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(1) Background: Infection with human cytomegalovirus (HCMV) leads to the production and release of subviral particles, termed Dense Bodies (DB). They are enclosed by a membrane resembling the viral envelope. This membrane mediates the entrance of DBs into cells in a way that is comparable to virus infection. HCMV attachment and entry trigger the induction of interferon synthesis and secretion, and the subsequent expression of interferon-regulated genes (IRGs) that might inhibit replication of the virus. Recently, we demonstrated that DBs induce a robust interferon response in the absence of infection. Little is known thus far, including how DBs influence HCMV infection and virus–host interaction. (2) Methods: Purified DBs were used to study the impact on virus replication and on the innate defense mechanisms of the cell. (3) Results: The incubation of cells with DBs at the time of infection had little effect on viral genome replication. Preincubation of DBs, however, led to a marked reduction in viral release from infected cells. These cells showed an enhancement of the cytopathic effect, associated with a moderate increase in early apoptosis. Despite virus-induced mechanisms to limit the interferon response, the induction of interferon-regulated genes (IRGs) was upregulated by DB treatment. (4) Conclusions: DBs sensitize cells against viral infection, comparable to the effects of interferons. The activities of these particles need to be considered when studying viral–host interaction.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death. HBV infection is an important risk factor for the tumorigenesis of HCC, given that the inflammatory environment is closely related to morbidity and prognosis. Consequently, it is of urgent importance to explore the immunogenomic landscape to supplement the prognosis of HCC. The expression profiles of immune‐related genes (IRGs) were integrated with 377 HCC patients to generate differentially expressed IRGs based on the Cancer Genome Atlas (TCGA) dataset. These IRGs were evaluated and assessed in terms of their diagnostic and prognostic values. A total of 32 differentially expressed immune‐related genes resulted as significantly correlated with the overall survival of HCC patients. The Gene Ontology functional enrichment analysis revealed that these genes were actively involved in cytokine‐cytokine receptor interaction. A prognostic signature based on IRGs (HSPA4, PSME3, PSMD14, FABP6, ISG20L2, TRAF3, NDRG1, NRAS, CSPG5, and IL17D) stratified patients into high-risk versus low-risk groups in terms of overall survival and remained as an independent prognostic factor in multivariate analyses after adjusting for clinical and pathologic factors. Several IRGs (HSPA4, PSME3, PSMD14, FABP6, ISG20L2, TRAF3, NDRG1, NRAS, CSPG5, and IL17D) of clinical significance were screened in the present study, revealing that the proposed clinical-immune signature is a promising risk score for predicting the prognosis of HCC.

Ting Ye,1,* Jia Feng,1,* Meng Cui,2 Jia Yang,2 Xue Wan,1 Dan Xie,1 Jinbo Liu1 1Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, People’s Republic of China; 2Department of Laboratory Medicine, The Leshan People’s Hospital, Luzhou, Sichuan, 614000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ting Ye; Jinbo LiuDepartment of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, No. 25, Taiping Street, Jiangyang District, Luzhou, Sichuan, 646000, People’s Republic of ChinaTel +86-830-3165730Email yeting1103@163.com; liulab2019@163.comBackground: Myocardial infarction associated transcript (MIAT) is identified as a long chain non-coding RNA (lncRNA), which was associated with myocardial infarction susceptibility. While intense efforts have been made to elucidate the relationship between MIAT and carcinogenesis, the tumor immunoreaction of MIAT remains elusive. Thus, this study aimed to investigate the role of MIAT in the immunoregulation of breast cancer (BC) and further explore the better clinical significance.Methods: The differential expression of MIAT between BC and normal/adjacent tissues was compared using Wilcoxon rank sum test. The diagnostic and prognostic values of elevated MIAT expression in BC tissues were unveiled via receiver operating characteristic (ROC) analysis and KM-plotter analysis. Limma and edgeR package were used to identify differentially expressed genes (DEGs) and microRNAs (DEMs) from TCGA database respectively. A co-expression dataset was constructed to comprehensively understand the relationship between MIAT and DEGs based on the Pearson correlation coefficient. Furthermore, GO and KEGG analyses were conducted to predict the potential functions of MIAT. We next intersected immune-related genes (IRGs) from ImmPort database with MIAT-co-expressed genes to obtain MIAT-co-expressed IRGs, in order to construct MIAT-microRNA (miRNA)-mRNA network. And the correlation between MIAT and tumor-infiltrating immune cells (TICs) and immunophenoscore (IPS) analysis was analyzed by TIMER and CIBERSORT. Finally, the reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) was used to detect the expression profiles of MIAT in serum samples.Results: The expression levels of MIAT were notably higher in BC than in normal or adjacent tissues. And MIAT expression could be used as a prognostic indicator of mortality risk in patients with BC in different aspects. Moreover, the enrichment analyses suggested that MIAT was strongly involved in BC immune response. In addition, TIMER database and CIBERSORT analyses indicated that MIAT was significantly correlated with 13 types of TICs (B cells, dendritic cells, neutrophils, CD8 T cells, CD4 memory resting T cells, CD4 memory activated T cells, gamma delta T cells, M1 macrophages, plasma cells, activated NK cells, monocytes, M2 macrophages, activated mast cells). Simultaneously, the IPS analysis implied that the higher the MIAT expression, the better the immunotherapy effect. The ROC curve analysis showed that the area under the curve (AUC) value of MIAT was 0.86 (sensitivity = 87.80%, specificity = 75.61%). And the high MIAT expression in serum was positive related to TNM stage (P = 0.032) and lymph node metastasis (P = 0.028).Conclusion: MIAT may be a valuable noninvasive diagnostic biomarker for BC and is associated with tumor-infiltrating immune cells in tumor microenvironment, suggesting MIAT as a potential target for future treatment of BC.Keywords: lncRNA MIAT, BC, biomarker, immune regulation, tumor-infiltrating, immune cells

