correlation with PALMD expression. PALMD demonstrated significant diagnostic potential in differentiating NSOI. Conclusions: This study identifies PALMD as a potential biomarker linked to NSOI, providing insights into its pathogenesis and offering new avenues for tracking disease progression.

Integrated Analysis of Serum and Tissue microRNA Transcriptome for Biomarker Discovery in Gastric Cancer.

Gastric cancer (GC) poses a significant global health challenge, demanding a detailed exploration of its molecular landscape. Studies suggest that exposure to environmental pollutants can lead to changes in microRNA (miRNA) expression patterns, which may contribute to the development and progression of GC. MiRNAs have emerged as crucial regulators implicated in GC pathogenesis. The largest GC serum miRNA dataset to date, comprising 1417 non-cancer controls and 1417 GC samples was used. We conducted a comprehensive analysis of miRNA expression profiles. Differential expression analysis, co-expression network construction, and machine learning models were employed to identify key serum miRNAs and their association with clinical parameters. Weighted Gene Co-expression Network Analysis (WGCNA) and immune infiltration analysis were used to validate the importance of the key miRNA. A total of 1766 differentially expressed miRNAs were identified, with miR-1290, miR-1246, and miR-451a among the top up-regulated, and miR-6875-5p, miR-6784-5p, miR-1228-5p, and miR-6765-5p among the top down-regulated. WGCNA revealed that modules M1 and M5 were significantly associated with GC subtypes and disease status. MiRNA-target gene network analysis identified prognostically significant genes TP53, EMCN, CBX8, and ALDH1A3. Machine learning models LASSO, SVM, randomforest, and XGBOOST demonstrated the diagnostic potential of miRNA profiles. Tissue and serum miR-187 emerged as an independent prognostic factor, influencing patient survival across clinical parameters. Gene expression and immune cell infiltration were different in tissues stratified by miR-187 expression. In summary, the integration of differential gene expression, co-expression analysis, and immune cell profiling provided insights into the molecular intricacies of GC progression.

Identification of novel biomarkers for atherosclerosis using single-cell RNA sequencing and machine learning.

Atherosclerosis (AS) is a predominant etiological factor in numerous cardiovascular diseases, with its associated complications such as myocardial infarction and stroke serving as major contributors to worldwide mortality rates. Here, we devised dependable AS-related biomarkers through the utilization of single-cell RNA sequencing, weighted co-expression network (WGCNA), and differential expression analysis. Furthermore, we employed various machine learning techniques (LASSO and SVM-RFE) to enhance the identification of AS biomarkers, subsequently validating them using the GEO dataset. Following this, CIBERSORT was employed to investigate the correlation between biomarkers and infiltrating immune cells. Consequently, 256 differentially expressed genes (DEGs) were selected in samples of AS and normal. GO and KEGG analyses indicated that these DEGs may be related to the negative regulation of leukocyte-mediated immunity, leukocyte cell-cell adhesion, and immune system processes. Notably, C1QC and COL1A1 were pinpointed as potential diagnostic markers for AS, a finding that was further validated in the GSE21545 dataset. Moreover, the area under the curve (AUC) values for these markers exceeded 0.8, underscoring their diagnostic utility. Analysis of immune cell infiltration revealed that the expression of C1QC was correlated with M0 macrophages, gamma delta T cells, activated mast cells and memory B cells. Similarly, COL1A1 expression was linked to M0 macrophages, memory B cells, activated mast cells, gamma delta T cells, and CD4 native T cells. Finally, these results were validated using mice and human samples through immunofluorescence, immunohistochemistry, and ELISA analysis. Overall, C1QC and COL1A1 would be potential biomarkers for AS diagnosis, and that would provides novel perspectives on the diagnosis and treatment of AS.

