Immune-related Differentially Expressed Genes Research Articles

Characterization of Immune-Related circRNAs and mRNAs in Human Chronic Atrophic Gastritis.

Chronic atrophic gastritis (CAG) is a severe condition characterized by inflammation and loss of appropriate mucosal glands in the stomach. The underlying mechanisms of CAG development remain unclear. Exploring immune-related circular RNAs (circRNAs) could provide insights for potential diagnostic and therapeutic strategies. Samples from 40 patients with CAG and non-CAG (CNAG) underwent high-throughput sequencing, and EdgeR analysis identified differentially expressed circRNAs and mRNAs. Gene Ontology (GO) analysis elucidated biological functions, while Immune Cell Abundance Identifier (ImmuCellAI) estimated immune cell abundance. Flow cytometry analyzed immune cell infiltration. Weighted gene co-expression network analysis (WGCNA) identified hub genes related to the immune response in CAG. CircRNA-mRNA networks were constructed, and qRT-PCR validated findings. A total of 163 differentially expressed immune-related genes (DEIRGs) were identified between CAG and CNAG. The upregulated immune-related mRNAs in CAG were significantly enriched in antimicrobial humoral response, viral entry into host cells, neutrophil activation, and leukocyte migration. Conversely, downregulated immune-related mRNAs were linked to regulation of natural killer cell-mediated cytotoxicity, positive regulation of adaptive immune response, antigen receptor-mediated signaling pathway, and B cell activation. Immune Cell Abundance Identifier (ImmuCellAI) and flow cytometry confirmed increased neutrophil infiltration in CAG compared to CNAG. WGCNA identified 56hub immune-related genes. Additionally, circRNA expression profiles in CNAG and CAG were explored, with 19 upregulated and 23 downregulated circRNAs identified in CAG. The upregulated circRNAs were associated with biological processes like carnitine metabolic process and regulation of B cell receptor signaling pathway. A circRNA-mRNA co-expression network was constructed based on five circRNAs highly related to hub immune-related genes. Furthermore, the expression of eight immune-related mRNAs and five circRNAs were validated in CAG. This study is the first systematic analysis of circRNA profiles in CAG and provide important insights for potential immunotherapeutic strategies and early diagnostic biomarkers in CAG treatment.

Galectin-9 activates host immune response and improve immunoprotection of Onychostoma macrolepis against Aeromonas hydrophila infection

Galectin-9 (Gal-9) belongs to a family of the glycan-binding proteins (GBPs) and is known to restrict bacterial activity via interacting with pathogen associated molecular pattern (PAMPs). However, the underlying immune mechanism of endogenous Gal-9 on fish against bacterial infection is still unclear. In this study, effect of Gal-9 from Onychostoma macrolepis (OmGal-9) on expression of immune related-genes were measured by HEK293T. The immune response of O. macrolepis with OmGal-9 overexpression to Aeromonas hydrophila (A. hydrophila) infection (1.65×108 CFU/mL) were evaluated by tissue bacterial load, fish survival rate and transcriptome analysis. The results showed that OmGal-9 displayed a punctate distribution in the nucleus and cytoplasm of HEK293T cells. Compared to cells transfected with the empty vector (EV) group, recombinant plasmid pEGFP-Gal9 treatment (Gal9) significantly down-regulated the expression of immune-related genes TNFα, STAT3, MyD88, LCK, and p52 of HEK293T cells stimulated with LPS at 24 h, while up-regulated IκBα and caspase1 (P < 0.05). The activities of catalase (CAT), superoxide dismutase (SOD), the total antioxidant capacity (T-AOC), alkaline phosphatase (AKP), acid phosphatase (ACP), and lysozyme (LZM) of O.macrolepis were significantly increased on 7 days in Gal9 group compared to EV group (P < 0.05). The bacterial load of liver, spleen, and kidney of O.macrolepis infected with A. hydrophila in Gal9 group at 24 h significantly lower than that in EV group (P < 0.05), and the survival rate has increased from 15% to 35%. A comparative transcriptome analysis between the Gal9 and EV group identified 305 differentially expressed genes (DEGs). The analysis showed that OmGal-9 might play important regulatory in glycolysis / gluconeogenesis, fatty acid degradation, and ascorbate and aldarate metabolism. Moreover, the immune-related DEGs were predominantly enriched in eleven pathways, with the most important three of them being linked to innate immunity: NOD-like, C-type lectin and Toll-like receptor signaling pathway. Taking together, OmGal-9 can enhance the resistance of fish to bacterial diseases by improving immune system function and activating immune-related pathways.

