Immune-related Differentially Expressed Genes Research Articles

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.

Mining Prognostic Biomarkers of Thyroid Cancer Patients Based on the Immune-Related Genes and Development of a Reliable Prognostic Risk Model.

Tumor immunity serves an essential role in the occurrence and development of thyroid cancer (THCA). The aim of this study is to establish an immune-related prognostic model for THCA patients by using immune-related genes (IRGs). Wilcox test was used to screen the differentially expressed immune-related genes (DEIRGs) in THCA and normal tissues, then the DEIRGs related to prognosis were identified using univariate Cox regression analysis. According to The Cancer Genome Atlas (TCGA) cohort, we developed a least absolute shrinkage and selection operator (LASSO) regression prognostic model and performed validation analyses regard to the predictive value of the model in internal (TCGA) and external (International Cancer Genome Consortium) cohorts respectively. Finally, we analyzed the correlation among the prognostic model, clinical variables, and immune cell infiltration. Eighty-two of 2,498 IRGs were differentially expressed between THCA and normal tissues, and 18 of them were related to prognosis. LASSO Cox regression analysis identified seven DEIRGs with the greatest prognostic value to construct the prognostic model. The risk model showed high predictive value for the survival of THCA in two independent cohorts. The risk score according to the risk model was positively associated with poor survival and the infiltration levels of immune cells, it can evaluate the prognosis of THCA patients independent of any other clinicopathologic feature. The prognostic value and genetic alternations of seven risk genes were evaluated separately. Our study established and verified a dependable prognostic model associated with immune for THCA, both the identified IRGs and immune-related risk model were clinically significant, which is conducive to promoting individualized immunotherapy against THCA.

An Immune-Related Gene Signature for Predicting Survival and Immunotherapy Efficacy in Esophageal Adenocarcinoma.

BACKGROUND Immune checkpoint inhibitor (ICI) therapy has attracted wide attention in the treatment of malignant tumors. This study was designed to build a prognostic model based on immune-related genes for esophageal adenocarcinoma (EAC). MATERIAL AND METHODS The expression of immune-related differentially-expressed genes (IRDEGs) between EAC and normal samples from The Cancer Genome Atlas database was analyzed. Univariate and multivariate Cox regressions were used to identify the prognostic IRDEGs and construct an immune-related gene signature (IRGS) to predict the overall survival (OS) of EAC patients. Then, the molecular mechanisms and immune characteristics were comprehensively analyzed. RESULTS A total of 111 IRDEGs were obtained from the weighted gene co-expression network analysis. Univariate Cox regression analysis showed that 12 IRDEGs (P<0.05 for all) were linked with OS in the EAC patients. Four genes were used to construct the IRGS based on the multivariate Cox regression analysis. Patients in the high-risk group showed worse OS than those in the low-risk group (P<0.001). A high-risk score was related to DNA replication relevant pathways, an increase in mutation rate, and an increase in activated mast cell infiltration. Patients with high-risk scores had lower tumor immune dysfunction and exclusion scores (P<0.001). CONCLUSIONS IRDEGs may be involved in the progression of EAC. The high-risk group is more suitable for immunotherapy, which may provide a reference value for the treatment of clinical EAC patients. Therefore, it is possible to identify the patients who are better suited for ICI therapy.

Bioinformatics Analysis of Immune Infiltration In Endometrial Carcinoma Tumors and its Relationship With Prognosis.

Endometrial carcinoma (EC) is the most common gynecological cancer, but lacks specific targetable markers. In order to explore the immune-related molecules that affect the progression and prognosis of EC, we analyzed the differential expression of genes in different histological grades of the disease. EC-related gene-expression data of different histological grades were downloaded from TCGA and GEO databases. The list of immune-related genes was obtained from the ImmPort database. In order to identity differentially-expressed genes (DEGs), differential-expression analysis was performed. The intersection of DEGs and immune-related genes was termed immune-related differentially-expressed genes (IRDEGs). IRDEGs were enriched in cancer-related functional pathways by gene-correlation analysis and GSEA-enrichment analysis. The association of IRDEGs with immune-cell tumor infiltration and gene polymorphisms was analyzed using IRDEG mRNA and protein-expression data in EC from TCGA and THPA databases. Three IRDEGs, TNFSF15, SEMA3E and TNFSF10, were involved in the analysis of the prognosis of EC patients. IRDEGs were not only related to clinical characteristics but could also affect the prognosis of patients. Gene-correlation and GSEA-enrichment analysis of IRDEGs showed that TNFSF15 and TNFSF10 were co-enriched in the IL2-STAT5 functional pathway. IRDEGs had a significant correlation with a variety of immune-cell types infiltrating EC tumors and were related to EC prognosis. IRDEG mRNA- and protein-expression levels were increased in EC compared to normal tissues. TNFSF15, SEMA3E and TNFSF10 may regulate the progression and prognosis of EC patients by affecting immune-cell infiltration of EC tumors.