Bioinformatics analysis of immune infiltration in human diabetic retinopathy and identification of immune-related hub genes and their ceRNA networks

Diabetic retinopathy (DR) is the most common microvascular complication in diabetic patients, and recent studies have shown that immune regulatory mechanisms are closely associated with retinal damage in DR. Therefore, this study focused on exploring immune cells and immune-related genes (IRGs) in DR and gaining insight into the ceRNA mechanisms by which IRGs regulate DR progression. Four datasets from human DR model retinal tissues were obtained from the Gene Expression Omnibus (GEO) database. R software was first used to identify differentially expressed mRNAs (DE-mRNAs) in the dataset GSE160306-mRNAs, then the distribution of immune cells in the gene matrix was analyzed by xCell and ImmuCellAI, ImmPort and InnateDB database were used to obtain immune-related hub genes (IRHGs) in the DR, and finally the STRING online tool and Cytoscape to construct the immune-related ceRNA network. The datasets GSE102485, GSE160308 and GSE160306-lncRNAs were used to validate the results of the ceRNA network further. The results of immune cell infiltration analysis showed that macrophages are important immune cells in DR; immune-related gene screening showed that FCGR2B is an IRHG in DR, and 2 immune-related ceRNA networks of IRHG were obtained: DDN-AS1/miR-10a-5p/FCGR2B and LINC01515/miR-10a-5p/FCGR2B. Our study suggests that infiltration of immune cells, especially the immune role of macrophages, is an important component of DR progression; the immune-related hub gene FCGR2B and its ceRNA network may be a key regulatory network for DR progression. The discovery of key immune cells, IRHG and ceRNA networks in this study may provide new prospects for early intervention and targeted treatment of DR.

Identification of neutrophil extracellular traps genes as potential biomarkers in psoriasis based on bioinformatics analysis

The epidermal infiltration of neutrophils is a hallmark of psoriasis (PSO) and its activation leads to the release of neutrophil extracellular traps (NETs). However, the molecular mechanism of NETs-related genes (NETRGs) has not been extensively studied in PSO. To define NETs-related-biomarkers for PSO. The GSE13355 and GSE78097 datasets, and NETRGs gene set were included in this study. The datasets used in this study were all microarray data. The weighted gene co-expression network analysis (WGCNA) and machine learning algorithms were used to mine key genes. Later on, single-gene gene set enrichment analysis (GSEA) and immune infiltration analysis were implemented. Finally, the expression of key genes was verified using quantitative real-time fluorescence PCR (qRT-PCR). A total of 3 key genes (S100A9, CLEC7A, and CXCR4) were derived, and they all had excellent diagnostic performance. The single-gene GSEA enrichment results indicated that the key genes were mainly enriched in the chemokine signaling pathway and humoral immune response in the high-expression group, while focal adhesion was enriched in the low-expression group. The correlation analysis indicated that all key genes were strongly negatively correlated with resting mast cells and TGF-β family member receptor, while they were strongly positively correlated with activated CD4 memory T cells and antigen processing and presentation. Lastly, the experimental results showed that the expression trends of key genes were consistent with public database. In this study, we successfully screened three potential PSO diagnostic genes (S100A9, CLEC7A and CXCR4) that were closely related to NETs, and these findings not only provided new molecular marker candidates for the precise diagnosis of PSO patients, but also revealed possible future therapeutic targets. However, further in-depth research and validation were necessary.

Combining bioinformatics and machine learning to identify diagnostic biomarkers of TB associated with immune cell infiltration

ObjectiveThe asymptomatic nature of tuberculosis (TB) during its latent phase, combined with limitations in current diagnostic methods, makes accurate diagnosis challenging. This study aims to identify TB diagnostic biomarkers by integrating gene expression screening with machine learning, evaluating their diagnostic potential and correlation with immune cell infiltration. MethodsWe analyzed GSE19435, GSE19444, and GSE54992 datasets to identify differentially expressed genes (DEGs). GO and KEGG enrichment characterized gene functions. Three machine learning algorithms identified potential biomarkers, validated with GSE83456, GSE62525, and RT-qPCR on clinical samples. Immune cell infiltration was analyzed and verified with blood data. Results249 DEGs were identified, with PDE7A and DOK3 emerging as potential biomarkers. RT-qPCR confirmed their expression, showing AUCs above 0.75 and a combined AUC of 0.926 for TB diagnosis. Immune infiltration analysis revealed strong correlations between PDE7A, DOK3, and immune cells. ConclusionPDE7A and DOK3 show strong diagnostic potential for TB, closely linked to immune cell infiltration, and may serve as promising biomarkers and therapeutic targets.