Alteration in the polybromo‐1 (PBRM1) protein encoding gene PBRM1 is the second most frequent mutation in clear cell renal cell carcinoma (ccRCC). It causes a series of changes in the tumorigenesis, progression, prognosis, and immune response of ccRCC patients. This study explored the PBRM1‐associated immunological features and identified the immune‐related genes (IRGs) linked to PBRM1 mutation using bioinformatics methods. A total of 37 survival IRGs associated with PBRM1 mutation in ccRCC patients were identified. To further explore the role of these IRGs in ccRCC and their association with immune status, eight IRGs with remarkable potential as individual targets were selected. An immune model that was constructed showed good performance in stratifying patients into different subgroups, showing clinical application potential compared to traditional clinical factors. Patients in the high‐risk group were inclined to have more advanced stage and higher grade tumors with node metastasis, distant metastasis, and poorer prognosis. Furthermore, these patients had high percentages of regulatory T cells, follicular helper T cells, and M0 macrophages and exhibited high expression levels of immune checkpoints proteins, such as CTLA‐4, PD‐1, LAG‐3, TIGIT, and CD47. Finally, a nomogram integrating the model and clinical factors for clinical application showed a more robust predictive performance for prognosis. The prediction model associated with PBRM1 mutation status and immunity can serve as a promising tool to stratify patients depending upon their immune status, thus facilitating immunotherapy in the future., In this study, we used bioinformatic methods to explore the effect of PBRM1 mutation in clear cell renal cell carcinoma samples on the immune status and identify immune‐related biomarkers related to PBRM1 mutation. As far as we know, this is the first report of a prediction model incorporating PBRM1 status and it serves as a promising tool to determine the immune status and stratify patients to judge the effectiveness of immunotherapy.

Immunity plays an important role in tumor development. In this study, we aimed to investigate molecular classification and its prognostic value in hepatocellular carcinoma (HCC) based on immune signature. Gene set enrichment analysis (GSEA) was used to calculate scores of immune pathways for HCC and hierarchical clustering in two databases (The Cancer Genome Atlas [TCGA], Liver Cancer-RIKEN, JP [LIRI_JP]). The scores of the immune microenvironment and the proportions of 22 immune cells were also calculated. Single-sample GSEA (ssGSEA) was used to screen survival prognosis-related immune pathways and calculate the hazard radio of differentially expressed immune-related genes (IRGs), which were validated in clinical samples and multiple datasets. Based on the immune characteristics, we identified three HCC subtypes, namely immunity high (Immunity_H), immunity medium (Immunity_M), and immunity low (Immunity_L), and confirmed that the classification was reliable and predictable. Immunity_H with a higher immune and stromal score indicated better survival rate. Cox regression analysis showed that IL18RAP and IL7R were the protective genes. Immune risk score was the independent risk factor of overall survival in HCC patients. These results indicated that immunogenomic classification could distinguish HCC patients with different immune status, which could impact the prognosis of the patients with HCC., Graphical abstract, Weiwei et al. provided an immunogenomic classification based on immune characteristics to distinguish hepatocellular carcinoma (HCC) patients with different immune status, and the prediction model can be used for individual immunotherapy of HCC patients.

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients’ overall survival (OS). The turquoise module ( cor = 0.67 ; P < 0.001 ) and its genes ( n = 1092 ) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome ( P < 0.0001 ). Also, this IRRS model was found to be an independent prognostic indicator of gliomas’ survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

Thyroid carcinoma (THCA) is the most common endocrine malignancy, and its incidence is increasing worldwide. Several studies have explored whether the tumor immune microenvironment and immune-related genes (IRGs) influence the prognosis of patients with THCA and can be used to predict the response to immune checkpoint inhibitors (ICIs). We developed an IRG prognostic/risk signature using a bioinformatics method, and its predictive capacity was validated in patients in the test set and the total set. Subsequently, we analyzed the correlation between this IRG prognostic signature and tumor-infiltrating immune cells, tumor mutation burden (TMB), and immune checkpoint protein expression in patients with THCA. With a multivariate analysis, the IRG prognostic signature, which comprised eight IRGs, was identified as an independent prognostic factor. High-risk patients had poor overall survival compared with low-risk patients. Plasma cells, monocytes, and dendritic cells infiltrated differently according to the IRG prognostic signature. The low-risk group had a higher TMB and immunophenoscore (IPS), which indicated a better response to ICIs. The qRT-PCR validated eight IRGs with differential expression in thyroid cancer and normal tissues. We conclude that the IRG prognostic signature may be a useful tool to predict survival and response to ICIs. However, further testing is required to assess the predictive capacity of this IRG prognostic signature.

