Identification Of Immune-related Genes Research Articles

Identification of Immune-Related Genes as Potential Biomarkers in Early Septic Shock

Plain Language SummarySeptic shock is a dangerous condition that happens when an infection spreads through the body and triggers a strong immune response, leading to the failure of multiple organs. Recognizing and treating septic shock quickly is crucial to prevent lasting damage to the body’s organs. However, doctors currently do not have highly effective tools to diagnose septic shock early. In this study, we looked at genetic information from patients with early septic shock. We used a large public database to find patterns in gene activity that could help identify the condition. By analyzing the genes, we could tell which types of immune cells were involved. We discovered that certain immune cells, like neutrophils, M0 macrophages (a kind of white blood cell that helps fight infections), and natural killer cells, were more active in patients with septic shock. We also found genes that are active during immune responses and disease progression. These genes were mostly involved in the body’s defense system, cell communication, and energy use. Among these genes, six stood out as being closely connected to M0 macrophages. These six genes could potentially serve as early warning signs for doctors to detect septic shock, as they were good at distinguishing between patients with and without the condition. In summary, the study identified six genes that might be useful for spotting septic shock early on. These findings could lead to better diagnostic tools, helping doctors to treat patients before their condition becomes critical.

Identification of immune-related genes and small-molecule drugs in hypertension-induced left ventricular hypertrophy based on machine learning algorithms and molecular docking.

Left ventricular hypertrophy (LVH) is a common consequence of hypertension and can lead to heart failure. The immune response plays an important role in hypertensive LVH; however, there is no comprehensive method to investigate the mechanistic relationships between immune response and hypertensive LVH or to find novel therapeutic targets. This study aimed to screen hub immune-related genes involved in hypertensive LVH as well as to explore immune target-based therapeutic drugs. RNA-sequencing data from a mouse model generated by angiotensin II infusion were subjected to weighted gene co-expression network analysis (WGCNA) to identify core expression modules. Machine learning algorithms were applied to screen immune-related LVH characteristic genes. Heart structures were evaluated by echocardiography and cardiac magnetic resonance imaging (CMRI). Validation of hub genes was conducted by RT-qPCR and western blot. Using the Connectivity Map database and molecular docking, potential small-molecule drugs were explored. A total of 1215 differentially expressed genes were obtained, most of which were significantly enriched in immunoregulation and collagen synthesis. WGCNA and multiple machine learning strategies uncovered six hub immune-related genes (Ankrd1, Birc5, Nuf2, C1qtnf6, Fcgr3, and Cdca3) that may accurately predict hypertensive LVH diagnosis. Immune analysis revealed that fibroblasts and macrophages were closely correlated with hypertensive LVH, and hub gene expression was significantly associated with these immune cells. A regulatory network of transcription factor-mRNA and a ceRNA network of miRNA-lncRNA was established. Notably, six hub immune-related genes were significantly increased in the hypertensive LVH model, which were positively linked to left ventricle wall thickness. Finally, 12 small-molecule compounds with the potential to reverse the high expression of hub genes were ruled out as potential therapeutic agents for hypertensive LVH. This study identified and validated six hub immune-related genes that may play essential roles in hypertensive LVH, providing new insights into the potential pathogenesis of cardiac remodeling and novel targets for medical interventions.

Identification of immune-related genes and integrated analysis of immune-cell infiltration in melanoma.