Analysis of the characteristics of immune infiltration in endometrial carcinoma and its relationship with prognosis based on bioinformatics.

To explore immune-related molecules that affect the prognosis of endometrial carcinoma (EC) using bioinformatic data mining. The expression data related to EC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. After differential expression analysis, the intersection with immune related genes in the ImmPort database was used to obtain immune related differentially expressed genes (IRDEGs). The correlation between clinicopathological information and the prognosis of IRDEGs was further analyzed to obtain prognosis related differentially expressed immune genes (PRDEIG). Gene correlation analysis and Gene Set Enrichment Analysis (GSEA) enrichment analysis showed that PRDEIG was enriched in cancer-related functional pathways. We then analyzed the relationship between PRDEIG and immune cell infiltration, and further analyzed the mRNA and protein expression of PRDEIG in EC using TCGA and the human protein expression atlas (THPA) databases. After the intersection of the differential expression analysis results and immune-related genes, 4 IRDEGs were obtained: osteoglycin (OGN), LTBP4, CXCL12, and SPP1. After analyzing the relationship between 4 IRDEGs and clinicopathological parameters and prognosis of patients with EC, revealed that only OGN was not only related to tumor immunity, but also affected the prognosis of patients with EC. Gene correlation and GSEA enrichment of OGN were analyzed. The results showed that OGN was significantly enriched in 6 functional pathways: epithelial mesenchymal transition, KRAS signaling up, myogenesis, UV response, allograft rejection and apical junction. In addition, it was also found that OGN was significantly correlated with a variety of immune cells. The results of TCGA and THPA database showed that the mRNA and protein expression levels of OGN decreased in EC. OGN may affect the epithelial mesenchymal transformation (EMT) of tumor by affecting the infiltration of tumor immune cells.

Development and validation of a MUC16 mutation-associated immune prognostic model for lung adenocarcinoma.

Mucin 16 (MUC16) mutation ranks third among all common mutations in lung adenocarcinoma (LUAD), and it has a certain effect on LUAD development and prognostic outcome. This research aimed to analyze the effects of MUC16 mutation on LUAD immunophenotype regulation and determine the prognostic outcome using an immune prognostic model (IPM) built with immune-related genes. The MUC16 mutation status and mRNA expression profiles were analyzed using diverse platforms and among several LUAD patients (n = 691). An IPM was then constructed using differentially expressed immune-related genes (DEIRGs) in MUC16MUT LUAD cases, and the data were compared with those of MUC16WT LUAD cases. The IPM's performance in distinguishing high-risk cases from low-risk ones among 691 LUAD cases was verified. Additionally, a nomogram was built and applied in the clinical setting. Furthermore, a comprehensive IPM-based analysis of how MUC16 mutation affected the tumor immune microenvironment (TIME) of LUAD was performed. MUC16 mutation decreased the immune response in LUAD. As revealed by functional annotation, the DEIRGs in the IPM were most significantly enriched in the humoral immune response function and the immune system disease pathway. Moreover, high-risk cases were associated with increased proportions of immature dendritic cells, neutrophils, and B-cells; enhanced type I interferon T-cell response; and increased expression of PD-1, CTLA-4, TIM-3, and LAG3 when compared with low-risk cases. MUC16 mutation shows potent association with TIME of LUAD. The as-constructed IPM displays high sensitivity to MUC16 mutation status and can be applied to discriminate high-risk LUAD cases from low-risk ones.

