Immune-related Differentially Expressed Genes Research Articles

Construction of an immune-related signature for predicting the ischemic events in patients undergoing carotid endarterectomy.

Background: Inflammatory responses have drawn more attention to atherosclerosis; however, the immune-related genes (IRGs) as a prognostic factor in atherosclerotic plaque remain to be fully elucidated. Here, the purpose of this study was to investigate whether the IRGs could be identified as a reliable biomarker for predicting ischemic events in patients undergoing carotid endarterectomy (CEA). Methods: Two datasets GSE97210 and GSE21545 were downloaded from the Gene Expression Omnibus (GEO) database. The dataset GSE97210 was used to explore the significant pathways and differentially expressed IRGs (DEIRGs) between plaques and controls, which were further screened to identify the prognostic DEIRGs in the GSE21545 dataset. The identification of molecular subgroups with the prognostic gene expression patterns was achieved through nonnegative matrix factorization (NMF) clustering. Functional analyses including GO, KEGG, GSVA, and GSEA analyses, and immune analyses including xCell and ssGSEA algorithms were conducted to elucidate the underlying mechanisms. The prognostic risk model was constructed using the LASSO algorithm and multivariate Cox regression analysis. Results: A total of 796 DEIRGs (including 588 upregulated and 208 downregulated) were identified. Nine prognostic DEIRGs were further screened with univariate Cox regression analysis. Two clusters with different prognosis were grouped based on the prognostic DEIRGs. Immune infiltration analysis shows that cluster 2 with a better prognosis presented with a higher immune response than cluster 1. A prognostic model based on seven IRGs (IL2RA, NR4A2, DES, ERAP2, SLPI, RASGRP1, and AGTR2) was developed and verified. Consistent with the immune analysis of the cluster, the immune infiltration in the low-risk group with a better prognosis was also more active than that in the high-risk group. Finally, a nomogram based on the seven genes was constructed, which might have future implications in clinical care. Conclusion: The expression of immune-related genes is correlated with the immune microenvironment of atherosclerotic patients and could be applied to predict the ischemic events in patients undergoing CEA accurately.

Identification of potential drug targets for vascular dementia and carotid plaques by analyzing underlying molecular signatures shared by them.

BackgroundVascular dementia (VaD) and carotid atherosclerotic plaques are common in the elderly population, conferring a heavy burden on families and society. Accumulating evidence indicates carotid atherosclerotic plaques to be a risk factor for VaD. However, the underlying mechanisms for this association are mainly unknown.Materials and methodsWe analyzed temporal cortex gene expression data of the GSE122063 dataset and gene expression data of the GSE163154 dataset to identify commonly differentially expressed genes (DEGs). Then we performed functional enrichment analysis, immune cell infiltration and evaluation, correlation analysis between differentially expressed immune-related genes (DEIRGs) and immune cells, receiver operating characteristic (ROC) analysis, and drug-gene analysis.ResultsWe identified 41 overlapped DEGs between the VaD and carotid atherosclerosis plaque datasets. Functional enrichment analyses revealed that these overlapped DEGs were mainly enriched in inflammatory and immune-related processes. Immunocyte infiltration and evaluation results showed that M0 macrophages, M2 macrophages, and T cells gamma delta had a dominant abundance in carotid atherosclerosis plaque samples, and M0 macrophages showed a significantly different infiltration percentage between the early and advanced stage plaques group. Resting CD4 memory T cells, M2 macrophages, and naive B cells were the top three highest infiltrating fractions in VaD. Furthermore, B cells and NK cells showed a different infiltration percentage between VaD and matched controls. We identified 12 DEIRGs, and the result of correlation analysis revealed that these DEIRGs were closely related to differentially expressed immune cells. We identified five key DEIRGs based on ROC analysis. The drug-gene interaction analysis showed that four drugs (avacopan, CCX354, BMS-817399, and ASK-8007) could be potential drugs for VaD and carotid atherosclerotic plaques treatment.ConclusionCollectively, these findings indicated that inflammatory and immune-related processes be a crucial common pathophysiological mechanism shared by VaD and carotid plaques. This study might provide new insights into common molecular mechanisms between VaD and carotid plaques and potential targets for the treatment.

Genetic and immune crosstalk between severe burns and blunt trauma: A study of transcriptomic data.