This study was conducted to screen out immune-related genes in connection with the prognosis of melanoma, construct a prognosis model and explore the relevant mechanisms. 1973 genes associated with immune system were derived from the Immport database, and RNA-seq data of melanoma and information of patients were searched from the Xena database. Cox univariate analysis, Lasso analysis and Cox multivariate analysis were used to screen out six genes to construct the model. Then the risk scores were estimated for patients based on our constructed prognosis model. Estimate was used to affirm that the model was about immune infiltration, and CIBERSORT was used to screen out immune cells associated with prognosis. TIDE was applied to predict the efficacy of immunotherapy. Finally, GSE65904 and GSE19234 were used to confirm the effectiveness of the model. ADCYAP1R1, GPI, NTS might cause poor prognosis while IFITM1, KIR2DL4, LIF were more likely conductive to prognosis of melanoma patients and a model of prognosis was constructed on the basis of these six genes. The effectiveness of the model has been proven by the ROC curve, and the miRNAs targeting the screened genes were found out, suggesting that the immune system might impact on the prognosis of melanoma by T cell CD8+, T cell CD4+ memory and NK cells. In this study, the screened six genes were associated with the prognosis of melanoma, which was conductive to clinical prognostic prediction and individualized treatment strategy.

Identification of immune-related genes in diagnosing retinopathy of prematurity with sepsis through bioinformatics analysis and machine learning.

Background: There is increasing evidence indicating that immune system dysregulation plays a pivotal role in the pathogenesis of retinopathy of prematurity (ROP) and sepsis. This study aims to identify key diagnostic candidate genes in ROP with sepsis. Methods: We obtained publicly available data on ROP and sepsis from the gene expression omnibus database. Differential analysis and weighted gene correlation network analysis (WGCNA) were performed to identify differentially expressed genes (DEGs) and key module genes. Subsequently, we conducted functional enrichment analysis to gain insights into the biological functions and pathways. To identify immune-related pathogenic genes and potential mechanisms, we employed several machine learning algorithms, including Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). We evaluated the diagnostic performance using nomogram and Receiver Operating Characteristic (ROC) curves. Furthermore, we used CIBERSORT to investigate immune cell dysregulation in sepsis and performed cMAP analysis to identify potential therapeutic drugs. Results: The sepsis dataset comprised 352 DEGs, while the ROP dataset had 307 DEGs and 420 module genes. The intersection between DEGs for sepsis and module genes for ROP consisted of 34 genes, primarily enriched in immune-related pathways. After conducting PPI network analysis and employing machine learning algorithms, we pinpointed five candidate hub genes. Subsequent evaluation using nomograms and ROC curves underscored their robust diagnostic potential. Immune cell infiltration analysis revealed immune cell dysregulation. Finally, through cMAP analysis, we identified some small molecule compounds that have the potential for sepsis treatment. Conclusion: Five immune-associated candidate hub genes (CLEC5A, KLRB1, LCN2, MCEMP1, and MMP9) were recognized, and the nomogram for the diagnosis of ROP with sepsis was developed.

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.

Identification of immune-related genes in the prognosis of head and neck cancer using a novel prognostic signature model

Increasing evidence indicates that the immune response plays a critical role in the development of head and neck cancer (HNC). We aimed to develop an immune-related gene signature and evaluate its prognostic value in patients with HNC. We retrieved an HNC cohort from The Cancer Genome Atlas database and divided the samples into high-risk and low-risk groups based on the median of the immune and stromal scores. We performed Venn and Cox analyses to identify the immune-related DEGs to use in our prognostic model. We evaluated the correlation between the model and immune-cell infiltration and validated the prognostic value of the model by applying it to 2 external HNC cohorts. We identified 7 DEGs-CCR4, WDFY4, VCAM1, LYZ, VSIG4, XIRP1, and CMKLR1-to use in our prognostic model and validated the model by applying it to 2 external HNC cohorts. We found that risk scores based on the model could reflect the status of the tumor microenvironment and that VSIG4 might be associated with lymph node metastasis in HNC. We developed a highly accurate immune-related prognostic 7-gene model in HNC predication, indicating that these 7 genes play critical roles in the tumor microenvironment.

Identification of immune-related genes in acute myocardial infarction based on integrated bioinformatical methods and experimental verification.