Integrated single-cell and bulk RNA sequencing analysis reveal immune-related biomarkers in postmenopausal osteoporosis

BackgroundPostmenopausal osteoporosis (PMOP) represents as a significant health concern, particularly as the population ages. Currently, there is a paucity of comprehensive descriptions regarding the immunoregulatory mechanisms and early diagnostic biomarkers associated with PMOP. This study aims to examine immune-related differentially expressed genes (IR-DEGs) in the peripheral blood mononuclear cells of PMOP patients to identify immunological patterns and diagnostic biomarkers. MethodsThe GSE56815 dataset from the Gene Expression Omnibus (GEO) database was used as the training group, while the GSE2208 dataset served as the validation group. Initially, differential expression analysis was conducted after data integration to identify IR-DEGs in the peripheral blood mononuclear cells of PMOP. Subsequently, feature selection of these IR-DEGs was performed using RF, SVM-RFE, and LASSO regression models. Additionally, the expression of IR-DEGs in distinct bone marrow cell subtypes was analyzed using single-cell RNA sequencing (scRNA-seq) datasets, allowing the identification of cellular communication patterns within various cell subgroups. Finally, molecular subtypes and diagnostic models for PMOP were constructed based on these selected IR-DEGs. Furthermore, the expression levels of characteristic IR-DEGs were examined in rat osteoporosis (OP) models. ResultsUsing machine learning, six IR-DEGs (JUN, HMOX1, CYSLTR2, TNFSF8, IL1R2, and SSTR5) were identified. Subsequently, two molecular subtypes of PMOP (subtype 1 and subtype 2) were established, with subtype 1 exhibiting a higher proportion of M1 macrophage infiltration. Analysis of the scRNA-seq dataset revealed 11 distinct cell clusters. It was noted that JUN was significantly overexpressed in M1 macrophages, while HMOX1 showed a marked elevation in endothelial cells and M2 macrophages. Cell communication results suggested that the PMOP microenvironment features increased interactions among M2 macrophages, CD8+ T cells, Tregs, and fibroblasts. The diagnostic model based on these six IR-DEGs demonstrated excellent diagnostic performance (AUC = 0.927). In the OP rat model, the expression of IL1R2 and TNFSF8 were significantly elevated. ConclusionJUN, HMOX1, CYSLTR2, TNFSF8, IL1R2, and SSTR5 may serve as promising molecular targets for diagnosing and subtyping patients with PMOP. These results offer novel perspectives on the early diagnosis of PMOP and the advancement of personalized immune-based therapies.

Prognostic model of lung adenocarcinoma based on immunoprognosis-related genes and related drug prediction.

Lung cancer (LC) is the most common malignant tumor in the world, and lung adenocarcinoma (LUAD) is the most common type of LC. Immune microenvironment plays a critical role in cancer from onset to relapse. We aimed to identify an effective immune-related prediction model for assessing prognosis and predicting the relevant tumor therapeutic drugs. According to the RNA sequencing data of LUAD transcriptome in The Cancer Genome Atlas (TCGA) database and the immune-related genes obtained from IMMPORT (The Immunology Database and Analysis Portal) database, immune prognosis-related genes were screened. Weighted gene co-expression network analysis (WGCNA) identified hub genes in differentially expressed immune-related genes (DEIRGs). Least absolute shrinkage and selection operator (LASSO) Cox and ten rounds of cross-validation were used to screen core genes to establish a prognostic model, and in situ hybridization was used to verify the expression of core genes in LUAD. Then the patients from the TCGA database were divided into high-risk group and low-risk group. The survival, tumor microenvironment (TME) and immune cell infiltration of different groups were further analyzed, and the differential genes between the two groups were analyzed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) enrichment analyses. Finally, the small molecular drugs that can inhibit the prognosis of LUAD were screened by Connectivity Map (CMAP), and the therapeutic mechanism of small molecular drug oxibendazole was verified by Cell Counting Kit-8 (CCK-8) experiment. A four-immunoprognosis-related gene model was established to forecast the overall survival (OS) of LUAD through LASSO Cox regression and ten rounds of cross-validation analysis. This prognostic model stratified LUAD patients into low-risk and high-risk groups. According to the findings of the survival analysis, the low-risk group had a greater OS than the high-risk group and the content of immune cells in LUAD was corrected with the survival prognosis of patients. Univariate and multivariate Cox regression also revealed that the prognostic model was an independent prognosis factor in LUAD. Five kinds of small molecular drugs which can inhibit the prognosis of LUAD were screened by CMAP. As shown by CCK-8 test, the small molecular drug "oxibendazole" can effectively inhibit the proliferation of LUAD cells. Based on immune-related prognostic genes, a new prognostic model for LUAD was constructed. Oxibendazole can inhibit the proliferation of LUAD cells, which provides a new idea for the treatment of LUAD.

Prognostic value of four immune-related genes in lower-grade gliomas: a biomarker discovery study.