Background: Severe burns and blunt trauma can lead to multiple organ dysfunction syndrome, the leading cause of death in intensive care units. In addition to infection, the degree of immune inflammatory response also affects prognosis. However, the characteristics and clinical relevance of the common mechanisms of these major diseases are still underexplored. Methods: In the present study, we performed microarray data analysis to identify immune-related differentially expressed genes (DEGs) involved in both disease progression in burns and blunt trauma. Six analyses were subsequently performed, including gene enrichment analysis, protein‐protein interaction (PPI) network construction, immune cell infiltration analysis, core gene identification, co-expression network analysis, and clinical correlation analysis. Results: A total of 117 common immune-related DEGs was selected for subsequent analyses. Functional analysis emphasizes the important role of Th17 cell differentiation, Th1 and Th2 cell differentiation, Cytokine-cytokine receptor interaction and T cell receptor signaling pathway in these two diseases. Finally, eight core DEGs were identified using cytoHubba, including CD8A, IL10, CCL5, CD28, LCK, CCL4, IL2RB, and STAT1. The correlation analysis showed that the identified core DEGs were more or less significantly associated with simultaneous dysregulation of immune cells in blunt trauma and sepsis patients. Of these, the downregulation of CD8A and CD28 had a worse prognosis. Conclusion: Our analysis lays the groundwork for future studies to elucidate molecular mechanisms shared in burns and blunt trauma. The functional roles of identified core immune-related DEGs and dysregulated immune cell subsets warrant further in-depth study.

Construction of Prognostic Risk Model for Small Cell Lung Cancer Based on Immune-Related Genes.

Small cell lung cancer (SCLC) is a highly invasive and fatal malignancy. Research at the present stage implied that the expression of immune-related genes is associated with the prognosis in SCLC. Accordingly, it is essential to explore effective immune-related molecular markers to judge prognosis and treat SCLC. Our research obtained SCLC dataset from Gene Expression Omnibus (GEO) and subjected mRNAs in it to differential expression analysis. Differentially expressed mRNAs (DEmRNAs) were intersected with immune-related genes to yield immune-related differentially expressed genes (DEGs). The functions of these DEGs were revealed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Thereafter, we categorized 3 subtypes of immune-related DEGs via K-means clustering. Kaplan-Meier curves analyzed the effects of 3 subtypes on SCLC patients' survival. Single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE validated that the activation of different immune gene subtypes differed significantly. Finally, an immune-related-7-gene assessment model was constructed by univariate-Lasso-multiple Cox regression analyses. Riskscores, Kaplan-Meier curves, receiver operating characteristic (ROC) curves, and independent prognostic analyses validated the prognostic value of the immune-related-7-gene assessment model. As suggested by GSEA, there was a prominent difference in cytokine-related pathways between high- and low-risk groups. As the analysis went further, we discovered a statistically significant difference in the expression of human leukocyte antigen (HLA) proteins and costimulatory molecules expressed on the surface of CD274, CD152, and T lymphocytes in different groups. In a word, we started with immune-related genes to construct the prognostic model for SCLC, which could effectively evaluate the clinical outcomes and offer guidance for the treatment and prognosis of SCLC patients.

Prognostic Index for Nonsmall Cell Lung Cancer Based on Immune-Related Genes Expression.

Immune system dysregulation is associated with tumor incidence and growth. Here, we established an RNA-based individualized immune signature associated with prognosis for nonsmall cell lung cancer (NSCLC) to guide adjuvant therapy. We downloaded publicly accessible data on RNA expression and clinical characteristics of NSCLC from the Cancer Genome Atlas (TCGA). From immune-related genes (IRGs) retrieved from the immunology database and analysis portal (ImmPort) database, we then screened differentially expressed immune-related genes (DEIRGs). Using overall survival (OS) as a clinical endpoint, we identified 26 prognostic DEIRGs via univariate and multivariate Cox regression analysis, and then developed a risk model based on these 26 IRGs with an area under the curve (AUC) of 0.701, and its predictive ability independent from other clinical factors. We also downloaded tumor immune infiltrate data and analyzed the correlations between lymphocytic infiltration with our risk scores, but found no significant association. Furthermore, we retrieved 86 differentially expressed transcription factors (TFs) to assess their regulatory relationships with the 26 prognostic DEIRGs. In summary, we developed a robust risk model to predict survival in patients with NSCLC, based on the expression of 26 IRGs. It provides novel predictive and therapeutic molecular targets.

Identification of Unique Genetic Biomarkers of Various Subtypes of Glomerulonephritis Using Machine Learning and Deep Learning.

(1) Objective: Identification of potential genetic biomarkers for various glomerulonephritis (GN) subtypes and discovering the molecular mechanisms of GN. (2) Methods: four microarray datasets of GN were downloaded from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression profiles of eight GN subtypes. Then, differentially expressed immune-related genes (DIRGs) were identified to explore the molecular mechanisms of GN, and single-sample gene set enrichment analysis (ssGSEA) was performed to discover the abnormal inflammation in GN. In addition, a nomogram model was generated using the R package “glmnet”, and the calibration curve was plotted to evaluate the predictive power of the nomogram model. Finally, deep learning (DL) based on a multilayer perceptron (MLP) network was performed to explore the characteristic genes for GN. (3) Results: we screened out 274 common up-regulated or down-regulated DIRGs in the glomeruli and tubulointerstitium. These DIRGs are mainly involved in T-cell differentiation, the RAS signaling pathway, and the MAPK signaling pathway. ssGSEA indicates that there is a significant increase in DC (dendritic cells) and macrophages, and a significant decrease in neutrophils and NKT cells in glomeruli, while monocytes and NK cells are increased in tubulointerstitium. A nomogram model was constructed to predict GN based on 7 DIRGs, and 20 DIRGs of each subtype of GN in glomeruli and tubulointerstitium were selected as characteristic genes. (4) Conclusions: this study reveals that the DIRGs are closely related to the pathogenesis of GN and could serve as genetic biomarkers in GN. DL further identified the characteristic genes that are essential to define the pathogenesis of GN and develop targeted therapies for eight GN subtypes.