Background Emerging evidence indicates that immune infiltrating cells in tumor microenvironment (TME) correlates with the development and progression of gastric cancer (GC). This study aimed to systematically investigate the immune‐related genes (IRGs) to develop a prognostic signature to predict the overall survival (OS) in GC. Method The gene expression profiles of training dataset (GSE62254), validation dataset I (GSE15459), and validation dataset II (GSE84437) were retrieved from GEO and TCGA databases. In the present study, we developed a 10 IRGs prognostic signature with the combination of weighted gene co‐expression network analysis (WGCNA) and least absolute shrinkage and selection operator method (LASSO) COX model. Results In the training dataset, the accuracy of the signature was 0.681, 0.741, and 0.72 in predicting 1, 3, and 5‐year OS separately. The signature also had good performance in validation dataset Ⅰ with the accuracy of 0.57, 0.619, and 0.694, and in validation dataset Ⅱ with the accuracy of 0.559, 0.624, and 0.585. Then, we constructed a nomogram using the signature and clinical information which had strong discrimination ability with the c‐index of 0.756. In the immune infiltration analysis, the signature was correlated with multiple immune infiltrating cells such as CD8 T cells, CD4 memory T cells, NK cells, and macrophages. Furthermore, several significant pathways were enriched in gene set enrichment analysis (GSEA) analysis, including TGF‐beta signaling pathway and Wnt signaling pathway. Conclusion The signature of 10 IRGs we identified can effectively predict the prognosis of GC and provides new insight into discovering candidate prognostic biomarkers of GC., We identified a 10 immune‐related genes signature as a prognostic biomarker panel for gastric cancer. The signature was correlated with multiple immune infiltrating cells and several significant pathways.

A substantial proportion of prostatic adenocarcinoma (PRAD) patients experience biochemical failure (BCF) after radical prostatectomy (RP). The immune microenvironment plays a vital role in carcinogenesis and the development of PRAD. This study aimed to identify a novel immune‐related gene (IRG)‐based signature for risk stratification and prognosis of BCF in PRAD. Weighted gene coexpression network analysis was carried out to identify a BCF‐related module in a discovery cohort of patients who underwent RP at the Massachusetts General Hospital. The median follow‐up time was 70.32 months. Random forest and multivariate stepwise Cox regression analyses were used to identify an IRG‐based signature from the specific module. Risk plot analyses, Kaplan‐Meier curves, receiver operating characteristic curves, univariate and multivariate Cox regression analyses, stratified analysis, and Harrell’s concordance index were used to assess the prognostic value and predictive accuracy of the IRG‐based signature in the internal discovery cohort; The Cancer Genome Atlas database was used as a validation cohort. Tumor immune estimation resource database analysis and CIBERSORT algorithm were used to assess the immunophenotype of PRAD. A novel IRG‐based signature was identified from the specific module. Five IRGs (BUB1B, NDN, NID1, COL4A6, and FLRT2) were verified as components of the risk signature. The IRG‐based signature showed good prognostic value and predictive accuracy in both the discovery and validation cohorts. Infiltrations of various immune cells were significantly different between low‐risk and high‐risk groups in PRAD. We identified a novel IRG‐based signature that could function as an index for assessing tumor immune status and risk stratification in PRAD., This study aimed to identify a novel immune‐related gene (IRG)‐based signature for risk stratification and prognosis of biochemical failure in prostatic adenocarcinoma (PRAD). We identified an IRG‐based signature using weighted gene coexpression network analysis and random forest and multivariable stepwise Cox regression analyses and assessed the prognostic value and predictive accuracy of the IRG‐based signature in both internal and external cohorts. This novel IRG‐based signature could function as an index for assessing tumor immune status and risk stratification in PRAD. In addition, we analyzed the immune microenvironment of PRAD and identified immune cell changes associated with high‐risk PRAD.

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: {"type":"entrez-geo","attrs":{"text":"GSE13507","term_id":"13507"}}GSE13507 (n = 165) and {"type":"entrez-geo","attrs":{"text":"GSE32894","term_id":"32894"}}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.