Unveiling ficolins: diagnostic and prognostic biomarkers linked to the Tumor Microenvironment in Lung Cancer

BackgroundFicolins (FCNs) are a family of proteins, comprising FCN1, FCN2 and FCN3, and integral to the immune system which have been implicated in the onset and progression of tumors. Despite their recognized roles, a comprehensive analysis of FCNs in lung cancer remains elusive.MethodsWe employed a variety of bioinformatics tools, including UCSC, SangerBox, Ualcan, cBioPortal, String, Metascape, GeneMANIA, TIDE, CTD, and CAMP databases to investigate the differential expression, diagnostic and prognostic significance, genetic alterations, functional enrichment, immune infiltration, and potential immunotherapeutic implications of FCN1, FCN2, and FCN3 in lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). Additionally, RT-qPCR and immunohistochemistry were utilized to validate the expressions of FCNs at the mRNA and protein levels in LUSC and LUAD.ResultsOur comprehensive bioinformatic analysis, supported by RT-qPCR and immunohistochemistry, revealed that the expressions of FCN1, FCN2 and FCN3 were consistently downregulated in both LUSC and LUAD tumor tissues. FCNs demonstrated significant diagnostic potential for LUSC and LUAD, with the area under the receiver operating characteristic curve (AUC) for FCN1 and FCN3 exceeding 0.90. Furthermore, FCN2 and FCN3 showed a strong negative correlation with overall survival (OS) in LUSC, whereas FCN1 and FCN2 were positively correlated with OS in LUAD, suggesting their prognostic value in lung cancer. Gene enrichment analysis indicated that FCNs were predominantly associated with the complement system and complement activation pathways. Immune infiltration analysis further revealed a significant positive correlation between FCNs and the presence of neutrophils and resting mast cells. Our analysis of immunotherapy outcomes revealed a significant disparity in the immunophenoscore (IPS) among lung cancer patients treated with immune checkpoint inhibitors (ICIs), distinguishing those with high FCN expression from those with low FCN expression. Additionally, we identified small molecule compounds related to FCNs and drugs pertinent to LUSC and LUAD.ConclusionFCNs held promise as diagnostic and prognostic biomarkers for LUSC and LUAD. This study also elucidated the relationship of FCNs with the tumor microenvironment, offering novel insights into the immunotherapeutic landscape for LUSC and LUAD.

Bioinformatics analysis of therapeutic targets for idiopathic pulmonary fibrosis and exploration of immune cell infiltration patterns

Idiopathic pulmonary fibrosis (IPF) is a severe progressive lung disease characterized by fibrotic changes in lung tissue, with limited treatment options. This study analyzed gene expression data from three gene expression omnibus datasets (GSE2052, GSE53845, and GSE110147) using R and LIMMA to identify differentially expressed genes (DEGs) in IPF samples. We identified 215 DEGs, comprising 106 upregulated and 109 downregulated genes. Weighted gene coexpression network analysis revealed five gene modules, with the module eigengene yellow showing the strongest correlation with IPF. Functional enrichment analysis of 40 consensus genes in this module indicated their significant involvement in extracellular matrix (ECM) organization. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed pathways related to protein digestion, cell adhesion molecules, and the advanced glycation end product–receptor for advanced glycation end product signaling pathway. Based on protein–protein interaction network analysis, collagen type XV alpha 1 chain (COL15A1) and collagen type VI alpha 3 chain (COL6A3) were identified as upregulated hub genes in IPF, which were regulated by microRNAs and transcription factors. Immune cell infiltration analysis showed significant changes in immune cell populations in IPF samples, with increases in memory B cells, plasma cells, and M0 macrophages and decreases in CD8 T cells, and resting natural killer cells. Potential drugs targeting COL15A1 and COL6A3 were predicted using multiple databases, revealing compounds such as (+)-JQ1, aristolochic acid I, and dexamethasone with promising binding potential. These findings suggest that COL15A1 and COL6A3 are central hub genes in IPF, are associated with ECM organization and immune response, and serve as therapeutic targets for IPF.