Identification of diagnostic biomarkers and therapeutic targets in peripheral immune landscape from coronary artery disease

BackgroundPeripheral biomarkers are increasingly vital non-invasive methods for monitoring coronary artery disease (CAD) progression. Their superiority in early detection, prognosis evaluation and classified diagnosis is becoming irreplaceable. Nevertheless, they are still less explored. This study aimed to determine and validate the diagnostic and therapeutic values of differentially expressed immune-related genes (DE-IRGs) in CAD.MethodsWe downloaded clinical information and RNA sequence data from the GEO database. We used R software, GO, KEGG and Cytoscape to analyze and visualize the data. A LASSO method was conducted to identify key genes for diagnostic model construction. The ssGSEA analysis was used to investigate the differential immune cell infiltration. Besides, we constructed CAD mouse model (low-density lipoprotein receptor deficient mice with high fat diet) to discover the correlation between the screened genes and severe CAD progress. We further uncovered the role of IL13RA1 might play in atherosclerosis.ResultsA total of 762 differential genes were identified between the peripheral blood of 218 controls and 199 CAD patients, which were significantly associated with infection, immune response and neural activity. 58 DE-IRGs were obtained by overlapping the differentially expressed genes(DEGs) and immune-related genes downloaded from ImmpDb database. Through LASSO regression, CCR9, CER1, CSF2, IL13RA1, INSL5, MBL2, MMP9, MSR1, NTS, TNFRSF19, CXCL2, HTR3C, IL1A, and NR4A2 were distinguished as peripheral biomarkers of CAD with eligible diagnostic capabilities in the training set (AUC = 0.968) and test set (AUC = 0.859). The ssGSEA analysis showed that the peripheral immune cells had characteristic distribution in CAD and also close relationship with specific DE-IRGs. RT-qPCR test showed that CCR9, CSF2, IL13RA1, and NTS had a significant correlation with LDLR−/− mice. IL13RA1 knocked down in RAW264.7 cell lines decreased SCARB1 and ox-LDL-stimulated CD36 mRNA expression, TGF-β, VEGF-C and α-SMA protein levels and increased the production of IL-6, with downregulation of JAK1/STAT3 signal pathway.ConclusionsWe constructed a diagnostic model of advanced-stage CAD based on the screened 14 DE-IRGs. We verified 4 genes of them to have a strong correlation with CAD, and IL13RA1 might participate in the inflammation, fibrosis, and cholesterol efflux process of atherosclerosis by regulating JAK1/STAT3 pathway.

Integrated analysis of immune parameters, miRNA-mRNA interaction, and immune genes expression in the liver of rainbow trout following infectious hematopoietic necrosis virus infection.

Rainbow trout (Oncorhynchus mykiss) is an important economical cold-water fish worldwide. However, infection with infectious hematopoietic necrosis virus (IHNV) has severely restricted the development of aquaculture and caused huge economic losses. Currently, little is known about the immune defense mechanisms of rainbow trout against IHNV. In this study, we detected the changes of immune parameters over different post-infection periods (6-, 12-, 24-, 48-, 72-, 96-, 120-, and 144 hours post-infection (hpi)), mRNA and miRNA expression profiles under 48 hpi (T48L) compared to control (C48L), and key immune-related genes expression patterns in rainbow trout liver following IHNV challenge through biochemical methods, RNA-seq, and qRT-PCR, and the function of miR-330-y was verified by overexpression and silencing in vitro and in vivo. The results revealed that alkaline phosphatase (AKP), alanine aminotransferase (ALT), catalase (CAT), and total superoxide dismutase (T-SOD) activities, and lysozyme (LZM) content showed significant peaks at 48 hpi, whereas malondialdehyde (MDA) content and aspartate aminotransferase (AST) activity decreased continuously during infection, and acid phosphatase (ACP) activity varied slightly. From RNA-seq, a total of 6844 genes and 86 miRNAs were differentially expressed, and numerous immune-related differentially expressed genes (DEGs) involved in RIG-I-like receptor signaling pathway, Toll-like receptor signaling pathway, NOD-like receptor signaling pathway, cytokine-cytokine receptor interaction, and antigen processing and presentation were significantly upregulated in T48Lm group, including IFIH1, DHX58, MAVS, TRAF3, IRF3, IRF7, MX1, TLR3, TLR8, MYD88, NOD1, NOD2, IL-8, CXCR1, CD209, CD83, and TAP1. Integrated analysis identified seven miRNAs (miR-425-x, miR-185-x, miR-338-x, miR-330-y, miR-361-x, miR-505-y, and miR-191-x) that target at least three key immune-related DEGs. Expression analysis showed that IFIH1, DHX58, IRF3, IRF7, MX1, TLR3, TLR8, and MYD88 showed a marked increase after 24 hpi during infection. Further research confirmed TAP1 as one of the targets of miR-330-y, overexpression of miR-330-y with mimics or agomir significantly reduced the expression levels of TAP1, IRF3, and IFN, and the opposite effects were obtained by inhibitor. These results facilitate in-depth understanding of the immune mechanisms in rainbow trout against IHNV.