The tumor microenvironment and IRGs are highly correlated with tumor occurrence, progression, and prognosis. However, their roles in grade II and III gliomas, termed LGGs in this study, remain to be fully elucidated. Our research aims to develop immune-related features for risk stratification and prognosis prediction in LGG. Using the ssGSEA method, we assessed the immune characteristics of the LGG population. We conducted differential analysis using LGG samples from the TCGA database and normal samples from GTEx, identifying 412 differentially expressed immune-related genes (DEIRGs). Subsequently, we utilized univariate Cox, LASSO, and multivariate Cox regression analyses to establish both a gene predictive model and a nomogram predictive model. Here, we found that the ESTIMATE score, immune score and stromal score of high-immunity, high-grade and isocitrate dehydrogenase (IDH) wild-type glioma were higher than those of the corresponding group, and the tumor purity was lower. Higher ESTIMATE scores, stromal scores and immune scores indicated a poor prognosis in patients with LGG. Our four-gene prognostic model demonstrated superior accuracy compared to other molecular features. Validation using the CGGA as a testing set and the combined TCGA and CGGA cohort confirmed its robust prognostic value. Additionally, a nomogram integrating the prognostic model and clinical variables showed enhanced predictive capability. Our study highlights the prognostic significance of the identified four DEIRGs (KLRC3, MR1, PDIA2, and RFXAP) in LGG patients. The predictive model and nomogram developed herein offer valuable tools for personalized treatment strategies in LGG. Future research should focus on further validating these findings and exploring the functional roles of these DEIRGs within the LGG tumor microenvironment.

Angiogenesis related genes based prognostic model of glioma patients developed by multi-omics approach

IntroductionGlioma, particularly glioblastoma (GBM), is a highly malignant brain tumor with poor prognosis despite current therapeutic approaches. The tumor microenvironment (TME), plays a crucial role in glioma progression by promoting invasion and drug resistance. Angiogenesis, the formation of new blood vessels, is a tightly regulated process involving endothelial cell activation, proliferation, and migration. In cancer, angiogenesis becomes dysregulated, leading to excessive blood vessel formation.MethodsWe enrolled bulk data of TCGA-LGG/GBM, CGGA-693, and CGGA-325 cohorts, scRNA data of GSE162631, GSE84465, and GSE138794 cohorts. Identification of malignant cells was conducted by “copycat” R package. The “AUCell” R package scored the activity of target gene set of each single cell. Consensus clustering was applied using the “ConsensusClusterPlus” R package, while tumor-infiltrating immune cells were determined using “IOBR” R package. To construct a prognostic model, we used LASSO and multiCOX algorithms based on the expression levels of the 15 hub genes, the efficacy of which was verified by KM and ROC analysis.ResultsWe identified 4 different malignant cell subclusters in glioma and disclosed their distinct gene expression patterns and interactions within TME. We identified differentially expressed immune-related genes (DE-ARGs) in glioma and found 15 genes that were specifically expressed in the malignant glioma cell populations. Glioma cells with higher expression of these DE-ARGs were associated with gliogenesis, glial cell development, and vasculature development. We found that tumor-infiltrating monocytes were the main interacting cell type within glioma TME. Using the expression patterns of the 15 screened DE-ARGs, we categorized glioma samples into 2 molecular clusters with distinct immune features, suggesting a possible relationship between angiogenesis and immune activation and recruitment. We constructed a prognostic model based on the expression levels of the 15 DE-ARGs and evaluated its predictive ability for glioma patient outcomes, which displayed exceedingly high efficacy.ConclusionWe characterized different malignant cell subclusters in glioma and investigate their gene expression patterns and interactions within TME. We constructed a prognostic model based on the expression levels of the 15 DE-ARGs and evaluated its predictive ability for glioma patient outcomes, which displayed exceedingly high efficacy.

Transcriptome Analysis Reveals the Immunosuppression in Tiger Pufferfish (Takifugu rubripes) under Cryptocaryon irritans Infection.

The tiger pufferfish (Takifugu rubripes), also known as fugu, has recently suffered from severe C. irritans infections under aquaculture environment, yet the underlying immune mechanisms against the parasite remain poorly understood. In this study, we conducted a comprehensive transcriptome analysis of the gill tissue from infected and uninfected fish using PacBio long-read (one pooled sample each for seriously infected and healthy individuals, respectively) and Illumina short-read (three pools for mildly infected, seriously infected, and healthy individuals, respectively) RNA sequencing technologies. After aligning sequence data to fugu's reference genome, 47,307 and 34,413 known full-length transcripts were identified and profiled in healthy and infected fish, respectively. Similarly, we identified and profiled 1126 and 803 novel genes that were obtained from healthy and infected fish, respectively. Interestingly, we found a decrease in the number of alternative splicing (AS) events and long non-coding RNAs (lncRNAs) after infection with C. irritans, suggesting that they may be involved in the regulation of the immune response in fugu. There were 687 and 1535 differentially expressed genes (DEGs) in moderately and heavily infected fish, respectively, compared to uninfected fish. Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that immune-related DEGs in the two comparison groups were mainly enriched in cytokine-cytokine receptor interactions, ECM-receptor interactions, T-cell receptor signaling pathways, Th1 and Th2 cell differentiation, and Th17 cell differentiation pathways. Further analysis revealed that a large number of immune-related genes were downregulated in infected fish relative to uninfected ones, such as CCR7, IL7R, TNFRSF21, CD4, COL2A1, FOXP3B, and ITGA8. Our study suggests that C. irritans is potentially a highly efficient parasite that may disrupt the defense mechanisms of fugu against it. In addition, in combination of short-read RNA sequencing and previous genome-wide association analyses, we identified five key genes (NDUFB6, PRELID1, SMOX, SLC25A4, and DENND1B) that might be closely associated with C. irritans resistance. This study not only provides valuable resources of novel genic transcripts for further research, but also provides new insights into the immune mechanisms underlying C. irritans infection response in farmed fugu.