Identification and Validation of Hub Immune-Related Genes in Non-Alcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) is the most common progressive liver disease worldwide. It can cause liver cancer and possibly death. Abnormal immune infiltration is involved in the progression of NAFLD. The aim of this study was to identify and validate the hub immune-related genes in NAFLD. Microarray data were downloaded from Gene Expression Omnibus, and immune-related differentially expressed genes (IRDEGs) were obtained. A protein-protein interaction network was used to further screen. The diagnostic value of the IRDEGs was evaluated by receiver operating characteristic curves. Differences in immune infiltration levels were analyzed using single-sample gene set enrichment analysis. Hub IRDEGs were identified by correlation analysis with immune infiltration levels. Finally, molecular experiments were used to confirm the expression of the hub IRDEGs and explore their roles in NAFLD. We obtained 18 IRDEGs. Five hub genes were further identified by protein-protein interaction network, receiver operating characteristic curves and correlation analysis: AQP9, BACH2, CD4, IL17RE and S100A9. Based on functional enrichment analysis, the hub genes were enriched primarily in many immune-related pathways. In NAFLD, AQP9, CD4, and IL17RE expression was significantly reduced, whereas BACH2 and S100A9 expression was elevated. PCR, oil red O staining and triglyceride detection revealed that the knock-down of BACH2 and S100A9 reduced lipid accumulation in NAFLD cells. This study provided insight into the profile of immune infiltration underlying NAFLD and identified AQP9, BACH2, CD4, IL17RE and S100A9 as ancillary diagnostic indicators of NAFLD. And BACH2 and S100A9 might be therapeutic targets for NAFLD.

Identification of key signaling pathways and hub genes related to immune infiltration in Kawasaki disease with resistance to intravenous immunoglobulin based on weighted gene co-expression network analysis.

Background: Kawasaki disease (KD) is an acute vasculitis, that is, the leading cause of acquired heart disease in children, with approximately 10%-20% of patients with KD suffering intravenous immunoglobulin (IVIG) resistance. Although the underlying mechanism of this phenomenon remains unclear, recent studies have revealed that immune cell infiltration may associate with its occurrence. Methods: In this study, we downloaded the expression profiles from the GSE48498 and GSE16797 datasets in the Gene Expression Omnibus database, analyzed differentially expressed genes (DEGs), and intersected the DEGs with the immune-related genes downloaded from the ImmPort database to obtain differentially expressed immune-related genes (DEIGs). Then CIBERSORT algorithm was used to calculate the immune cell compositions, followed by the WGCNA analysis to identify the module genes associated with immune cell infiltration. Next, we took the intersection of the selected module genes and DEIGs, then performed GO and KEGG enrichment analysis. Moreover, ROC curve validation, Spearman analysis with immune cells, TF, and miRNA regulation network, and potential drug prediction were implemented for the finally obtained hub genes. Results: The CIBERSORT algorithm showed that neutrophil expression was significantly higher in IVIG-resistant patients compared to IVIG-responsive patients. Next, we got differentially expressed neutrophil-related genes by intersecting DEIGs with neutrophil-related module genes obtained by WGCNA, for further analysis. Enrichment analysis revealed that these genes were associated with immune pathways, such as cytokine-cytokine receptor interaction and neutrophil extracellular trap formation. Then we combined the PPI network in the STRING database with the MCODE plugin in Cytoscape and identified 6 hub genes (TLR8, AQP9, CXCR1, FPR2, HCK, and IL1R2), which had good diagnostic performance in IVIG resistance according to ROC analysis. Furthermore, Spearman's correlation analysis confirmed that these genes were closely related to neutrophils. Finally, TFs, miRNAs, and potential drugs targeting the hub genes were predicted, and TF-, miRNA-, and drug-gene networks were constructed. Conclusion: This study found that the 6 hub genes (TLR8, AQP9, CXCR1, FPR2, HCK, and IL1R2) were significantly associated with neutrophil cell infiltration, which played an important role in IVIG resistance. In a word, this work rendered potential diagnostic biomarkers and prospective therapeutic targets for IVIG-resistant patients.

Identification and validation of immune-related biomarkers and potential regulators and therapeutic targets for diabetic kidney disease