Comprehensive analysis of a novel signature incorporating lipid metabolism and immune-related genes for assessing prognosis and immune landscape in lung adenocarcinoma.

BackgroundAs the crosstalk between metabolism and antitumor immunity continues to be unraveled, we aim to develop a prognostic gene signature that integrates lipid metabolism and immune features for patients with lung adenocarcinoma (LUAD).MethodsFirst, differentially expressed genes (DEGs) related to lipid metabolism in LUAD were detected, and subgroups of LUAD patients were identified via the unsupervised clustering method. Based on lipid metabolism and immune-related DEGs, variables were determined by the univariate Cox and LASSO regression, and a prognostic signature was established. The prognostic value of the signature was evaluated by the Kaplan–Meier method, time-dependent ROC, and univariate and multivariate analyses. Five independent GEO datasets were employed for external validation. Gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed to investigate the underlying mechanisms. The sensitivity to common chemotherapeutic drugs was estimated based on the GDSC database. Finally, we selected PSMC1 involved in the signature, which has not been reported in LUAD, for further experimental validation.ResultsLUAD patients with different lipid metabolism patterns exhibited significant differences in overall survival and immune infiltration levels. The prognostic signature incorporated 10 genes and stratified patients into high- and low-risk groups by median value splitting. The areas under the ROC curves were 0.69 (1-year), 0.72 (3-year), 0.74 (5-year), and 0.74 (10-year). The Kaplan–Meier survival analysis revealed a significantly poorer overall survival in the high-risk group in the TCGA cohort (p < 0.001). In addition, both univariate and multivariate Cox regression analyses indicated that the prognostic model was the individual factor affecting the overall survival of LUAD patients. Through GSEA and GSVA, we found that tumor progression and inflammatory and immune-related pathways were enriched in the high-risk group. Additionally, patients with high-risk scores showed higher sensitivity to chemotherapeutic drugs. The in vitro experiments further confirmed that PSMC1 could promote the proliferation and migration of LUAD cells.ConclusionsWe developed and validated a novel signature incorporating both lipid metabolism and immune-related genes for all-stage LUAD patients. This signature can be applied not only for survival prediction but also for guiding personalized chemotherapy and immunotherapy regimens.

Nalidixic acid potentiates the antitumor activity in sorafenib-resistant hepatocellular carcinoma via the tumor immune microenvironment analysis.

Sorafenib resistance is often developed and impedes the benefits of clinical therapy in hepatocellular carcinoma (HCC) patients. However, the relationship between sorafenib resistance and tumor immune environment and adjuvant drugs for sorafenib-resistant HCC are not systemically identified. This study first analyzed the expression profiles of sorafenib-resistant HCC cells to explore immune cell infiltration levels and differentially expressed immune-related genes (DEIRGs). The prognostic value of DEIRGs was analyzed using Cox regression and Kaplan–Meier analysis based on The Cancer Genome Atlas. The primary immune cells infiltrated in sorafenib-resistant HCC mice were explored using flow cytometry (FCM). Finally, small-molecule drugs for sorafenib-resistant HCC treatment were screened and validated by experiments. The CIBERSORT algorithm and mice model showed that macrophages and neutrophils are highly infiltrated, while CD8+ T cells are downregulated in sorafenib-resistant HCC. Totally, 34 DEIRGs were obtained from sorafenib-resistant and control groups, which were highly enriched in immune-associated biological processes and pathways. NR6A1, CXCL5, C3, and TGFB1 were further identified as prognostic markers for HCC patients. Finally, nalidixic acid was identified as a promising antagonist for sorafenib-resistant HCC treatment. Collectively, our study reveals the tumor immune microenvironment changes and explores a promising adjuvant drug to overcome sorafenib resistance in HCC.

Exploration of the relationship between hippocampus and immune system in schizophrenia based on immune infiltration analysis.