An integrated signature of clinical metrics and immune-related genes as a prognostic indicator for ST-segment elevation myocardial infarction patient survival

BackgroundThe immune-inflammatory pathway plays a critical role in myocardial infarction development. However, few studies have systematically explored immune-related genes in relation to myocardial infarction prognosis using bioinformatic analysis. Our study aims to identify differentially expressed immune-related genes(DEIRGs) in ST-segment elevation myocardial infarction (STEMI) patients and investigate their association with clinical outcomes. Materials and methodsWe conducted a systematic review of Gene Expression Omnibus datasets, selecting GSE49925, GSE60993, and GSE61144 for analysis. DEIRGs were identified using GEO2R and overlapped across the chosen datasets. Functional enrichment analysis elucidated the DEIRGs' biological functions and pathways. We established an optimal prognostic prediction model using LASSO penalized Cox proportional hazards regression. The signature's clinical utility was evaluated through survival analysis, ROC curve assessment, and decision curve analysis. Additionally, we constructed a prognostic nomogram for survival rate prediction. External validation was performed using our own plasma samples. ResultsThe resulting prognostic signature integrated two dysregulated DEIRGs (S100A12 and IL2RB) and two clinical variables (serum creatinine level and Gensini score). This signature effectively stratified patients into low- and high-risk groups. Survival analysis, ROC curve analysis, and decision curve analysis demonstrated its robust predictive performance and clinical utility within the first two years post-disease onset. External validation confirmed significant outcome differences between risk groups. ConclusionsOur study establishes a prognostic signature that combines DEIRGs and clinical variables for STEMI patients. The signature exhibits promising predictive capabilities for patient stratification and survival risk assessment.

Investigating the Role of SNAI1 and ZEB1 Expression in Prostate Cancer Progression and Immune Modulation of the Tumor Microenvironment.

Prostate cancer (PCa) is an immunologically cold tumor and the molecular processes that underlie this behavior are poorly understood. In this study, we investigated a primary cohort of intermediate-risk PCa (n = 51) using two NanoString profiling panels designed to study cancer progression and immune response. We identified differentially expressed genes (DEGs) and pathways associated with biochemical recurrence (BCR) and clinical risk. Confirmatory analysis was performed using the TCGA-PRAD cohort. Noteworthy DEGs included collagens such as COL1A1, COL1A2, and COL3A1. Changes in the distribution of collagens may influence the immune activity in the tumor microenvironment (TME). In addition, immune-related DEGs such as THY1, IRF5, and HLA-DRA were also identified. Enrichment analysis highlighted pathways such as those associated with angiogenesis, TGF-beta, UV response, and EMT. Among the 39 significant DEGs, 11 (28%) were identified as EMT target genes for ZEB1 using the Harmonizome database. Elevated ZEB1 expression correlated with reduced BCR risk. Immune landscape analysis revealed that ZEB1 was associated with increased immunosuppressive cell types in the TME, such as naïve B cells and M2 macrophages. Increased expression of both ZEB1 and SNAI1 was associated with elevated immune checkpoint expression. In the future, modulation of EMT could be beneficial for overcoming immunotherapy resistance in a cold tumor, such as PCa.

Anti-inflammatory and immunoregulatory effects of colistin sulphate on human PBMCs.

In previous studies, CST has been identified as having an immunostimulatory effect on Caenorhabditis elegans and macrophage of rats. Here, we further investigated its immunomodulatory effects on human peripheral blood mononuclear cells (PBMCs). LPS-stimulated PBMCs inflammatory model was established. Flow cytometry was applied to measure phagocytosis of PBMCs. Cytokine mRNA and protein expression levels of LPS-stimulated PBMCs with or without CST were measured by qRT-PCR and ELISA. The transcriptomic profile of CST-treated PBMCs was investigated by RNA-sequencing. Gene Ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) were applied to find potential signalling pathways. PBMCs showed a significant increase in phagocytic activity at 6 h after being incubated with CST at the concentration of 10 μg/mL. In the presence of LPS, CST maintained and promoted the expression of TNF-α and chemokine CCL24. The content of pro-inflammatory cytokines, such as IL-1β, IL-6 and IFN-γ, which were released from LPS-stimulated PBMCs, was reduced by CST at 6 h. Anti-inflammatory cytokines, such as IL-4, IL-13 and TGF-β1, were significantly increased by CST at 24 h. A total of 277 differentially expressed immune-related genes (DEIRGs) were detected and cytokine-cytokine receptor interaction was highly enriched. CST presented obvious anti-inflammatory and immunoregulatory effects in LPS-induced PBMCs inflammatory model not only by improving the ability of PBMCs to clear pathogens but also by decreasing pro-inflammatory cytokines and increasing anti-inflammatory cytokines. And the mechanism may be related to cytokine-cytokine receptor interaction.

