Immune-related Differentially Expressed Genes Research Articles

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.

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.

Mapping the immunological battlefield in gastric cancer: prognostic implications of an immune gene expression signature

BackgroundGastric cancer (GC) is a heterogeneous malignancy with variable clinical outcomes. The immune system has been implicated in GC development and progression, highlighting the importance of immune-related gene expression patterns and their prognostic significance.ObjectiveThis study aimed to identify differentially expressed immune-related genes (DEIRGs) and establish a prognostic index for GC patients using comprehensive bioinformatic analyses.MethodsWe integrated RNA sequencing data from multiple databases and identified DEIRGs by overlapping differentially expressed genes with immune-related genes. Functional enrichment analysis was performed to uncover the biological processes and signaling pathways associated with DEIRGs. We conducted a Weighted Gene Co-expression Network Analysis (WGCNA) to identify key gene modules related to with GC. Cox regression analysis was conducted to determine independent prognostic DEIRGs for overall survival prediction. Based on these findings, we developed an immune-related gene prognostic index (IRGPI) based on these findings. The prognostic value of the IRGPI was validated using survival analysis and an independent validation cohort. Functional enrichment analysis, gene mutation analysis, and immune cell profiling were performed to gain insights into the biological functions and immune characteristics associated with the IRGPI-based subgroups.ResultsWe identified 493 DEIRGs significantly enriched in immune-related biological processes and signaling pathways associated with GC. WGCNA analysis revealed a significant module (turquoise module) associated with GC, revealing potential therapeutic targets. Cox regression analysis identified RNASE2, CGB5, CTLA4, and DUSP1 as independent prognostic DEIRGs. The IRGPI, incorporating the expression levels of these genes, demonstrated significant prognostic value in predicting overall survival. The IRGPI-based subgroups exhibited distinct biological functions, genetic alterations, and immune cell compositions.ConclusionOur study identified DEIRGs and established a prognostic index (IRGPI) for GC patients. The IRGPI exhibited promising prognostic potential and provided insights into GC tumor biology and immune characteristics. These findings have implications for guiding therapeutic strategies.

Identification CCL2,CXCR2,S100A9 of the immune-related gene markers and immune infiltration characteristics of inflammatory bowel disease and heart failure via bioinformatics analysis and machine learning.

Recently, heart failure (HF) and inflammatory bowel disease (IBD) have been considered to be related diseases with increasing incidence rates; both diseases are related to immunity. This study aims to analyze and identify immune-related gene (IRG) markers of HF and IBD through bioinformatics and machine learning (ML) methods and to explore their immune infiltration characteristics. This study used gene expressiondata (GSE120895, GSE21610, GSE4183) from the Gene Expression Omnibus (GEO) database to screen differentially expressed genes (DEGs) and compare them with IRGs from the ImmPort database to obtain differentially expressed immune-related genes (DIRGs). Functional enrichment analysis of IRGs was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, three machine models and protein-protein interactions (PPIs) were established to identify diagnostic biomarkers. The receiver operating characteristic (ROC) curves were applied to evaluate the diagnostic value of the candidate biomarkersin the validation set (GSE1145, GSE36807) and obtain their correlations with immune cells through the Spearman algorithm. Finally, the CIBERSORT algorithm was used to evaluate the immune cell infiltration of the two diseases. Thirty-four DIRGs were screened and GO and KEGG analysis results showed that these genes are mainly related to inflammatory and immune responses. CCL2, CXCR2 and S100A9 were identified as biomarkers.The immune correlation results indicated in both diseases that CCL2 is positively correlated with mast cell activation, CXCR2 is positively correlated with neutrophils and S100A9 is positively correlated with neutrophils and mast cell activation. Analysis of immune characteristics showed that macrophages M2, macrophages M0 and neutrophils were present in both diseases. CCL2, CXCR2 and S100A9 are promising biomarkers that will become potential immunogenetic biomarkers for diagnosing comorbidities of HF and IBD. macrophages M2, macrophages M0, neutrophil-mediated inflammation and immune regulation play important roles in the development of HF and IBD and may become diagnostic and therapeutic targets.