Immune dysfunction has been implicated in the pathogenesis of schizophrenia (SZ). Despite previous studies showing a broad link between immune dysregulation and the central nervous system of SZ, the exact relationship has not been completely elucidated. With immune infiltration analysis as an entry point, this study aimed to explore the relationship between schizophrenia and the immune system in more detail from brain regions, immune cells, genes, and pathways. Here, we comprehensively analyzed the hippocampus (HPC), prefrontal cortex (PFC), and striatum (STR) between SZ and control groups. Differentially expressed genes (DEGs) and functional enrichment analysis showed that three brain regions were closely related to the immune system. Compared with PFC and STR, there were 20 immune-related genes (IRGs) and 42 immune pathways in HPC. The results of immune infiltration analysis showed that the differential immune cells in HPC were effector memory T (Tem) cells. The correlation of immune-related DEGs (IDEGs) and immune cells further analysis showed that NPY, BLNK, OXTR, and FGF12, were moderately correlated with Tem cells. Functional pathway analysis indicated that these four genes might affect Tem by regulating the PI3K-AKT pathway and the neuroactive ligand-receptor interaction pathway. The receiver operating characteristic curve (ROC) analysis results indicated that these four genes had a high diagnostic ability (AUC=95.19%). Finally, the disease animal model was successfully replicated, and further validation was conducted using the real-time PCR and the western blot. These results showed that these gene expression changes were consistent with our previous expression profiling. In conclusion, our findings suggested that HPC in SZ may be more closely related to immune disorders and modulate immune function through Tem, PI3K-Akt pathway, and neuroactive ligand-binding receptor interactions. To the best of our knowledge, the Immucell AI tool has been applied for the first time to analyze immune infiltration in SZ, contributing to a better understanding of the role of immune dysfunction in SZ from a new perspective.

Screening of sphingolipid metabolism-related genes associated with immune cells in myocardial infarction: a bioinformatics analysis.

BackgroundInflammation and immune cell infiltration in infarcted myocardial tissue are critical to myocardial infarction (MI) prognosis, and alterations in sphingolipid metabolism (SM) have been shown to potentially influence the inflammatory response and induce cardioprotection, but the underlying mechanisms are unclear. We therefore performed bioinformatics analysis to screen for key genes of SM in MI immune cells.MethodsThree matrix files including GSE61145, GSE23294, and GSE71906 were downloaded from the Gene Expression Omnibus (GEO) database. GSE61145 was a human peripheral blood database, and GSE23294 and GSE71906 were 2 mouse myocardial tissue databases. R and annotation packages were used to screen for differentially-expressed genes (DEGs). Datasets of human and mouse cardiac tissues were downloaded from the GEO database for subsequent validation. The downloaded platform and matrix files were processed using R language and annotation packages. Key targets and enrichment pathways were identified using Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The Wilcoxon test was performed on the genes involved in SM pathways in neutrophils.ResultsA total of 261 DEGs were obtained from human peripheral blood datasets, among which 101 were immune-related. GO analysis revealed that neutrophil activation, T cell activation, and T cell differentiation were significantly enriched in the immune-related DEGs. Three types of immune cells were identified in infarcted myocardial tissues. In addition, 194 DEGs were obtained from mouse myocardial tissue data, among which 6 SM-related genes (Asah1, Degs1, Neu1, Sptlc2, Sphk1, and Gba2) were significantly associated with MI. Evaluation of the relationships between these DEGs and neutrophils showed that the expression of the Sptlc2 gene was significantly upregulated in neutrophils of the MI group, while the expression levels of the Asah1 and Degs1 genes were downregulated.ConclusionsWe identified 3 SM-related genes that were highly associated with neutrophils in MI, which may advance our understanding of SM in immune cells after MI.

Identification and characterization of four immune-related signatures in keloid.

A keloid is a fibroproliferative disorder of unknown etiopathogenesis that requires ill-defined treatment. Existing evidence indicates that the immune system plays an important role in the occurrence and development of keloid. However, there is still a lack of research on the immune-related signatures of keloid. Here we identified immune-related signatures in keloid and explored their pathological mechanisms. Transcriptomic datasets (GSE7890, GSE92566, and GSE44270) of keloid and normal skin tissues were obtained from the Gene Expression Omnibus database. The overlap of differentially expressed genes and immune-related genes was considered as differentially expressed immune-related genes (DEIGs). Functional analysis, expression, and distribution were applied to explore the function and characteristics of DEIGs, and the expression of these DEIGs in keloid and normal skin tissues was verified by immunohistochemistry. Finally, we conducted interactive network analysis and immune infiltration analysis to determine the therapeutic potential and immune correlation. We identified four DEIGs (LGR5, PTN, JAG1, and DKK1). In these datasets, only GSE7890 met the screening criteria. In the GSE7890 dataset, DKK1 and PTN were downregulated in keloid, whereas JAG1 and LGR5 were upregulated in keloid. In addition, we obtained the same conclusion through immunohistochemistry. Functional analysis indicated that these four DEIGs were mainly involved in stem cell, cell cycle, UV response, and therapy resistance. Through interactive network analysis, we found that these DEIGs were associated with drugs currently used to treat keloid, such as hydrocortisone, androstanolone, irinotecan, oxaliplatin, BHQ-880, and lecoleucovorin. Finally, many immune cells, including CD8+ T cells, resting memory CD4+ T cells, and M1 macrophages, were obtained by immune infiltration analysis. In conclusion, we identified four immune signaling molecules associated with keloid (LGR5, PTN, JAG1, and DKK1). These immune-related signaling molecules may be important modules in the pathogenesis of keloid. Additionally, we developed novel therapeutic targets for the treatment of this challenging disease.