Background. Diffuse large B cell lymphoma (DLBCL) is a life-threatening malignant tumor characterized by heterogeneous clinical, phenotypic, and molecular manifestations. Given the association between immunity and tumors, identifying a suitable immune biomarker could improve DLBCL diagnosis. Methods. We systematically searched for DLBCL gene expression microarray datasets from the GEO database. Immune-related genes (IRGs) were obtained from the ImmPort database, and 318 transcription factor (TF) targets in cancer were retrieved from the Cistrome Cancer database. An immune-related classifier for DLBCL prognosis was constructed using Cox regression and LASSO analysis. To assess differences in overall survival between the low- and high-risk groups, we analyzed the tumor microenvironment (TME) and immune infiltration in DLBCL using the ESTIMATE and CIBERSORT algorithms. WGCNA was applied to study the molecular mechanisms explaining the clinical significance of our immune-related classifier and TFs. Results. Eighteen IRGs were selected to construct the classifier. The multi-IRG classifier showed powerful predictive ability. Patients with a high-risk score had poor survival. Based on the AUC for three- and five-year survival, the classifier exhibited better predictive power than clinical data. Discrepancies in overall survival between the low- and high-risk score groups might be explained by differences in immune infiltration, TME, and transcriptional regulation. Conclusions. Our study describes a novel prognostic IRG classifier with strong predictive power in DLBCL. Our findings provide valuable guidance for further analysis of DLBCL pathogenesis and clinical treatment.

Background Colorectal cancer (CRC) is the leading cause of cancer deaths and most common malignant tumors worldwide. Immune-related genes (IRGs) can predict prognoses of patients and the effects of immunotherapy. A series of colon cancer (CCa) samples from The Cancer Genome Atlas (TCGA) were analyzed to provide a new perspective into this field. Methods Differential IRGs and IRGs with significant clinical outcomes (sIRGs) were calculated by the limma algorithm and univariate COX regression analysis. The potential molecular mechanisms of IRGs were detected by PPI, KEGG and GO analysis. Immune-related risk score model (IRRSM) was established based on multivariate COX regression analysis. Based on the median risk score of IRRSM, the high-risk group and low-risk group were distinguished. The expression levels of IHNBA and JAG2 and relationships between IHNBA and clinical features were verified by RT-qPCR. Results 6 differential sIRGs of patients with CCa were selected by univariate COX regression analysis. Based on the sIRGs (INHBA, JAG2 and CCL19), the IRRSM was established to predict survival probability of CCa patients and to explore the potential correlations with clinical features. Furthermore, IRRSM reflected the infiltration status of 22 types of immune cells. The expression levels of IHNBA and JAG2 were higher in CCa tissues than that in adjacent normal tissues. The expression levels of IHNBA and JAG2 were increased in advanced T stages. Conclusion Our results illustrated that some sIRGs showed the latent value of predicting the prognoses of CCa patients and the clinical features. This study could provide a new insight for immune research and treatment strategies in CCa patients.

Thyroid‐associated ophthalmopathy (TAO), the most common and severe manifestation of Graves' disease (GD), is a disfiguring and potentially blinding autoimmune disease. The high relapse rate (up to 20%) and substantial side effects of glucocorticoid treatment further decrease the life quality of TAO patients. To develop novel therapies, we amid to explore the immunopathogenesis of TAO. To identify the key immune‐related genes (IRGs) in TAO, we integrated the IRG expression profiles in thyrocytes from a GD patient set (GD vs healthy control) and a TAO patient set (TAO vs GD). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein‐protein interaction (PPI) and receiver operating characteristic (ROC) curve analyses identified the leptin receptor (LEPR) gene as the key IRG in TAO immunopathogenesis. Gene set enrichment analysis (GSEA) suggested enrichment of the antigen presentation pathway in TAO patients with higher LEPR. Increased LEPR expression was validated in TAO orbital tissues, and weighted gene co‐expression network analysis (WGCNA) showed that cell adhesion processes were positively correlated with LEPR. Our study revealed that LEPR is a key gene in TAO immunopathogenesis and plays different roles in thyrocytes and orbital tissues. Our findings provide new insights into diagnostic and therapeutic biomarkers for TAO.

Hepatocellular carcinoma (HCC) is one of the main causes of cancer deaths globally. Immunotherapy is becoming increasingly important in the cure of advanced HCC. Thus it is essential to identify biomarkers for treatment response and prognosis prediction. We searched publicly available databases and retrieved 465 samples of genes from The Cancer Genome Atlas (TCGA) database and 115 tumor samples from Gene Expression Omnibus (GEO). Meanwhile, we used the ImmPort database to determine the immune-related genes as well. Weighted gene correlation network analysis, Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to identify the key immune related genes (IRGs) which are closely related to prognosis. Gene set enrichment analysis (GSEA) was implemented to explore the difference of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway between Immune high- and low-risk score groups. Finally, we made a prognostic nomogram including Immune-Risk score and other clinicopathologic factors. A total of 318 genes from prognosis related modules were identified through weighted gene co-expression network analysis (WGCNA). 46 genes were strongly linked to prognosis after univariate Cox analysis. We constructed a seven genes prognostic signature which showed powerful prediction ability in both training cohort and testing cohort. 16 significant KEGG pathways were identified between high- and low- risk score groups using GSEA analysis. This study identified and verified seven immune-related prognostic biomarkers for the patients with HCC, which have potential value for immune modulatory and therapeutic targets.

Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

Bladder cancer (BLCA) has a high incidence and recurrence rate, and the effect of immunotherapy varies from person to person. Immune-related genes (IRGs) have been shown to be associated with immunotherapy and prognosis in many other cancers, but their role in immunogenic BLCA is less well defined. In this study, we constructed an eight-IRG risk model, which demonstrated strong prognostic and immunotherapeutic predictive power. The signature was significantly related to tumor clinicopathological characteristics, tumor class, immune cell infiltration and mutation status. Additionally, a nomogram containing the risk score and other potential risk factors could effectively predict the long-term overall survival probability of BLCA patients. The enriched mechanisms identified by gene set enrichment analysis suggested that the reason why this signature can accurately distinguish high- and low-risk populations may be closely related to the different degrees of innate immune response and T cell activation in different patients.

Background. An increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated. Methods. Univariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254, n = 300 ; GSE15459, n = 191 ; and GSE26901, n = 109 ). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort ( n = 600 ) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253, n = 432 ; GSE84437, n = 431 ; and TCGA, n = 336 ). Immune cell infiltration (ICI) was quantified by the CIBERSORT method. Results. A risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort ( AUC > 0.7 ). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC ( p < 0.001 ). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group ( p < 0.001 ), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor ( p = 0.011 ). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score ( p = 0.00085 ). The patients’ risk score increased with the progression of the clinicopathological stage. Conclusion. In this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC.

The immune response is associated with the progression and prognosis of epithelial ovarian cancer (EOC). However, the roles of infiltrated immune cells and immune-related genes (IRGs) in EOC have not been reported comprehensively. In the current study, the differentially expressed genes (DEGs) were filtered based on the integrated gene expression data acquired from The University of California at Santa Cruz (UCSC) Genome Browser. Then, IRGs and transcriptional factors (TFs) were screened based on the ImmPort database and Cistrome database. A total of 501 differentially expressed IRGs, and 76 TFs were detected. A TF-mediated network was constructed by univariate Cox analysis to reveal the potential regulatory mechanisms of IRGs. Next, a nine immune-based prognostic risk model using nine IRGs (PI3, CXCL10, CXCL11, LCN6, CCL17, CCL25, MIF, CX3CR1, and CSPG5) was established. Based on the risk score worked out from the signature, the EOC patients could be classified into low-risk and high-risk groups. Furthermore, the immune landscapes, elevated by the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm and the Tumor Immune Estimation Resource (TIMER) database, effectuated different patterns in two groups. Thus, an immune-based prognostic risk model of EOC elucidates the immune status in the tumor microenvironment, and hence, could be used for prognosis.

Background: Bladder cancer (BLCA) is one of the most common urinary tract malignant tumors. It is associated with poor outcomes, and its etiology and pathogenesis are not fully understood. There is great hope for immunotherapy in treating many malignant tumors; therefore, it is worthwhile to explore the use of immunotherapy for BLCA.Methods: Gene expression profiles and clinical information were obtained from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were downloaded from the Immunology Database and Analysis Portal. Differentially-expressed and survival-associated IRGs in patients with BLCA were identified using computational algorithms and Cox regression analysis. We also performed functional enrichment analysis. Based on IRGs, we employed multivariate Cox analysis to develop a new prognostic index.Results: We identified 261 IRGs that were differentially expressed between BLCA tissue and adjacent tissue, 30 of which were significantly associated with the overall survival (all P

Background Immune cells account for a large proportion of the tumour microenvironment in anaplastic thyroid carcinomas (ATCs). However, the expression pattern of immune-related genes (IRGs) in ATCs is unclear. Our study aimed to identify an immune-related signature indicating the dedifferentiation of thyroid cells. Methods We compared the differences in thyroid differentiation score (TDS), infiltration of immune cells and enriched pathways between ATCs and papillary thyroid carcinomas (PTCs) or normal thyroid tissues in the Gene Expression Omnibus database. Univariate and multivariable Cox analyses were used to screen prognosis-associated IRGs in The Cancer Genome Atlas database. After constructing a risk score, we investigated its predictive value for differentiation and survival by applying receiver operating characteristic and Kaplan–Meier curves. We further explored its associations with important immune checkpoint molecules, infiltrating immune cells and response to immunotherapy. Results Compared with PTCs or normal thyroid tissues, ATCs exhibited lower TDS values and higher enrichment of immune cells and activation of the inflammatory response. The quantitative analyses and immunohistochemical staining validated that most ATC cell lines and ATC tissues had higher expression of MMP9 and lower expression of SDC2 than normal thyroid samples and PTC. Higher risk scores indicates dedifferentiation and a worse prognosis. Additionally, the risk score was positively correlated with the immune checkpoint molecules PDL1, CTLA4, IDO1, and HAVCR2 and infiltration of multiple immune cells. Importantly, we found that the samples with higher risk scores tended to have a better response to immunotherapy than those with lower scores. Conclusion Our findings indicate that the risk score may not only contribute to the determination of differentiation and prognosis of thyroid carcinomas but also help the prediction of immune cells infiltration and immunotherapy response.