Identification of Immune-Related Genes as Biomarkers for Uremia.

Uremia, which is characterized by immunodeficiency, is associated with the deterioration of kidney function. Immune-related genes (IRGs) are crucial for uremia progression. The co-expression network was constructed to identify key modular genes associated with uremia. IRGs were intersected with differentially expressed genes (DEGs) between uremia and control groups and key modular genes to obtain differentially expressed IRGs (DEIRGs). DEIRGs were subjected to functional enrichment analysis. The protein-protein interaction (PPI) network was constructed. The candidate genes were identified using the cytoHubba tool. The biomarkers were identified using various machine learning algorithms. The diagnostic value of the biomarkers was evaluated using receiver operating characteristic (ROC) analysis. The immune infiltration analysis was implemented. The biological pathways of biomarkers were identified using gene set enrichment analysis and ingenuity pathway analysis. The mRNA expression of biomarkers was validated using blood samples of patients with uremia and healthy subjects with quantitative real-time polymerase chain reaction (qRT-PCR). In total, four biomarkers (PDCD1, NGF, PDGFRB, and ZAP70) were identified by machine learning methods. ROC analysis demonstrated that the area under the curve values of individual biomarkers were > 0.9, indicating good diagnostic power. The nomogram model of biomarkers exhibited good predictive power. The proportions of six immune cells significantly varied between the uremia and control groups. ZAP70 expression was positively correlated with the proportions of resting natural killer (NK) cells, naïve B cells, and regulatory T cells. Functional enrichment analysis revealed that the biomarkers were mainly associated with translational function and neuroactive ligand-receptor interaction. ZAP70 regulated NK cell signaling. The PDCD1 and NGF expression levels determined using qRT-PCR were consistent with those determined using bioinformatics analysis. PDCD1, NGF, PDGFRB, and ZAP70 were identified as biomarkers for uremia, providing a theoretical foundation for uremia diagnosis.

Histopathological observation and comparative transcriptome analysis reveal immune response mechanisms to Aeromonas dhakensis infection in Macrobrachium rosenbergii

The Macrobrachium rosenbergii industry is threatened by various Aeromonas, resulting in high mortality of adult prawns. However, there are few studies on the immune response of M. rosenbergii infected with Aeromonas dhakensis. In this study, we observed the hepatopancreas and gills histopathologically, performed a comparative transcriptome analysis of the hepatopancreas, and analyzed the candidate gene expression of immune-related genes in the hemolymph, hepatopancreas, and gills of M. rosenbergii that had been infected with A. dhakensis. Histopathology revealed the hepatopancreas was successively inflamed, followed by cellular vacuolation, lumen deformation, and finally tissue erosion; partial and severe inflammation of the gills occurred successively, and eventually the gill tissue atrophy and the gill filaments detached from the gill arch. Transcriptome analysis showed that a total of 77,742 unigenes and 8664 differentially expressed genes (DEGs), and the immune-related DEGs were mainly enriched in lysosome and phagosome pathways. In addition, 4 immune-related candidate genes (RhoA, CASP9, PKC, and DSCIGN) based on KEGG and PPI analysis were monitored at 6, 12, and 24h post injection (hpi) in hepatopancreas, hemolymph and gills. Their spatio-temporal expression results indicated that A. dhakensis have activated the immune system of M. rosenbergii. The present study may provide new information on the complex immune mechanism of M. rosenbergii.

Integrating TSPO PET imaging and transcriptomics to unveil the role of neuroinflammation and amyloid-β deposition in Alzheimer’s disease