BackgroundDiabetic kidney disease (DKD) is a major complication of diabetes and the leading cause of end-stage renal disease worldwide. Renal inflammation and infiltration of immune cells contribute to the development and progression of DKD. Thus, the aim of the present study was to identify and validate immune-related biomarkers and analyze potential regulators including transcription factors (TFs), microRNAs (miRNAs), and drugs for DKD.MethodsImmune-related genes from the ImmPort database and glomeruli samples from GSE1009 and GSE30528 were used to identify differentially expressed immune-related genes (DEIRGs) of DKD. The expression level and clinical correlation analyses of DEIRGs were verified in the Nephroseq database. Murine podocytes were cultured to construct the high glucose-induced podocyte injury model. The reliability of the bioinformatics analysis was experimentally validated by RT-qPCR in podocytes. Networks among DEIRGs, regulators, and drugs were constructed to predict potential regulatory mechanisms for DKD.ResultsDKD-associated DEIRGs were identified. CCL19 and IL7R were significantly upregulated in the DKD group and negatively correlated with glomerular filtration rate (GFR). GHR, FGF1, FYN, VEGFA, F2R, TGFBR3, PTGDS, FGF9, and SEMA5A were significantly decreased in the DKD group and positively correlated with GFR. RT-qPCR showed that the relative mRNA expression levels of GHR, FGF1, FYN, TGFBR3, PTGDS, FGF9, and SEMA5A were significantly down-regulated in the high glucose-induced podocyte injury group. The enriched regulators for DEIRGs included 110 miRNAs and 8 TFs. The abnormal expression of DEIRGs could be regulated by 16 established drugs.ConclusionsThis study identified immune-related biomarkers, regulators, and drugs of DKD. The findings of the present study provide novel insights into immune-related diagnosis and treatment of DKD.

Identification of novel biomarkers linking depressive disorder and Alzheimer’s disease based on an integrative bioinformatics analysis

BackgroundPrevious reports revealed that a history of major depressive disorder (MDD) increased the risk of Alzheimer’s disease (AD). The immune disorder is associated with MDD and AD pathophysiology. We aimed to identify differentially expressed immune-related genes (DEIRGs) that are involved in the pathogenesis of MDD and AD.MethodsWe downloaded mRNA expression profiles (GSE76826 and GSE5281) from the Gene Expression Omnibus (GEO) database. The R software was used to identify DEIRGs for the two diseases separately. Functional enrichment analysis and PPI network of DEIRGs were performed. Finally, the relationship between shared DEIRGs and immune infiltrates of AD and MDD were analyzed, respectively.ResultsA total of 121 DEIRGs linking AD and MDD were identified. These genes were significantly enriched in immune-related pathways, such as the JAK-STAT signaling pathway, regulation of chemotaxis, chemotaxis, cytokine-cytokine receptor interaction, and primary immunodeficiency. Furthermore, three shared DEIRGs (IL1R1, CHGB, and NRG1) were identified. Correlation analysis between DEIRGs and immune cells revealed that IL1R1 and NRG1 had a negative or positive correlation with some immune cells both in AD and MDD.ConclusionBoth DEIRGs and immune cell infiltrations play a vital role in the pathogenesis of AD and MDD. Our findings indicated that there are common genes and biological processes between MDD and AD, which provides a theoretical basis for the study of the comorbidity of MDD and AD.

CD8A is a Promising Biomarker Associated with Immunocytes Infiltration in Hyperoxia-Induced Bronchopulmonary Dysplasia.

Bronchopulmonary dysplasia (BPD) refers to a chronic lung disease which is commonly observed in preterm infants. It can usually be caused by several pathological processes that endanger the long-term lung development, such as inflammation and immune dysfunction. In this study, a bioinformatics approach was applied to identify the differentially expressed immune-related genes (DEIRGs). We downloaded the transcriptional profiles (GSE32472 dataset) from the Gene Expression Omnibus (GEO) database and performed gene set enrichment analysis (GSEA). Cell type Identification By Estimating Relative Subsets of RNA Transcripts (CIBERSORT), microenvironment cell populations counter (MCPcounter), and Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) were used for the analysis of the immune cell infiltration landscape of BPD. A weighted co-expression network was subsequently constructed using weighted gene co-expression network analysis (WGCNA) to screen candidate differentially expressed immune related genes (DEIRGs). GSEA results indicated that immune-related pathways were mainly involved in BPD. Ten significantly different immune cell types were observed between BPD and normal groups. A total of 228 DEGs in the turquoise module were identified, and 31 DEIRGs were further identified. Cluster of the differentiation 8 alpha (CD8A) expression was down-regulated in BPD, and its expression was validated by the GSE25286, GSE25293, GSE99633 datasets and qRT-PCR. In addition, CD8A expression was closely associated with immune cells infiltration, especially T cells CD8 and neutrophil. A distinct immune cell infiltration landscape was found between BPD and normal group. CD8A can be a novel candidate biomarker for BPD, which plays an essential role in the onset and progress of hyperoxia-related BPD via the disruption of immune cell functions.