Construction of a novel mRNA–miRNA–lncRNA/circRNA triple subnetwork associated with immunity and aging in intervertebral disc degeneration

Objective Intervertebral disk degeneration (IVDD) is one of the most common causes of low back pain. However, in the etiology of IVDD, the specific method by which nucleus pulposus (NP) cell senescence and the immune response induce disease is uncertain. Methods Gene Expression Omnibus database was used to find differentially expressed genes (DEGs), differentially expressed miRNAs (DE miRNAs), differentially expressed lncRNAs (DE lncRNAs), and differentially expressed circRNAs (DE circRNAs). Functional enrichment analysis was performed through Enrichr database. Potential regulatory miRNAs, lncRNAs and circRNAs of mRNAs were predicted by ENCORI and circBank, respectively. Results We identified 198 upregulated and 131 downregulated genes, 39 upregulated and 22 downregulated miRNAs, 2152 upregulated and 564 downregulated lncRNAs, and 352 upregulated and 279 downregulated circRNAs as DEGs, DE miRNAs, DE lncRNAs, DE circRNAs, respectively. Functional enrichment analysis revealed that they were significantly enriched in Toll-like receptor signaling route and the NF-kappa B signaling pathway. An mRNA–miRNA–lncRNA/circRNA network linked to the pathogenesis of NP cells in IVDD was constructed based on node degree and differential expression level. Eight immune-related DEGs (6 upregulated and 2 downregulated genes) and five aging-related DEGs (3 upregulated and 2 downregulated genes) were identified in the critical network. Conclusion We established a novel immune-related and aging-related triple regulatory network of mRNA–miRNA–lncRNA/circRNA ceRNA, among which all RNAs may be utilized as the pathogenesis biomarker of NP cells in IVDD.

Construction of an immune-related gene signature for overall survival prediction and immune infiltration in gastric cancer.

Treatment options for patients with gastric cancer (GC) continue to improve, but the overall prognosis is poor. The use of PD-1 inhibitors has also brought benefits to patients with advanced GC and has gradually become the new standard treatment option at present, and there is an urgent need to identify valuable biomarkers to classify patients with different characteristics into subgroups. To determined the effects of differentially expressed immune-related genes (DEIRGs) on the development, prognosis, tumor microenvironment (TME), and treatment response among GC patients with the expectation of providing new biomarkers for personalized treatment of GC populations. Gene expression data and clinical pathologic information were downloaded from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were searched from ImmPort. DEIRGs were extracted from the intersection of the differentially-expressed genes (DEGs) and IRGs lists. The enrichment pathways of key genes were obtained by analyzing the Kyoto Encyclopedia of Genes and Genomes (KEGGs) and Gene Ontology (GO) databases. To identify genes associated with prognosis, a tumor risk score model based on DEIRGs was constructed using Least Absolute Shrinkage and Selection Operator and multivariate Cox regression. The tumor risk score was divided into high- and low-risk groups. The entire cohort was randomly divided into a 2:1 training cohort and a test cohort for internal validation to assess the feasibility of the risk model. The infiltration of immune cells was obtained using 'CIBERSORT,' and the infiltration of immune subgroups in high- and low-risk groups was analyzed. The GC immune score data were obtained and the difference in immune scores between the two groups was analyzed. We collected 412 GC and 36 adjacent tissue samples, and identified 3627 DEGs and 1311 IRGs. A total of 482 DEIRGs were obtained. GO analysis showed that DEIRGs were mainly distributed in immunoglobulin complexes, receptor ligand activity, and signaling receptor activators. KEGG pathway analysis showed that the top three DEIRGs enrichment types were cytokine-cytokine receptors, neuroactive ligand receptor interactions, and viral protein interactions. We ultimately obtained an immune-related signature based on 10 genes, including 9 risk genes (LCN1, LEAP2, TMSB15A mRNA, DEFB126, PI15, IGHD3-16, IGLV3-22, CGB5, and GLP2R) and 1 protective gene (LGR6). Kaplan-Meier survival analysis, receiver operating characteristic curve analysis, and risk curves confirmed that the risk model had good predictive ability. Multivariate COX analysis showed that age, stage, and risk score were independent prognostic factors for patients with GC. Meanwhile, patients in the low-risk group had higher tumor mutation burden and immunophenotype, which can be used to predict the immune checkpoint inhibitor response. Both cytotoxic T lymphocyte antigen4+ and programmed death 1+ patients with lower risk scores were more sensitive to immunotherapy. In this study a new prognostic model consisting of 10 DEIRGs was constructed based on the TME. By providing risk factor analysis and prognostic information, our risk model can provide new directions for immunotherapy in GC patients.