PurposeDespite the revealed role of immunological dysfunctions in the development and progression of Alzheimer’s disease (AD) through animal and postmortem investigations, direct evidence regarding the impact of genetic factors on microglia response and amyloid-β (Aβ) deposition in AD individuals is lacking. This study aims to elucidate this mechanism by integrating transcriptomics and TSPO, Aβ PET imaging in clinical AD cohort.MethodsWe analyzed 85 patients with PET/MR imaging for microglial activation (TSPO, [18F]DPA-714) and Aβ ([18F]AV-45) within the prospective Alzheimer’s Disease Immunization and Microbiota Initiative Study Cohort (ADIMIC). Immune-related differentially expressed genes (IREDGs), identified based on AlzData, were screened and verified using blood samples from ADIMIC. Correlation and mediation analyses were applied to investigate the relationships between immune-related genes expression, TSPO and Aβ PET imaging.ResultsTSPO uptake increased significantly both in aMCI (P < 0.05) and AD participants (P < 0.01) and showed a positive correlation with Aβ deposition (r = 0.42, P < 0.001). Decreased expression of TGFBR3, FABP3, CXCR4 and CD200 was observed in AD group. CD200 expression was significantly negatively associated with TSPO PET uptake (r =—0.33, P = 0.013). Mediation analysis indicated that CD200 acted as a significant mediator between TSPO uptake and Aβ deposition (total effect B = 1.92, P = 0.004) and MMSE score (total effect B =—54.01, P = 0.003).ConclusionBy integrating transcriptomics and TSPO PET imaging in the same clinical AD cohort, this study revealed CD200 played an important role in regulating neuroinflammation, Aβ deposition and cognitive dysfunction.

Identification of differential immune cells and related diagnostic genes in patients with diabetic retinopathy.

Diabetic retinopathy (DR) is a frequent microvascular abnormality associated with diabetes mellitus. The loss of retinal immunity is an important underlying mechanism of the DR pathogenesis, including the change in retinal immunosuppressive characteristics and the blood-retinal barrier disturbances. Therefore, this investigation screens immune-associated biomarkers in the retina of DR patients. In this investigation, the differential expression genes (DEGs) were acquired from Gene Expression Omnibus data GSE102485. The relative expression of 22 immune cell types in each sample was calculated by CIBERSORT analysis based on gene expression profile. The core module closely associated with immune infiltration was also screened by weighted gene co-expression network analysis (WGCNA). The overlapping DEGs and module genes were the differentially expressed immune-related genes (DEIRGs). With the help of the genes/proteins (STRING) database and MCODE plug-in, the protein-protein interaction (PPI) network hub genes were screened. Furthermore, the miRNA-hub genes and transcription factor (TF)-hub gene regulatory network were used to explain the possible signal pathways in DR. The hub genes verification was carried out by Polymerase Chain Reaction. Lastly, select CSF1R and its related pathway factor p-ERK1/2 to verify their expression in RF/6A under normal and high glucose environments. A total of 3583 principle DEGs, that enriched immune-related GO terms and infection-related pathways were identified. CIBERSORT analysis showed that naive B cells, M2 macrophages, eosinophils, and neutrophil infiltration were significantly different. After intersecting 3583 DEGs, 168 DEIRGs and 181 module genes were identified. Furthermore, 15 hub genes, TYROBP, FCGR3A, CD163, FCGR2A, PTPRC, TLR2, CD14, VSIG4, HCK, CSF1R, LILRB2, ITGAM, CTSS, CD86, and LY86, were identified via PPI network. The identified hub genes were up-regulated in DR and showed a high DR diagnostic value. Regulatory networks of the miRNA- and TF-hub genes can help understand the etiology of disease at the genetic level and optimize treatment strategy. CD14, VSIG4, HCK, and CSF1R were verified to be highly expressed in the vitreous of patients with DR. n RF/6A, CSF1R, and p-ERK1/2 were significantly overexpressed under high glucose conditions, with a statistically significant difference. This investigation identified 15 genes (TYROBP, FCGR3A, CD163, FCGR2A, PTPRC, TLR2, CD14, VSIG4, HCK, CSF1R, LILRB2, ITGAM, CTSS, CD86, and LY86) as hub DR genes, which may serve as a new potential point for the diagnosis and treatment of DR. CSF1R/p-ERK1/2 signaling may promotes the development of retinal neovascularization.