Acute myocardial infarction (AMI) is one of the leading causes of death worldwide. The etiology of AMI is complex and has not been fully defined. In recent years, the role of immune response in the development, progression and prognosis of AMI has received increasing attention. The aim of this study was to identify key genes associated with the immune response in AMI and to analyze their immune infiltration. The study included a total of two GEO databases, containing 83 patients with AMI and 54 healthy individuals. We used the linear model of microarray data (limma) package to find the differentially expressed genes associated with AMI, performing weighted gene co-expression analysis (WGCNA) to further identify the genes associated with inflammatory response to AMI. We found the final hub genes through the protein-protein interaction (PPI) network and least absolute shrinkage and selection operator (LASSO) regression model. To verify the above conclusions, we constructed mice AMI model, extracting myocardial tissue to perform qRT-PCR. Furthermore, the CIBERSORT tool for immune cells infiltration analysis was also carried out. A total of 5,425 significant up-regulated and 2,126 down-regulated genes were found in GSE66360 and GSE24519. A total of 116 immune-related genes in close association with AMI were screened by WGCNA analysis. These genes were mostly clustered in the immune response on the basis of GO and KEGG enrichment. With construction of PPI network and LASSO regression analysis, this research found three hub genes (SOCS2, FFAR2, MYO10) among these differentially expressed genes. The immune cell infiltration results revealed that significant differences could be found on T cells CD4 memory activated, Tregs (regulatory T cells), macrophages M2, neutrophils, T cells CD8, T cells CD4 naive, eosinophils between controls and AMI patients.

Identification of immune-related genes and patient selection for hepatocellular carcinoma immunotherapy.

Hepatocellular carcinoma (HCC) is a malignant disease with a poor prognosis. Among the treatment strategies for HCC, tumor immunotherapy (TIT) is a promising research hotspot, in which identifying novel immune-related biomarkers and selecting suitable patient population are urgent issues to be solved. In this study, an abnormal expression map of HCC cell genes was constructed using public high-throughput data from 7,384 samples (3,941 HCC vs. 3,443 non-HCC tissues). Through single-cell RNA sequencing (scRNA-seq) cell trajectory analysis, the genes defined as potential drivers of HCC cell differentiation and development were selected. By screening for both immune-related genes and those associated with high differentiation potential in HCC cell development, a series of target genes were identified. Coexpression analysis was performed using Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) to find the specific candidate genes involved in similar biological processes. Subsequently, nonnegative matrix factorization (NMF) was conducted to select patients suitable for HCC immunotherapy based on the coexpression network of candidate genes. HSP90AA1, CDK4, HSPA8, HSPH1, and HSPA5 were identified as promising biomarkers for prognosis prediction and immunotherapy of HCC. Through the use of our molecular classification system, which was based on a function module containing 5 candidate genes, patients with specific characteristics were found to be suitable candidates for TIT. These findings provide new insights into the selection of candidate biomarkers and patient populations for future HCC immunotherapy.

Identification of immune-related genes in diagnosing atherosclerosis with rheumatoid arthritis through bioinformatics analysis and machine learning.

Increasing evidence has proven that rheumatoid arthritis (RA) can aggravate atherosclerosis (AS), and we aimed to explore potential diagnostic genes for patients with AS and RA. We obtained the data from public databases, including Gene Expression Omnibus (GEO) and STRING, and obtained the differentially expressed genes (DEGs) and module genes with Limma and weighted gene co-expression network analysis (WGCNA). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis, the protein-protein interaction (PPI) network, and machine learning algorithms [least absolute shrinkage and selection operator (LASSO) regression and random forest] were performed to explore the immune-related hub genes. We used a nomogram and receiver operating characteristic (ROC) curve to assess the diagnostic efficacy, which has been validated with GSE55235 and GSE73754. Finally, immune infiltration was developed in AS. The AS dataset included 5,322 DEGs, while there were 1,439 DEGs and 206 module genes in RA. The intersection of DEGs for AS and crucial genes for RA was 53, which were involved in immunity. After the PPI network and machine learning construction, six hub genes were used for the construction of a nomogram and for diagnostic efficacy assessment, which showed great diagnostic value (area under the curve from 0.723 to 1). Immune infiltration also revealed the disorder of immunocytes. Six immune-related hub genes (NFIL3, EED, GRK2, MAP3K11, RMI1, and TPST1) were recognized, and the nomogram was developed for AS with RA diagnosis.