Construction of LncRNA-mediated CeRNA network for investigating the immune pathogenesis of myocardial infarction.

Myocardial infarction (MI) is a cardiovascular disease that seriously threatens human health. However, an immune-related competitive endogenous RNA (ceRNA) network has not been reported in MI. The GSE66360, GSE19339, GSE97320, GSE61741, and GSE168281 datasets were acquired from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRNAs) from MI patients and healthy controls were screened and an immune-related ceRNA network was constructed. Furthermore, the key long noncoding RNAs(lncRNAs) highly related to the immune mechanism of MI were identified utilizing the random walk with restart algorithm. Finally, the expression of the hub genes was further verified in the GSE66360, GSE19339, and GSE97320 datasets, and quantitativereal-time polymerase chain reaction (qRT-PCR) was performed for the MI patients and healthy controls. A total of 184 differentially expressed immune-related genes(DE-IRGs) and 432 DE-miRNAs were obtained, and an immune-related ceRNA network comprising 1421 lncRNAs, 61 DE-miRNAs, and 139 DE-IRGs was constructed. According to the order of stress, betweenness, and closeness, NEAT1, KCNQ1OT1, and XIST were identified as key lncRNAs. Moreover, random walk with restart analysis also suggested that NEAT1, KCNQ1OT1, and XIST are key lncRNAs. Subsequently, a ceRNA network of 10 hub genes and 3 lncRNAs was constructed. Finally, we found that the expression of FCER1G and TYROBP significantly differed between MI patients and control individuals in the GSE66360, GSE19339, and GSE97320 datasets. qRT-PCR revealed that the expression of NEAT1, KCNQ1OT1, XIST, FCER1G, and TYROBP was significantly elevated in MI tissue samples compared to healthy control tissue samples. NEAT1, KCNQ1OT1, XIST, FCER1G, and TYROBP are involved in MI and can be used as molecular biomarkers for the screening and diagnosis of MI. Furthermore, the immune system plays an essential role in the onset and progression of MI.

Implication of m6A Methylation Regulators in the Immune Microenvironment of Bronchopulmonary Dysplasia.

N6-methyladenosine (m6A) regulates gene expression and governs many important biological processes. However, the function of m6A in the development of bronchopulmonary dysplasia (BPD) remains poorly characterized. Thus, the purpose of this investigation was to evaluate the effects of m6A RNA methylation regulators on the development of BPD. BPD-related transcriptome data were downloaded from the GEO database. Differentially expressed m6A methylation regulators between BPD and control group were identified. Consensus clustering was conducted for the classification of BPD and association between clusters and BPD phenotypes were explored. Analysis of differentially expressed genes (DEGs) and immune-related DEGs was performed. The GSEA, GO and KEGG analyses were used to interpret the functional enrichments. The composition of immune cell subtypes in BPD subsets was predicted by CIBERSORT analysis. Compared with the control group, expression of most m6A regulators showed significant alteration, especially for IGF2BP1/2/3. BPD was classified into 2 subsets, and cluster 1 was correlated with severe BPD. Furthermore, the results of functional enrichment analyses showed a disturbed immune-related signaling pathway. Based on CIBERSORT analysis, we found that the proportion of immune cell subsets changed between cluster 1 and cluster 2. Our study revealed the implication of m6A methylation regulators in the development of BPD, which might provide a novel insight for the diagnosis and treatment of BPD.

Identification of Shared Biomarkers and Immune Infiltration Signatures between Vitiligo and Hashimoto's Thyroiditis.

Vitiligo and Hashimoto's thyroiditis (HT) are concomitant autoimmune diseases characterized by the destruction of melanocytes or thyrocytes. We aimed to explore the immunological mechanism of this comorbidity and screen their potential biomarkers. We downloaded the microarray datasets from the GEO database. Differentially expressed genes (DEGs) and immune-related genes (IRGs) were selected. The immune-related differentially expressed genes (IRDEGs) were obtained by taking the intersection. Candidate biomarkers were elected by Cytoscape software. CIBERSORT was used to depict immune cell infiltration prospects. Correlation analysis was conducted between infiltrating cells and several indicators. The results were validated by real-time quantitative PCR (RT-qPCR). Three datasets and 60 IRDEGs were obtained in total. Pathway enrichment analysis showed that the T cell receptor signaling pathway, IL-17 signaling pathway, receptor-ligand activity, and signaling receptor activator activity were significantly enriched. We screened out four hub genes, including IFNG, STAT1, IL1B, and CXCL10. The ROC curve indicated the highest diagnostic value of CXCL10 in both vitiligo and HT. Immuno-infiltration analysis revealed significant changes in T cell subsets and macrophage subtypes, which were correlated with four hub genes, melanocyte markers, and thyroid-specific antigens. qPCR validated the hub genes in peripheral blood mononuclear cells from patients with comorbidity. IFNG, STAT1, IL1B, and CXCL10, were the key IRDEGs to vitiligo and HT. These genes may participate in the comorbidity by remodeling the immune cell infiltration pattern, and cross-expressed antigens may mediate the common damage of melanocytes and thyroid tissues.