Exploring the role of miR-200 family in regulating CX3CR1 and CXCR1 in lung adenocarcinoma tumor microenvironment: implications for therapeutic intervention

Lung adenocarcinoma (LUAD) is the most common malignant subtype of lung cancer (LC). miR-200 family is one of the prime miR regulators of epithelial-mesenchymal transition (EMT) and worst overall survival (OS) in LC patients. The study aimed to identify and validate the key differentially expressed immune-related genes (DEIRGs) regulated by miR-200 family which may serve for therapeutic aspects in LUAD tumor microenvironment (TME) by affecting cancer progression, invasion, and metastasis. The study identified differentially expressed miRNAs (DEMs) in LUAD, consisting of hsa-miR-200a-3p and hsa-miR-141-5p, respectively. Two highest-degree subnetwork motifs identified from 3-node miRNA FFL were: (i) miR-200a-3p-CX3CR1-SPIB and (ii) miR-141-5p-CXCR1-TBX21. TIMER analysis showed that the expression levels of CX3CR1 and CXCR1 were significantly positively correlated with infiltrating levels of M0-M2 macrophages and natural killer T (NKT) cells. The OS of LUAD patients was significantly affected by lower expression levels of hsa-miR-200a-3p, CX3CR1 and SPIB. These DEIRGs were validated using the human protein atlas (HPA) web server. Further, we validated the regulatory role of hsa-miR-200a-3p in an in-vitro indirect co-culture model using conditioned media from M0, M1 and M2 polarized macrophages (THP-1) and LUAD cell lines (A549 and H1299 cells). The results pointed out the essential role of hsa-miR-200a-3p regulated CX3CL1 and CX3CR1 expression in progression of LC TME. Thus, the study augments a comprehensive understanding and new strategies for LUAD treatment where miR-200 family regulated immune-related genes, especially chemokine receptors, which regulate the metastasis and invasion of LUAD, leading to the worst associated OS.

Comprehensive analysis for the immune related biomarkers of platinum-based chemotherapy in ovarian cancer

BackgroundOvarian cancer (OC) is one of the most lethal gynecological malignancies. This study aimed to identify biomarkers that were sensitive to platinum-based chemotherapeutic agents and can be used in immunotherapy and explore the importance of their mechanisms of action. MethodsRNA-seq profiles and clinicopathological data for OC samples were obtained from The Cancer Genome Atlas (TCGA) and cBioPortal platform, respectively. Platinum-sensitive and platinum-resistant OC samples in the TCGA cohort were selected based on the clinical information. RNA-seq data for 70 OC samples withSingle-sample gene set enrichment analysis (ssGSEA) and unsupervised clustering were used to classify OC patients from the TCGA cohort into clusters with different proportions of infiltrating immune cells. ESTIMATE analysis was used to assess the immune landscape among clusters. Differential expression, univariate Cox regression, and LASSO regression analyses were performed to construct prognostic model. Spearman correlation analysis was conducted to investigate the correlations among immune checkpoint inhibitors (ICIs) and risk score, half-maximal drug inhibitory concentration (IC50) and risk score. ResultsUsing ssGSEA and unsupervised clustering, OC samples were divided into two clusters with different immune cell infiltration. Then, 1715 differentially expressed immune-related genes (DEIRGs) were identified between two clusters, 984 differentially expressed platinum-sensitive related genes (DEPSRGs) between 149 platinum-sensitive and 63 platinum-resistant OC samples were identified, and 5384 differentially expressed genes (DEGs) between 380 OC and 194 normal samples were detected from the TCGA cohort. Six biomarkers (GMPPB, SRPK1, STC1, PRSS16, HPDL, and SPTSSB) were detected to establish a prognostic model. The OC patients in the TCGA cohort were classified into high- and low-risk groups. The receive operating characteristic (ROC) curve was plotted and demonstrated that the prognostic model performed well with the area under ROC curve (AUC) greater than 0.6. The expressions of 5 ICIs, including CD200, TNFRSF18, CD160, CD200R1, and CD274 (PD-L1), were significantly different between two risk groups, and the risk score was significant negative associated with CTLA4, TNFRSF4, TNFRSF18, and CD274. Moreover, there were significant differences in IC50 of 10 chemo drugs between two risk groups, patients in the high-risk group could be more resistant to po0tinib, dasatinib, and neratinib. ConclusionIn summary, this study constructed a novel prognostic model based on six prognostic biomarkers, including GMPPB, SRPK1, STC1, PRSS16, HPDL, and SPTSSB, which can be utilized for predicting the prognosis of OC patients. These biomarkers were the potential therapeutic targets.