Identification and validation of an immune signature associated with EMT and metabolic reprogramming for predicting prognosis and drug response in bladder cancer.

BackgroundEpithelial-mesenchymal transition (EMT), one leading reason of the dismal prognosis of bladder cancer (BLCA), is closely associated with tumor invasion and metastasis. We aimed to develop a novel immune−related gene signature based on different EMT and metabolic status to predict the prognosis of BLCA.MethodsGene expression and clinical data were obtained from TCGA and GEO databases. Patients were clustered based on EMT and metabolism scores calculated by ssGSEA. The immune-related differentially expressed genes (DEGs) between the two clusters with the most obvious differences were used to construct the signature by LASSO and Cox analysis. Time-dependent receiver operating characteristic (ROC) curves and Kaplan–Meier curves were utilized to evaluate the gene signature in training and validation cohorts. Finally, the function of the signature genes AHNAK and NFATC1 in BLCA cell lines were explored by cytological experiments.ResultsBased on the results of ssGSEA, TCGA patients were divided into three clusters, among which cluster 1 and cluster 3 had completely opposite EMT and metabolic status. Patients in cluster 3 had a significantly worse clinical prognosis than cluster 1. Immune-related DEGs were selected between the two clusters to construct the predictive signature based on 14 genes. High-risk patients had poorer prognosis, lower proportions of CD8+ T cells, higher EMT and carbohydrate metabolism, and less sensitivity to chemotherapy and immunotherapy. Overexpression of AHNAK or NFATC1 promoted the proliferation, migration and invasion of T24 and UMUC3 cells. Silencing ANHAK or NFATC1 could effectively inhibit EMT and metabolism in T24 and UMUC3 cells.ConclusionThe established immune signature may act as a promising model for generating accurate prognosis for patients and predicting their EMT and metabolic status, thus guiding the treatment of BLCA patients.

Differential Expression and Prognostic Correlation of Immune Related Factors Between Right and Left Side Colorectal Cancer.

BackgroundGrowing evidence suggests that colorectal cancer (CRC) should be considered a heterogeneous disease. The right side (RCC) and left side (LCC) colorectal cancer have different clinical characteristics and immune landscapes. The aim of this study was to analyze differential expression and prognostic correlation of immune-related factors between RCC and LCC.MethodsThe gene expression profile and clinical characteristics of CRC patients were retrieved from The Cancer Genome Atlas data portal (n=525). Using a deconvolution algorithm, immune cell infiltration in RCC and LCC based on the RNA-seq data was analyzed. Differentially expressed genes (DEGs) were obtained by performing differential gene expression analysis. Immune-related DEGs were derived by the intersection with immune-related factors downloaded from the IMMPORT database. To further validate the findings, we applied immunohistochemical (IHC) staining of a CRC tissue microarray (TMA). The distribution of immune cells in RCC and LCC and changes in the expression of immune molecules on their membranes were verified. The expression levels of circulating cytokines were measured by flow cytometry to detect the cytokines secreted by immune cells in RCC and LCC. Furthermore, to reveal the prognostic value of differential immune factors on RCC and LCC patients, survival analysis based on mRNA levels using TCGA cohort and survival analysis using protein levels was performed using our CRC patients.ResultsThe infiltration of immune cells differed between RCC and LCC, the infiltration degree of macrophages M0 was significantly higher in LCC, while the infiltration degree of differentiated macrophages M1 and M2, CD4+ T and CD8+ T cells was significantly higher in RCC. The expression of related molecules by immune cells also differed between RCC and LCC. The expression of 7 genes in RCC was higher than that in LCC, which were CCR5, CD209, CD8A, HCK, HLA-DPB1, HLA-DQA1, HLA-DRA, respectively. Meanwhile, the expression of 2 genes in LCC was higher than in RCC, which were IL-34 and PROCR. Patients with RCC having high expression of HLA-DQA1 mRNA or proteins had better survival and LCC patients with high expression of IL 34 mRNA or protein had better survival.ConclusionsIn this study, we comprehensively compared differences in immune cells and regulating factors between left and right colorectal cancer. Different expression patterns and their effects on survival were identified. The analysis of immune-related factors may provide a theoretical basis for precise immunotherapy of RCC and LCC.

Prediction of Prognosis in Patients with Hepatocellular Carcinoma Based on Molecular Subtypes of Immune Genes.