Comprehensive Analysis of Immune Infiltration and Key Genes in Peri-Implantitis Using Bioinformatics and Molecular Biology Approaches.

BACKGROUND Peri-implantitis is the main cause of failure of implant treatment, and there is little research on its molecular mechanism. This study aimed to identify key biomarkers and immune infiltration of peri-implantitis using a bioinformatics method. MATERIAL AND METHODS Three Gene Ontology (GO) gene expression profiles were selected from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified by the LIMMA package, and functional correlations of DEGs were analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Information on immune-related genes was obtained from ImmPort (https://www.immport.org) and InnateDB (http://www.innatedb.com). Immune-related DEGs were screened by least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE). The single-sample Gene Set Enrichment Analysis algorithm was used to analyze immune cell infiltration in gingival tissue between peri-implantitis and normal controls. Finally, results of bioinformatics analysis were verified by qPCR. RESULTS A total of 398 DEGs were identified, of which 96 were immune-related. Enrichment analysis showed these genes were enriched in inflammatory response, leucocyte chemotaxis, immune response-regulating signaling pathway, and cell activation. Seven key genes were selected by LASSO and SVM-RFE. Receiver operating characteristic curve results showed these genes had excellent diagnostic efficacy. Results of qPCR showed significant differences in the expression of these genes. CONCLUSIONS Differences in key genes and immune infiltration between peri-implantitis and gingival tissues of normal controls may provide new insights into the development of peri-implantitis. Elucidating the difference in immune infiltration between peri-implantitis tissues and normal tissues will help to understand the development of peri-implantitis.

Cryptosporidium parvum infection alters the intestinal mucosa transcriptome in neonatal calves: implications for immune function.

One of the leading causes of infectious diarrhea in newborn calves is the apicomplexan protozoan Cryptosporidium parvum (C. parvum). However, little is known about its immunopathogenesis. Using next generation sequencing, this study investigated the immune transcriptional response to C. parvum infection in neonatal calves. Neonatal male Holstein-Friesian calves were either orally infected (N = 5) or not (CTRL group, N = 5) with C. parvum oocysts (gp60 subtype IIaA15G2R1) at day 1 of life and slaughtered on day 7 after infection. Total RNA was extracted from the jejunal mucosa for short read. Differentially expressed genes (DEGs) between infected and CTRL groups were assessed using DESeq2 at a false discovery rate < 0.05. Infection did not affect plasma immunohematological parameters, including neutrophil, lymphocyte, monocyte, leucocyte, thrombocyte, and erythrocyte counts as well as hematocrit and hemoglobin concentration on day 7 post infection. The immune-related DEGs were selected according to the UniProt immune system process database and were used for gene ontology (GO) and pathway enrichment analysis using Cytoscape (v3.9.1). Based on GO analysis, DEGs annotated to mucosal immunity, recognizing and presenting antigens, chemotaxis of neutrophils, eosinophils, natural killer cells, B and T cells mediated by signaling pathways including toll like receptors, interleukins, tumor necrosis factor, T cell receptor, and NF-KB were upregulated, while markers of macrophages chemotaxis and cytosolic pattern recognition were downregulated. This study provides a holistic snapshot of immune-related pathways induced by C. parvum in calves, including novel and detailed feedback and feedforward regulatory mechanisms establishing the crosstalk between innate and adaptive immune response in neonate calves, which could be utilized further to develop new therapeutic strategies.