Immune-related gene IL17RA as a diagnostic marker in osteoporosis.

Objectives: Bone immune disorders are major contributors to osteoporosis development. This study aims to identify potential diagnostic markers and molecular targets for osteoporosis treatment from an immunological perspective. Method: We downloaded dataset GSE56116 from the Gene Expression Omnibus database, and identified differentially expressed genes (DEGs) between normal and osteoporosis groups. Subsequently, differentially expressed immune-related genes (DEIRGs) were identified, and a functional enrichment analysis was performed. A protein-protein interaction network was also constructed based on data from STRING database to identify hub genes. Following external validation using an additional dataset (GSE35959), effective biomarkers were confirmed using RT-qPCR and immunohistochemical (IHC) staining. ROC curves were constructed to validate the diagnostic values of the identified biomarkers. Finally, a ceRNA and a transcription factor network was constructed, and a Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed to explore the biological functions of these diagnostic markers. Results: In total, 307 and 31 DEGs and DEIRGs were identified, respectively. The enrichment analysis revealed that the DEIRGs are mainly associated with Gene Ontology terms of positive regulation of MAPK cascade, granulocyte chemotaxis, and cytokine receptor. protein-protein interaction network analysis revealed 10 hub genes: FGF8, KL, CCL3, FGF4, IL9, FGF9, BMP7, IL17RA, IL12RB2, CD40LG. The expression level of IL17RA was also found to be significantly high. RT-qPCR and immunohistochemical results showed that the expression of IL17RA was significantly higher in osteoporosis patients compared to the normal group, as evidenced by the area under the curve Area Under Curve of 0.802. Then, we constructed NEAT1-hsa-miR-128-3p-IL17RA, and SNHG1-hsa-miR-128-3p-IL17RA ceRNA networks in addition to ERF-IL17RA, IRF8-IL17RA, POLR2A-IL17RA and ERG-IL17RA transcriptional networks. Finally, functional enrichment analysis revealed that IL17RA was involved in the development and progression of osteoporosis by regulating local immune and inflammatory processes in bone tissue. Conclusion: This study identifies the immune-related gene IL17RA as a diagnostic marker of osteoporosis from an immunological perspective, and provides insight into its biological function.

Identification of novel immune-related biomarker and therapeutic drugs in Parkinson disease via integrated bioinformatics analysis.

The present study was designed to identify immune-related biomarker and candidate drugs for Parkinson disease (PD) by weighted gene co-expression network analysis. Differentially expressed genes were identified in PD and healthy samples in the Gene Expression Omnibus (GEO) database. Besides, immune-related genes were obtained from the immunology database. Then, a co-expression network was constructed by the weighted gene co-expression network analysis package. Diagnostic model for PD was constructed by Lasso and multivariate Cox regression. Furthermore, differentially expressed genes (DEGs) were used to establish PPI and competing endogenous RNA (ceRNA) networks. Functional enrichment and pathway analysis were performed. Drug-hub gene interaction analysis was performed via DGIdb database. PD samples and normal samples were found to have 220 upregulated genes and 216 downregulated genes in the GSE6613 dataset. The differentially expressed genes contained 50 immune-related genes, with 40 upregulated genes and 10 downregulated genes. We obtained 7 hub genes by intersecting the DEGs and candidate hub genes. As potential diagnostic markers, 2 immune-related DEGs were identified among the 7 hub genes. According to functional enrichment analysis, these DEGs were mainly enriched in immune response, inflammatory response, and cytokine-cytokine receptor interactions. Totally, we obtained 182 drug-gene interaction pairs in Drug-Gene Interaction database (DGIdb). Our results revealed crucial genes and candidate drugs for PD patients and deepen our understanding of the molecular mechanisms involved in PD.