For those patients with hepatocellular carcinoma (HCC), it is really a heavy burden. Herein, the immune genes of HCC were analyzed in groups to determine prognostic biomarkers related to immune genes in HCC. The mRNA data, clinical data in TCGA-LIHC dataset, and immune gene in the ImmPort database were collected for the combining usage with K-means concordance clustering to cluster HCC patients according to the immune gene matrix. Based on ssGSEA analysis result, HCC patients were sorted into high- and low-immune subtypes, and survival curve presented that patients in high-immune subtypes had a better prognosis. Subsequently, differential expression analysis was performed to obtain immune-related differentially expressed genes (IRGs). Cox and lasso analyses were performed for obtaining five optimal immune genes related to prognosis, and a risk assessment model was then established. Patient samples in the training and validation sets were, respectively, divided into high- and low-risk groups. K-M survival curves presented a better prognosis of patients in the low-risk group than in the high-risk group. The ROC curve indicated that this model was finely used for the prediction of prognosis. In addition, immune infiltration assessment revealed that NR0B1 and FGF9 had potential to impact the tumor immune microenvironment. Finally, using qRT-PCR and transwell assays, it was demonstrated that the macrophage chemotaxis was enhanced when NR0B1 and FGF9 were highly expressed in HCC cells. In general, a 5-gene prognostic risk assessment model was constructed based on immune genes and bioinformatics analysis methods, which provides some reference for the prognosis of HCC as well as immunotherapy.

The Function, Role and Process of DDX58 in Heart Failure and Human Cancers.

BackgroundHeart failure (HF) is the most common outcome of cardiovascular disease, and an increasing number of patients with heart failure die from noncardiac causes, such as cancer. Epidemiological data suggest that ischemic cardiomyopathy–induced HF (ischemic HF) may be associated with an increased incidence of cancer. This study aimed to investigate the possible mechanisms of the association between ischemic HF and cancer, as well as potential therapeutic targets.MethodsWeighted gene co-expression network analysis was performed to analyze the correlations between phenotypes and gene modules using immune cells as phenotypes. Differential analysis was then performed to screen differentially expressed genes (DEGs) in ischemic HF and normal control samples. The macrophage-related Brown module was identified as the key module, and immune-related DEGs were obtained by taking the intersection of the Brown module, DEGs, and immune-related genes using a Venn diagram. DDX58 was identified as the key gene using a protein–protein interaction network and expression analyses and validated using immunohistochemistry. Kaplan–Meier survival analysis was performed to analyze the correlation between DDX58 expression and tumor prognosis. Spearman correlation analysis was performed to assess the correlation between DDX58 expression and immune cell infiltration.ResultsDDX58 was identified as a key immune-related gene associated with ischemic HF and was highly expressed in most cancer types. The survival analysis revealed a significant negative correlation between high DDX58 expression and prognosis in multiple tumor types. Moreover, DDX58 expression was significantly associated with immune cell infiltration and immune checkpoint gene expression in many cancer types.ConclusionDDX58 is a key immune-related gene in ischemic HF and may play a crucial role in the relationship between ischemic HF and cancer. Pan-cancer analysis suggests that DDX58 is a promising clinical prognostic marker for most cancers and may be a therapeutic target for cancer patients and ischemic HF patients at an increased risk of cancer.

Identification of Immune-Related Gene Signature in Stanford Type A Aortic Dissection.

Background: Stanford type A aortic dissection (ATAAD) is a common life-threatening event in the aorta. Recently, immune disorder has been linked to the risk factors that cause ATAAD at the molecular level. However, the specific immune-related gene signature during the progression is unclear. Methods: The GSE52093 and GSE98770 datasets related to ATAAD from the Gene Expression Omnibus (GEO) database were acquired. The immune gene expression levels were analyzed by single sample gene set enrichment analysis (ssGSEA). The correlations between gene networks and immune scores were determined by weighted gene correlation network analysis (WGCNA). The different immune subgroups were finally divided by consensus clustering. The differentially expressed genes (DEGs) were identified and subsequent functional enrichment analyses were conducted. The hub genes were identified by protein–protein interaction (PPI) network and functional similarities analyses. The immune cell infiltration proportion was determined by the CIBERSORT algorithm. Results: According to the ssGSEA results, the 13 ATAAD samples from the GEO database were divided into high- and low-immune subgroups according to the ssGSEA, WGCNA, and consensus clustering analysis results. Sixty-eight immune-related DEGs (IRDEGs) between the two subgroups were enriched in inflammatory-immune response biological processes, including leukocyte cell–cell adhesion, mononuclear cell migration, and myeloid leukocyte migration. Among these IRDEGs, 8 genes (CXCR4, LYN, CCL19, CCL3L3, SELL, F11R, DPP4, and VAV3) were identified as hub genes that represented immune-related signatures in ATAAD after the PPI and functional similarities analyses. The proportions of infiltrating CD8 T cells and M1 macrophages were significantly higher in ATAAD patients in the immune-high group than the immune-low group. Conclusion: Eight immune-related genes were identified as hub genes representing potential biomarkers and therapeutic targets linked to the immune response in ATAAD patients.