Identification and Verification of Potential Markers Related to Myocardial Fibrosis by Bioinformatics Analysis.

Mounting evidence indicates that myocardial fibrosis (MF) is frequently intertwined with immune and metabolic disorders. This comprehensive review aims to delve deeply into the crucial role of immune-related signature genes in the pathogenesis and progression of MF. This exploration holds significant importance as understanding the underlying mechanisms of MF is essential for developing effective diagnostic and therapeutic strategies. The dataset GSE9735 about myocardial fibrosis and non-fibrosis was downloaded from GEO database. Differentially expressed genes (DEGs) were identified by 'limma' package in R software. Then, the biological function of DEG was determined by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. XCell was used to estimate the composition pattern of matrix and immune cells. Protein-protein interaction (PPI) network was constructed based on STRING analysis software, and Hub genes were screened and functional modules were analyzed. The correlation between hub genes and immune cell subtypes was analyzed. Hub genes with |correlation coefficient|> 0.45 and p-value < 0.05 were used as characteristic biomarkers. Finally, the logistic regression model is used to verify the three markers in the training set and verification set (GSE97358 and GSE225336). A total of 635 DEGs were identified. Functional enrichment analysis shows that inflammation and immune response, extracellular matrix and structural remodeling play an important role in the pathological mechanism of MF. Immune cell infiltration analysis showed that immune cells (Plasma cells, Eosinophils, Chondrocytes and Th2 cells) significantly changed in MF pathological conditions. In PPI network analysis, IL1β, TTN, PTPRC, IGF1, ALDH1A1, CYP26A1, ALDH1A3, MYH11, CSF1R and CD80 were identified as hub genes, among which IL1β, CYP26A1 and GNG2 were regarded as immune-related characteristic markers. The AUC scores of the three biomarkers are all above 0.65, which proves that they have a good discrimination effect in MF. In this study, three immune-related genes were identified as diagnostic biomarkers of MF, which provided a new perspective for exploring the molecular mechanism of MF. This study takes a comprehensive approach to understanding the intricate relationship between myocardial fibrosis and immune metabolism. By identifying key immune-related biomarkers, this study not only reveals the molecular basis of myocardial fibrosis but also paves the way for the development of novel diagnostic tools and therapeutic strategies. These findings are critical for improving patient prognosis and may have broader implications for studying and treating other cardiovascular diseases associated with immune dysregulation.

A comprehensive analysis of CEBPA on prognosis and function in uterine corpus endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is one of the most common tumours of the female reproductive system. CCAAT enhancer-binding protein alpha (CEBPA) is a member of the transcription factor family involved in regulating processes such as cell proliferation, differentiation, metabolism, and the immune response. However, the role of CEBPA in UCEC has not been clarified. Here, we performed a comprehensive analysis to explore the expression level, prognostic value, immune infiltration and biological function of CEBPA in UCEC. In this study, we found that CEBPA expression was upregulated and associated with poor prognosis in UCEC patients. KEGG and GO analyses revealed that the genes positively correlated with CEBPA were enriched primarily in immune regulation and oxidative phosphorylation. Immune infiltration analysis revealed that CEBPA is strongly correlated with immune cell infiltration in UCEC. RT-qPCR indicated that CEBPA may regulate the OXPHOS level in Ishikawa cells. CCK-8, cell cycle, Transwell and scratch wound healing assays revealed that CEBPA promoted Ishikawa cell proliferation, invasion and migration. In addition, PPI and survival analyses suggested that CEBPG may be a potential target of CEBPA in UCEC. These results demonstrated that CEBPA may be a potential therapeutic target in UCEC.