Identification and characterization of two immune-related subtypes in human chronic kidney disease

BackgroundImmune response plays a vital role in the initiation and development of chronic kidney disease (CKD). Detailed mechanisms and specific immune-related biomarkers of CKD need further clarification. We aimed to identify and characterize immune-related infiltrates that are implicated in the CKD development using a bioinformatics method. MethodsThe expression profiles of GSE66494 dataset were acquired from the Gene Expression Omnibus (GEO) database. Patients with CKD were divided into low- vs. high-immune subtypes based on their immune score. Based on such analysis, we identified differentially expressed genes (DEGs) of low- and high-immune subtypes. The weight gene co-expression network analysis (WGCNA) was used to identify immune-associated modules between two subtypes. The gene set enriched (GSEA) and variation (GSVA) analyses were correlated with their functional types using the molecular complex detection (MCODE) method. Finally, the immune infiltration landscape between subtypes was revealed using the xCell algorithm. ResultsThe total number of 131 differentially expressed immune-related genes (DEIRGs) were identified between low- vs. high-immune subtypes. Out of them GSEA/GSVA results identified and enriched immune- and inflammation-related pathways. In particular, GSVA results indicated that immune-related pathways were activated in high-immune subgroups. The core DEIRG genes that were identified to be involved in CKD development included: the protein tyrosine phosphatase receptor type C (PTPRC; also known as CD45) regulating cell growth and differentiation, an early activation marker (CD69), co-receptor for T cell receptor (CD8A), and T cell co-stimulatory signal (CD28). These core DEIRD genes were further verified by the GSE96804 dataset. We also found a higher proportion of immune cells infiltrating the high-immune subgroup. Furthermore, the four core genes were positively correlated with most immune cell types. ConclusionAmong 131 DEIRG genes, four genes (PTPRC, CD69, CD8A, and CD28) were identified as potential biomarkers associated with the immune cell infiltration in CKD patients, which may provide a novel insight for immunotherapy for CKD.

Immune-Related Genes and Immune Cell Infiltration Characterize the Maturation Status of Arteriovenous Fistulas: An Integrative Bioinformatics Study and Experimental Validation Based on Transcriptome Sequencing.

Arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis, but the low maturation rate is concerning. Immune cells' impact on AVF maturation lacks bioinformatics research. The study aims to investigate the potential predictive role of immune-related genes and immune cell infiltration characteristics in AVF maturation. We analyzed the high-throughput sequencing dataset to identify differentially expressed genes (DEGs). Then, we performed enrichment analyses (GO, KEGG, GSEA) on immune-related genes and pathways in mature AVF. We focused on differentially expressed immune-related genes (DEIRGs) and constructed a PPI network to identify hub genes. These hub genes were validated in other databases and experiments, including qPCR and immunohistochemistry (IHC). The immune cell infiltration characteristics in native veins, failed AVFs, and matured AVFs were analyzed by cibersortX. Partial experimental validation was conducted using clinical samples. Our results showed that immune-related genes and signaling pathways are significantly enriched in mature AVF. We validated this in other databases and ultimately identified three hub genes (IL1B, IL6, CXCR4) in combination with experiments. Significant differences in immune cell infiltration characteristics were observed among native veins, failed AVFs, and matured AVFs. Immune cell infiltration analysis revealed that accumulation of CD4+ T cells, dendritic cells, mast cells and M2 macrophages contribute to AVF maturation. These immune-related genes and immune cells have the potential to serve as predictive factors for AVF maturation. We partially validated this experimentally. From a bioinformatics perspective, our results have identified, for the first time, a set of immune-related genes and immune cell infiltration features that can characterize the maturation of AVF and significantly impact AVF maturation. These features hold potential as predictive indicators for AVF maturation outcomes.

Suppression of overactivated immunity in the early stage is the key to improve the prognosis in severe burns.

Severe burns can lead to systemic inflammatory response syndrome (SIRS) and multiple organ dysfunction syndrome (MODS) due to inflammation-immunity dysregulation. This study aimed to identify key immune-related molecules and potential drugs for immune regulation in severe burn treatment. Microarray datasets GSE77791 and GSE37069 were analyzed to identify immune-related differentially expressed genes (DEGs), enriched pathways and prognosis-related genes. The DGIdb database was used to identify potentially clinically relevant small molecular drugs for hub DEGs. Hub DEGs were validated by total RNA from clinical blood samples through qPCR. The efficacy of drug candidates was tested in a severe burn mouse model. Pathologic staining was used to observe organ damage. Enzyme Linked Immunosorbent Assay (ELISA) was used to detect the serum IL-1b, IL-6, TNF-a and MCP-1 contents. Activation of the NF-κB inflammatory pathway was detected by western blotting. Transcriptome sequencing was used to observe inflammatory-immune responses in the lung. A total of 113 immune-related DEGs were identified, and the presence of immune overactivation was confirmed in severe burns. S100A8 was not only significantly upregulated and identified to be prognosis-related among the hub DEGs but also exhibited an increasing trend in clinical blood samples. Methotrexate, which targets S100A8, as predicted by the DGIdb, significantly reduces transcription level of S100A8 and inflammatory cytokine content in blood, organ damage (lungs, liver, spleen, and kidneys) and mortality in severely burned mice when combined with fluid resuscitation. The inflammatory-immune response was suppressed in the lungs. S100A8 with high transcription level in blood is a potential biomarker for poor severe burn prognosis. It suggested that methotrexate has a potential application in severe burn immunotherapy. Besides, it should be emphasized that fluid resuscitation is necessary for the function of methotrexate.