Aim: To build a valid prognostic model based on immune-related genes for lung squamous cell carcinoma (LUSC). Materials & methods: Differential expression of immune-related genes between LUSC and normal specimens from TCGA dataset and underlying molecular mechanisms were systematically analyzed. Constructing and validating the high-risk and low-risk groups for LUSC survival. Results: The immune-related gene-based prognostic index (IRGPI) could predict the overall survival in patients with different clinicopathological characteristics. Functional enrichment analysis of differential expression of immune-related gene signature indicated distinctive molecular pathways between high-risk and low-risk groups. Conclusion: Analysis of IRGs in LUSC enable us to stratify patients into distinct risk groups, which may help to screen LUSC patients at risk and decision making on follow-up therapeutic intervention.

In this study, we used the ESTIMATE algorithm to analyze clinical data and transcriptome profiles of 1635 colorectal cancer (CRC) samples from the Gene Expression Omnibus and The Cancer Genome Atlas databases and identify prognostic immune-related genes (IRGs). We identified 941 differentially expressed (4 downregulated and 937 upregulated) genes by comparing samples with high and low immune, stromal scores and tumor purity. LASSO Cox regression analyses showed that the risk score based on a ten-IRG signature was an independent prognostic factor in CRC. The nomogram with pathological stages (TNM) and the ten-IRG signature showed a C-index of 0.769 (95% CI, 0.717-0.821), and area under ROC curve values of 0.788, 0.782 and 0.789 for 1-, 3-, and 5-year OS, respectively. TIMER database analysis showed positive correlation between the ten prognostic IRGs and the levels of tumor-infiltrated immune cells, including CD4+ and CD8+ T cells, macrophages, neutrophils, and dendritic cells. These findings demonstrate that this novel ten-IRG signature correlates with the pathological stages and the levels of multiple tumor-infiltrated immune cell types. This makes the ten-IRG signature a potential prognostic factor for CRC patients.

Guang-zhao Huang,1,* Qing-qing Wu,1,* Ze-nan Zheng,1,* Ting-ru Shao,1 Fei Li,1 Xin-yan Lu,1 Heng-yu Ye,1 Gao-xiang Chen,1 Yu-xing Song,1 Wei-sen Zeng,2 Yi-long Ai,3 Xiao-zhi Lv1 1Department of Oral & Maxillofacial Surgery, NanFang Hospital, Southern Medical University, Guangzhou, Guangdong Province, People’s Republic of China; 2Department of Cell Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, Guangdong Province, People’s Republic of China; 3Foshan Stomatological Hospital, School of Stomatology and Medicine, Foshan University, Foshan, Guangdong Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiao-zhi LvDepartment of Oral & Maxillofacial Surgery, NanFang Hospital, Southern Medical University, Baiyun District, Guangzhou, Guangdong Province, 510080, People’s Republic of ChinaTel +86 13724811658Email lxzsurgeon@126.comYi-long AiFoshan Stomatological Hospital, School of Stomatology and Medicine, Foshan University, Chancheng District, Foshan, Guangdong Province, 528000, People’s Republic of ChinaTel +86 13827755774Email aiyilong@126.comPurpose: Plenty of studies showed that the immune system was associated with cancer initiation and progression. This study aimed to explore the prognostic biomarkers from immune-related genes (IRGs) in oral squamous cell carcinoma (OSCC).Materials and Methods: RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) and IRGs and transcription factors (TFs) were extracted. Then, the co-expression network between IRGs and TFs was constructed using the “WGCNA” package in R software. Furthermore, a gene expression signature according to IRGs was constructed to predict OSCC prognosis and its accuracy was validated by survival analysis. Subsequently, correlation analyses between risk-score and immune cells level and clinical parameters were performed. Finally, immune-related biomarkers were selected and further investigated using gain-of-function assays in vitro.Results: A total of 32 normal cases and 317 OSCC cases were selected in our study. Differentially-expressed analysis indicated that there were 381 differentially-expressed IRGs and 62 TFs in OSCC. Among them, 25 TFs and 21 IRGs were enrolled in the co-expression network. Furthermore, we found that gene expression signature on the basis of 10 IRGs could predict the prognosis accurately and a high-risk score based on gene expression signature meant a high T classification, terminal clinical stage, and low immune cells level in OSCC. Finally, cathepsin G (CTSG) was identified as a potential immune-related biomarker and therapeutic target in OSCC.Conclusion: In conclusion, IRGs were directly involved in the development and progression of OSCC. Furthermore, CTSG was identified as a potential independent biomarker and might be an immunotherapeutic target in OSCC treatment.Keywords: oral squamous cell carcinoma, immune, biomarker, bioinformatics, CTSG