Identification of Immune-Related Genes and Small-Molecule Drugs in Interstitial Cystitis/Bladder Pain Syndrome Based on the Integrative Machine Learning Algorithms and Molecular Docking.

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic, severely distressing clinical syndrome characterized by bladder pain and pressure perceptions. The origin and pathophysiology of IC/BPS are currently unclear, making it difficult to diagnose and formulate successful treatments. Our study is aimed at investigating the role of immune-related genes in the diagnosis, progression, and therapy of IC/BPS. The gene expression datasets GSE11783, GSE11839, GSE28242, and GSE57560 were retrieved from the GEO database for further analysis. Immune-related IC/BPS differentially expressed genes (DEGs) were identified by limma. Three distinct machine learning approaches, least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forest (RF), were used to find the immune-related IC characteristic genes. Nomogram and receiving operator curves (ROC) were plotted to measure characteristic effectiveness. Using the CMap database and the molecular docking approach, potential small-molecule medicines were found and verified. Consensus cluster analysis was also performed to separate the IC/BPS samples into immunological subtypes. A total of 24 immune-related IC/BPS-DEGs were identified. When compared to the normal control group, the IC/BPS cohort had significantly more immune cell infiltration. Integrative machine learning methods discovered 5 IC/BPS characteristic genes (RASGRP1, PPBP, RBP4, CR2, and PROS2) that may predict IC/BPS diagnosis and immune cell infiltration. Furthermore, two immunological subgroups with substantial variations in immune cell infiltration across IC/BPS samples were identified, which were named cluster1 and cluster2, with the hallmark genes having greater expression in cluster2. Finally, bumetanide was shown to have the potential to be a medication for the treatment of IC/BPS, and it performed well in terms of its molecular binding with RASGRP1. We found and validated 5 immune-related IC/BPS genes (RASGRP1, PPBP, RBP4, CR2, and PROS2) and 2 IC/BPS immune subtypes. In addition, bumetanide was discovered to be a potential drug for treating IC/BPS, which may provide new insight into the diagnosis and immune therapy of IC/BPS patients.

Identification of immune-related genes and development of a prognostic model in mantle cell lymphoma.

The immune landscape, prognostic model, and molecular variations of mantle cell lymphoma (MCL) remain unclear. Hence, an integrated bioinformatics analysis of MCL datasets is required for the development of immunotherapy and the optimization of targeted therapies. Data were obtained from the Gene Expression Omnibus (GEO) database (GSE32018, GSE45717 and GSE93291). The differentially expressed immune-related genes were selected, and the hub genes were screened by three machine learning algorithms, followed by enrichment and correlation analyses. Next, MCL molecular clusters based on the hub genes were identified by K-Means clustering, the probably approximately correct (PAC) algorithm, and principal component analysis (PCA). The landscape of immune cell infiltration and immune checkpoint molecules in distinct clusters was explored by single-sample gene-set enrichment analysis (ssGSEA) as well as the CIBERSORT and xCell algorithms. The prognostic genes and prognostic risk score model for MCL clusters were identified by least absolute shrinkage and selection operator (LASSO)-Cox analysis and cross-validation for lambda. Correlation analysis was performed to explore the correlation between the screened prognostic genes and immune cells or immune checkpoint molecules. Four immune-related hub genes (CD247, CD3E, CD4, and GATA3) were screened in MCL, mainly enriched in the T-cell receptor signaling pathway. Based on the hub genes, two MCL molecular clusters were recognized. The cluster 2 group had a significantly worse overall survival (OS), with down-regulated hub genes, and a variety of activated immune effector cells declined. The majority of immune checkpoint molecules had also decreased. An efficient prognostic model was established, including six prognostic genes (LGALS2, LAMP3, ICOS, FCAMR, IGFBP4, and C1QA) differentially expressed between two MCL clusters. Patients with a higher risk score in the prognostic model had a poor prognosis. Furthermore, most types of immune cells and a range of immune checkpoint molecules were positively correlated with the prognostic genes. Our study identified distinct molecular clusters based on the immune-related hub genes, and showed that the prognostic model affected the prognosis of MCL patients. These hub genes, modulated immune cells, and immune checkpoint molecules might be involved in oncogenesis and could be potential prognostic biomarkers in MCL.