A DEIRGs-based immune score model for predicting prognosis and immunotherapy effects in breast cancer.

e13558 Background: Immune-based strategies have reignited prospect for the treatment of breast cancer(BRCA).Nevertheless, due to the molecular and genetic heterogeneity of tumors, a single biomarker can no longer meet the clinical needs in the highly complex tumor microenvironment(TMB).The main purpose of this study was to explore the immunobiological markers of BRCA prognosis in help to guide patient management and establish personalized risk assessment. Methods: Univariate/multivariable Cox proportional hazards regression and least absolute shrinkage and selection operator (LASSO) Cox analysis was used to establish a DEIRGs-based model in BRCA.GSEA analysis was used to explore biological signaling pathways. The immune infiltration landscape of BRCA was conducted via CIBERSORT algorithms and immunotherapy response was calculated through TIDE algorithm and TMB analysis. Results: In this study, 1178 BRCA samples from The Cancer Genome Atlas (TCGA) were used as the training set, and 140 samples from Gene Expression Omnibus (GEO) database were used as the validation set. Immune-related genes were collected from ImmPort and Innatedb database. A total of 547 differentially expressed immune related genes(DEIRGs) were identified. Enrichment analysis of the DEIRGs revealed that they were involved in the regulation of the P13K-Akt signaling pathway and cytokine–cytokine receptor interaction. To identify the genes most relevant to BRCA, we performed a WGCNA analysis to select the modules with the strongest correlation between the module traits. A signature based on six DEIRGs(CETP, DES, IL33, TXNIP, RNF125, STAT5B) was constructed. These cases were stratified into high- and low-risk groups according to the median risk score. Kaplan–Meier analysis showed that OS in the high-risk group was significantly shorter than that in the low-risk group (P&lt;0.001).The model presented unique characteristics in terms of immune cell infiltration and immune function in TME. The results from the tumor mutation burden(TMB) and tumor immune dysfunction and exclusion(TIDE) analysis revealed that the model could efficiently predict the potential response of immunotherapy. ROC curves were primarily used to assess the predictive power of the prognostic model, with AUC values of 0.736, 0.816 and 0.836 at 10, 15 and 20 years, respectively. More importantly, we researched the relationship between this model and TMB and TIDE score, which confirmed that this signature gave the best prognostic value. Conclusions: We have developed a novel immune score model, which can effectively and efficiently predict the prognosis of BRCA patients as well as the effect of immunotherapy. The high-risk subtype was featured by higher TIDE score and lower TMB. Our findings suggest that immunotherapy may be efficacious for low-risk groups of BRCA patients. The findings can be served as reference for the further research and validation of biomarkers.

Development of a Novel Immune-Related Gene Signature to Predict Prognosis and Immunotherapeutic Efficiency in Gastric Cancer.

Background: Gastric cancer (GC) is the fifth most common malignancy and the third leading cause of tumor-related deaths globally. Herein, we attempted to build a novel immune-related gene (IRG) signature that could predict the prognosis and immunotherapeutic efficiency for GC patients. Methods: The mRNA transcription data and corresponding clinical data of GC were downloaded from The Cancer Genome Atlas (TCGA) database as the training group and the GSE84437 data set as the testing cohort, followed by acquisition of IRGs from the InnateDB resource and ImmPort database. Using the univariate Cox regression analysis, an IRG signature was developed. Several immunogenomic analyses were performed to illustrate the associations between the immune risk score and tumor mutational burden, immune cell infiltrations, function of immune infiltration, clinical characteristics, immune subtype, and immunotherapeutic response. Results: The analysis of 343 GC samples and 30 normal samples from the TCGA database gave rise to 8,713 differentially expressed genes (DEGs) and 513 differentially expressed immune-related genes (DEIRGs) were extracted. The novel IRG signature contained eight DEIRGs (FABP4, PI15, RNASE2, CGB5, INHBE, RLN2, DUSP1, and CD36) and was found to serve as an independent predictive and prognostic factor for GC. Then, the GC patients were separated into the high- and low-risk groups based on the median risk score, wherein the low-risk group presented a better prognosis and was more sensitive to immunotherapy than did the high-risk group. According to the time-dependent ROC curves and AUCs, the immunotherapeutic value of the signature was better than the Tumor Immune Dysfunction and Exclusion (TIDE) and T-cell inflammatory signature (TIS) scores. In addition, the AUCs of the risk score for predicting 1-, 2-, and 3-year OS were 0.675, 0.682, and 0.710, respectively, which indicated that the signature had great predictive power. Conclusion: This study presents a novel IRG signature based on the tumor immune microenvironment, which could improve the prediction of the prognosis and immunotherapeutic efficiency for GC patients. The powerful signature may serve as novel biomarkers and provide therapeutic targets for precision oncology in clinical practice.