Increasingly attention is being given to immune molecules in pancreatic cancer. The purpose of this study was to understand the potential clinical application of immune-regulated genes (IRGs) in the stratification of prognosis and to facilitate the development of personalized prognostic information for pancreatic cancer patients. We systematically used public data to comprehensively analyze immune-regulated gene pair (IRGP) expression profiles and clinical data. In our study, IRGP signature was identified to predict the overall survival (OS) of pancreatic cancer patients. We suggested that immune genes are enriched in different risk groups. In the high-risk group, M1 macrophages and resting NK cells were significantly enriched, while the percentages of naïve B cells, resting dendritic cells, CD8 T cells and regulatory T cells (Tregs) were significantly higher in the low-risk group, and we verified these results with immunohistochemical experiments. Gene ontology (GO) analysis confirmed that the IRGP index (IRGPI) signature genes in the cohort were mostly party to sensory perception of a chemical stimulus and the adaptive immune response. The identification of these pathways provides a basis for studying the molecular mechanisms of IRGPI signaling to predict the prognosis of pancreatic cancer. Our study effectively constructed a robust IRGP signature with prognostic value for pancreatic cancer, presenting a conceivable method for deciding on a preoperative treatment., Graphical Abstract

Graphical abstract, Immunotherapy has become a promising therapeutic option for Head and neck squamous cell carcinoma (HNSC). However, only a small percentage of patients could benefit from it, and the overall prognosis was far from satisfactory. In this study, by comprehensively computational analyses of hundreds of HNSC samples, a prognostic signature composed of 13 immune-related genes (IRGs) was constructed. The results of the analyses in multiple datasets indicated that our signature had high predictive accuracy and could serve as an independent prognostic predictor. Based on this signature and multiple clinical variables, we also established a prognostic nomogram to quantitatively predict the survival risk of individual patients. Moreover, this signature could accurately predict survival, reflect the immune microenvironment, and predict immunotherapy efficacy among HNSC patients. Two potential drugs (doxorubicin and daunorubicin) were also identified via Connectivity Map and molecular docking, which could be used for HNSC combination therapy. Taken together, we developed and validated a robust IRG-based prognostic signature to monitor the prognosis of HNSC, which could provide a solid foundation for individualized cancer immunotherapy.

Background: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy worldwide, and the prognosis of HNSCC remains bleak. Numerous studies revealed that the tumor mutation burden (TMB) could predict the survival outcomes of a variety of tumors. Objectives: This study aimed to investigate the TMB and immune cell infiltration in these patients and construct an immune-related genes (IRGs) prognostic model. Methods: The expression data of 546 HNSCC patients were obtained from The Cancer Genome Atlas (TCGA) database. All patients were divided into high- and low- TMB groups, and the relationship between TMB and clinical relevance was further analyzed. The differentially expressed genes (DEGs) were identified using the R software package, limma. Functional enrichment analyses were conducted to identify the significantly enriched pathways between two groups. CIBERSORT algorithm was adopted to calculate the abundance of 22 leukocyte subtypes. The IRGs prognostic model was constructed via the multivariate Cox regression analysis. Results: Missense mutation and single nucleotide variants (SNV) were the most predominant mutation types in HNSCC. TP53, TTN, and FAT1 were the most frequently mutated genes. Patients with high TMB were observed with worse survival outcomes. The functional analysis of TMB associated DEGs showed that the identified DEGs mainly involved in spliceosome, RNA degradation, proteasome, and RNA polymerase pathways. We observed that macrophages, T cells CD8, and T cells CD4 memory were the most commonly infiltrated subtypes of immune cells in HNSCC. Finally, an IRGs prognostic model was constructed, and the AUC of the ROC curve was 0.635. Conclusions: Our results suggest that high TMB is associated with poor prognosis in HNSCC patients. The constructed model has potential prognostic value for the prognosis of these individuals, and it needs to be further validated in large-scale and prospective studies.