Transcriptome analysis provides novel insights into the immune mechanisms of Macrobrachium nipponense during molting

Molting is a basic physiological behavior of the Oriental river prawn (Macrobrachium nipponense), however, the gene expression patterns and immune mechanisms during the molting process of Oriental river prawn are unclear. In the current study, the gene expression levels of the hepatopancreas of the Oriental river prawn at different molting stages (pre-molting, Prm; mid-molting, Mm; and post-molting, Pom) were detected by mRNA sequencing. A total of 1721, 551, and 1054 differentially expressed genes (DEGs) were identified between the Prm hepatopancreas (PrmHe) and Mm hepatopancreas (MmHe), MmHe and Pom hepatopancreas (PomHe) and PrmHe and PomHe, respectively. The results showed that a total of 1151 DEGs were annotated into 316 signaling pathways, and the significantly enriched immune-related pathways were "Lysosome", "Hippo signaling pathway", "Apoptosis", "Autophagy-animal", and "Endocytosis". The qRT-PCR verification results of 30 randomly selected DEGs were consistent with RNA-seq. The expression patterns of eight immune related genes in different molting stages of the Oriental river prawn were analyzed by qRT-PCR. The function of Caspase-1 (CASP1) was further investigated by bioinformatics, qRT-PCR, and RNAi analysis. CASP1 has two identical conserved domains: histidine active site and pentapeptide motif, and the expression of CASP1 is the highest in ovary. The expression levels of triosephosphate isomerase (TPI), Cathepsin B (CTSB) and Hexokinase (HXK) were evaluated after knockdown of CASP1. This research provides a valuable basis to improve our understanding the immune mechanisms of Oriental river prawns at different molting stages. The identification of immune-related genes is of great significance for enhancing the immunity of the Oriental river prawn, or other crustaceans, by transgenic methods in the future.

Identification of immune-related genes in atopic dermatitis, contact dermatitis, and psoriasis

Abstract Background: The exact mechanisms and targeted therapies for atopic dermatitis (AD), contact dermatitis (CD), and psoriasis (PS) remain unknown. Objectives: This study aimed to identify the biomarkers related to immune and novel therapeutic drugs for AD, CD, and PS. Methods: The GSE153007 dataset including 12 AD, 9 CD, and 14 PS samples and 40 control samples, which was obtained from the Gene Expression Omnibus database and analyzed. The immune infiltration level of each sample was then evaluated using the single-sample gene set enrichment analysis (ssGSEA). Then, we screened for immune-related differentially expressed genes (DEGs) that overlapped. The Database for Annotation, Visualization, and Integrated Discovery database was used to perform the gene ontology (GO) biological process. Furthermore, using search tool for the retrieval of interaction gene (STRING), the protein-protein interaction (PPI) was predicted on immune-related DEGs. We also searched the DGIdb database for novel therapeutic drugs for AD, CD, and PS. Results: According to ssGSEA results, most immune cells were highly infiltrated in the disease group. GO analysis indicated that AD, CD, and PS were enriched in signal transduction, inflammatory response, immune response, and innate immune response. We further found hub genes related to AD (CD4, ITGAM), CD (CD8A, CD86), and PS (CD4, CD8A) from PPI network. Moreover, the drug prediction indicated that drugs targeting CSF1R was the most effective for AD, whereas drugs targeting FCGR3A and CD86 were more effective for CD and PS. Conclusion: These immune-associated genes such as FCGR3A, CD86, and CSF1R might be regarded as therapeutic targets for patients with AD, CD, and PS.

Identification of Immune-Related Genes in Patients with Acute Myocardial Infarction Using Machine Learning Methods.

ObjectiveThis study aimed to analyze immune-related genes and immune cell components in the peripheral blood of patients with acute myocardial infarction (AMI).MethodsSix datasets were obtained from the GEO repository comprising 88 healthy samples and 215 AMI samples. We performed the weighted gene co-expression analysis (WGCNA) and five machine learning (ML) methods to identify immune-related genes and construct diagnostic models. CIBERSORT algorithm was adopted for the assessment of the degree of immune infiltration. Finally, RT-PCR, immunofluorescence double and immunohistochemistry were conducted to analyze the expression level of the identification of featured immune-related genes and localization relationship in heart tissue of AMI mouse model.ResultsA total of 496 immune-related DEGs were obtained between AMI and normal samples. WGCNA finally determined the co-expression modules that showed the most significantly positively associated with AMI (r=0.41; P<0.001). Among the five ML models, XGBoost had the highest AUC (0.849) and accuracy (0.812) to discriminate patients with AMI from normal in the validation sets. Furthermore, we found that the proportion of chemokine receptor (CCR), macrophages, neutrophils, and Treg cells in the AMI groups was significantly higher than that in the normal groups. In vitro RT-PCR verification revealed that SOCS3, MMP9, and AQP9 expression increased significantly in the AMI mouse model. Among the 22 immune cells, AQP9, MMP9, and SOCS3 displayed the strongest positive correlation with neutrophils. In MI-mice, MPO stained strongly along the lateral cardiomyocytes, whereas it was weaker in sham mice. Combined immunofluorescence was observed in same parts of the cytoplasm of cardiomyocytes in myocardial infarction area, indicating co-localization of MPO with MMP9 and SOCS3 in these areas, respectively.ConclusionImmune-related genes and immune cells are intimately related to AMI. Constructing different ML models based on these biomarkers could be a valuable approach to diagnosing AMI in clinical practice.

Identification of Immune-Related Genes for Risk Stratification in Multiple Myeloma Based on Whole Bone Marrow Gene Expression Profiling.

Background: Multiple myeloma (MM) is characterized by abnormal proliferation of bone marrow clonal plasma cells. Tumor immunotherapy, a new therapy that has emerged in recent years, offers hope to patients, and studying the expression characteristics of immune-related genes (IRGs) based on whole bone marrow gene expression profiling (GEP) in MM patients can help guide personalized immunotherapy. Methods: In this study, we explored the potential prognostic value of IRGs in MM by combining GEP and clinical data from the GEO database. We identified hub IRGs and transcription factors (TFs) associated with disease progression by Weighted Gene Co-expression Network Analysis (WGCNA), and modeled immune-related prognostic signature by univariate and multivariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis. Subsequently, the prognostic ability of signature was verified by multiple statistical methods. Moreover, ssGSEA and GSEA algorithm reveled different immunological characteristics and biological function variation in different risk groups. We mapped the hub IRGs by protein-protein interaction network (PPI) and extracted the top 10 ranked genes. Finally, we conducted vitro assays on two alternative IRGs. Results: Our study identified a total of 14 TFs and 88 IRGs associated with International Staging System (ISS). Ten IRGs were identified by Cox -LASSO regression analysis, and used to develop optimal prognostic signature for overall survival (OS) in MM patients. The 10-IRGs were BDNF, CETP, CD70, LMBR, LTBP1, NENF, NR1D1, NR1H2, PTK2B and SEMA4. In different groups, risk signatures showed excellent survival prediction ability, and MM patients also could be stratified at survival risk. In addition, IRF7 and SHC1 were hub IRGs in PPI network, and the vitro assays proved that they could promote tumor progression. Notably, ssGSEA and GSEA results confirmed that different risk groups could accurately indicate the status of tumor microenvironment (TME) and activation of biological pathways. Conclusion: Our study suggested that immune-related signature could be used as prognostic markers in MM patients.

Identification of Immune-Related Genes Concurrently Involved in Critical Illnesses Across Different Etiologies: A Data-Driven Analysis.

Severe trauma and sepsis can lead to multiple organ dysfunction syndrome, which is a leading cause of death in intensive care units with mortality rates in excess of 50%. In addition to infection, the degree of immuno-inflammatory response also influences the outcome. The genomic changes observed after a variety of pathophysiological insults, such as trauma, sepsis, burns are similar, and consist of innate immune activation and adaptive immunity suppression. However, the characteristics of the shared mechanisms of aforementioned critical illnesses and the clinical relevance remain less explored. In the present study, we performed a data analysis to identify functional genes concurrently involved in critical illnesses across differing etiologies (trauma and sepsis derived from community-acquired pneumonia/abdominal source) and explored the shared signaling pathways these common genes involved in to gain insight into the underlying molecular mechanisms. A number of immune-related biological functions were found to be dysregulated in both trauma and sepsis in the present study, so we continued to identify immune-related common genes, profiled the immune cell proportion, and explored the relationships between them. The diagnostic and prognostic value of the immune-related common genes was also evaluated to address their potential clinical utilization as novel biomarkers. Notably, we identified a list of 14 immune-related genes concurrently dysregulated in trauma and sepsis showing favorable diagnostic value, among which S100P can predict prognosis of sepsis patients. Moreover, a spectrum of immune cell subsets including naïve B cells, CD8+ T cells, CD4+ memory resting T cells, activated NK cells, resting dendritic cells, plasma cells, Tregs, macrophages M0 and macrophages M1 was found to be concurrently dysregulated in both trauma and sepsis, and a close relation between above identified immune-related genes and immune cell subsets was observed. Our data-driven findings lay a foundation for future research to elucidate the pathophysiology regarding the aspect of inflammatory and immune response in critical illnesses, and suggest future studies focus on interpreting the function roles of the identified immune-related genes, as well as the reactive immune cell subsets.

Identification of Immune-Related Genes for Establishment of Prognostic Index in Hepatocellular Carcinoma.

Background: Immune checkpoint inhibitor (ICI) therapy has been proved to be a promising therapy to many types of solid tumors. However, effective biomarker for estimating the response to ICI therapy and prognosis of hepatocellular carcinoma (HCC) patients remains underexplored. The aim of this study is to build a novel immune-related prognostic index based on transcriptomic profiles.Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to identify immune-related hub genes that are differentially expressed in HCC cohorts. Next, univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to detect hub genes associated to overall survival (OS). To validate the immune-related prognostic index, univariate and multivariate Cox regression analysis were performed. CIBERSORT and ESTIMATE were used to explore the tumor microenvironment and immune infiltration level.Results: The differential expression analysis detected a total of 148 immune-related genes, among which 25 genes were identified to be markedly related to overall survival in HCC patients. LASSO analysis yielded 10 genes used to construct the immune-related gene prognostic index (IRGPI), by which a risk score is computed to estimate low vs. high risk indicating the response to ICI therapy and prognosis. Further analysis confirmed that this immune-related prognostic index is an effective indicator to immune infiltration level, response to ICI treatment and OS. The IRGPI low-risk patients had better overall survival (OS) than IRGPI high-risk patients on two independent cohorts. Moreover, we found that IRGPI high-risk group was correlated with high TP53 mutation rate, immune-suppressing tumor microenvironment, and these patients acquired less benefit from ICI therapy. In contrast, IRGPI-low risk group was associated with low TP53 and PIK3CA mutation rate, high infiltration of naive B cells and T cells, and these patients gained relatively more benefit from ICI therapy.

Identification of immune-related genes and susceptible population of pulmonary tuberculosis by constructing TF-miRNA-mRNA regulatory network

We aimed to explore the potential biomarkers and susceptible population for early diagnosis and treatment of tuberculosis (TB). Ten hub differentially expressed TB-related genes (DETRGs) from GSE83456 dataset were screened with the “limma” package and the GeneCards database. Unsupervised clustering was utilized to identify susceptible population among TB patients based on 10 hub DETRGs. TRANSFAC, MirTarbase, miRanda and TargetScan was used to predict microRNAs and transcription factors (TFs) and construct TF-miRNA-mRNA regulatory network. The results showed that a total of 266 DEGs were identified. Functional analysis mainly enriched in interferon pathway, cytokine and receptor interaction and host defense response to virus, while the four-module genes screened were closely related to interferon-γ signal transduction pathway as well. Based on 10 DETRGs, TB patients were divided into two clusters with significant differences in neutrophil function and 16 hub miRNAs and 10 hub TFs were predicted. Finally, NFATc1- (miR145) - STAT1 regulatory pathway was identified as the critical regulatory pathway, which mediates cytokine receptor binding, interleukin-1 receptor binding and TNF signaling pathway. Hence, we concluded that immunoheterogeneity exists among TB patients and NFATC1-(miR145)-STAT1 regulatory pathway might be associated with tuberculosis infection, which may be valuable targets for prevention and treatment of tuberculosis.

Identification of Immune-Related Genes in Sepsis due to Community-Acquired Pneumonia.

Background Immunosuppression has a key function in sepsis pathogenesis, so it is of great significance to find immune-related markers for the treatment of sepsis. Methods Datasets of community-acquired pneumonia (CAP) with sepsis from the ArrayExpress database were extracted. Differentially expressed genes (DEGs) between the CAP group and normal group by Limma package were performed. After calculation of immune score through the ESTIMATE algorithm, the DEGs were selected between the high immune score group and the low immune score group. Enrichment analysis of the intersected DEGs was conducted. Further, the protein-protein interaction (PPI) of the intersected DEGs was drawn by Metascape tools. Related publications of the key DEGs were searched in NCBI PubMed through Biopython models, and RT-qPCR was used to verify the expression of key genes. Results 360 intersected DEGs (157 upregulated and 203 downregulated) were obtained between the two groups. Meanwhile, the intersected DEGs were enriched in 157 immune-related terms. The PPI of the DEGs was performed, and 8 models were obtained. In sepsis-related research, eight genes were obtained with degree ≥ 10, included in the models. Conclusion CXCR3, CCR7, HLA-DMA, and GPR18 might participate in the mechanism of CAP with sepsis.

Identification of immune-related genes that predict prognosis and risk of bladder cancer: bioinformatics analysis of TCGA database.

Background: Bladder cancer (BLCA) is the major tumor of the urinary system, and immune-related genes (IRGs) contribute significantly to its initiation and prognosis.Results: A total of 51 prognostic IRGs significantly associated with overall survival were identified. Functional enrichment analysis revealed that these genes were actively involved in tumor-related functions and pathways. Using multivariate Cox regression analysis, we detected 11 optimal IRGs (ADIPOQ, PPY, NAMPT, TAP1, AHNAK, OLR1, PDGFRA, IL34, MMP9, RAC3, and SH3BP2). We validated the prognostic value of this signature in two validation cohorts: GSE13507 (n = 165) and GSE32894 (n = 224). Furthermore, we performed a western blot and found that the expression of these IRGs matched their mRNA expression in TCGA. Moreover, correlations between risk score and immune-cell infiltration indicated that the prognostic signature reflected infiltration by several types of immune cells.Conclusion: We identified and validated an 11-IRG-based risk signature that may be a reliable tool to evaluate the prognosis of BLCA patients and help to devise individualized immunotherapies.Methods: Bioinformatics analysis was performed using TCGA and ImmPort databases. Cox regression was used to identify prognostic signatures. Two external GEO cohorts and western blotting of samples were performed to validate the IRG signature.