Background Glioma is the most common intracranial tumor. The inflammatory response actively participates in the malignancy of gliomas. There is still limited knowledge about the biological function of immune‐related genes (IRGs) and their potential involvement in the malignancy of gliomas. Methods We screened differentially expressed and survival‐associated IRGs, and explored their potential molecular characteristics. Then we developed a prognostic index derived from seven hub IRGs. A prognostic nomogram was built to indicate the prognostic value of the prognostic index and seven IRGs. We characterized the immune infiltration landscape to analyze tumor‐immune interactions. The real‐time quantitative polymerase chain reaction assay was performed to validate bioinformatics results. Results The differentially expressed IRGs are involved in cell chemotaxis, cytokine activity, and the chemokine‐mediated signaling pathway. The prognostic index derived from seven IRGs had clinical prognostic value in glioma, and positively correlated with the malignant clinicopathological characteristics. A nomogram further indicated that the prognostic index and seven hub IRGs had clinical prognostic value for gliomas. We revealed that the prognostic index could reflect the state of the glioma immune microenvironment. Conclusion This study demonstrates the importance of an IRG‐based prognostic index as a potential biomarker for predicting malignancy in gliomas., Graphical abstract

Screening and therapeutic programs for colorectal cancer (CRC) are invasive or not effective and unable to meet patient needs. Major advances in immunogenomics may change this status but need more exploration. Differentially expressed genes and immune-related genes (IRGs) were identified by computational methods. A prognostic model was established and validated based on survival-related IRGs via stepwise multivariate Cox regression analysis. Nine IRGs were selected and identified as survival-related genes. A 7-gene prognostic model could offer a preliminary and valid determination of risk in CRC patients. The area under the curve of the receiver operating characteristic was 0.672. The 7-gene prognostic model might be used as a novel prognostic tool in CRC patients.

Purpose Gastric cancer (GC) is one of the most common and fatal malignancies globally. While microsatellite instability (MSI) index has earlier been correlated with survival outcome in gastric cancer patients, the present study aims to construct a risk-stratification model based on immune-related genes in GC patients with varying MSI status. Results The univariate and multivariate Cox regression analyses identified SEMA7A, NUDT6, SCGB3A1, NPR3, PTH1R, and SHC4 as signature genes, which were used to build the prognostic model for GC patients with microsatellite instability-low (MSI-L) and microsatellite stable (MSS). Whereas, for GC patients with microsatellite instability-high (MSI-H), prognostic model was established with three genes (SEMA6A, LTBP1, and BACH2), based on the univariate and multivariate Cox regression, and Kaplan-Meier survival analyses. Conclusion The prognostic immune-related gene signature identified in this study may offer new targets for personalized treatment and immunotherapy for GC patients with MSI-H or MSI-L/MSS status. Methods The Cancer Genome Atlas (TCGA) and ImmPort databases were used to extract expression data and to explore prognostic genes from the immune-related genes (IRGs), respectively. Univariate and multivariate Cox regression analysis were applied to identify IRGs correlated with patient prognosis. The regulatory network between prognostic IRGs and TFs were performed using R software.

Tumor immunity is closely associated with the prognosis of tumors, including osteosarcoma (OS). The aim of the present study was to construct an immune-related prognostic index (PI) to predict the prognosis of OS. Herein, OS expression data were sourced from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. We divided the OS patients into nonmetastatic and metastatic groups, allowing differentially immune-related genes (DIRGs) to be selected. After DIRGs were further investigated by enrichment analysis, four keys prognostic IRGs (CD79A, CSF3R, MTNR1B and NPPC) were identified using a Cox proportional hazards model. Then, an immune-related prognostic index was constructed. Finally, gene set enrichment analysis (GSEA) was employed to further explore the underlying mechanisms. The difference in tumor-infiltrating immune cell (TIIC) abundance was also discussed. In our study, eight upregulated genes and 30 downregulated genes were identified. Several Gene Ontology (GO) terms and the most significantly enriched KEGG pathways were immune-associated functions and pathways. Four genes, including CD79A, CSF3R, MTNR1B and NPPC, were used to establish a risk assessment model for evaluating OS prognosis. GSEA revealed that the risk score was related to cytokine receptor interaction and to the chemokine and B cell receptor signaling pathways. Furthermore, high risk markedly related to the infiltration of several immune cell types, including M2 macrophages, naïve CD4 T cells, and CD8 T cells. In sum, we developed a survival model for OS. The underlying molecular mechanisms of the high-risk group may affect immune-related biological processes and TIICs.Abbreviations TARGET: Therapeutically Applicable Research To Generate Effective Treatments; PI: Prognostic index; OS: Osteosarcoma; DIRGs: Differentially immune-related genes; GSEA: Gene set enrichment analysis; TIIC: Tumor-infiltrating immune cell., Graphical abstract

Background Hepatocellular carcinoma (HCC) is closely associated with the immune microenvironment. To identify the effective population before administering treatment, the establishment of prognostic immune biomarkers is crucial for early HCC diagnosis and treatment. Results A total of 335 IRGs identified from 788 overlapping IRGs were associated with the survival of HCC. A prognostic immunoscore model was identified. The Kaplan-Meier survival curves and time-dependent ROC analysis revealed a powerful prognostic performance of immunoscore signature via multi validation. Besides, the immunoscore signature exhibited a better predictive power compared to other prognostic signatures. Gene set enrichment analysis showed multiple signaling differences between the high and low immunoscore group. Furthermore, immunoscore was significantly associated with multiple immune cells and immune infiltration in the tumor microenvironment. Conclusions We identified the immunoscore as a robust marker for predicting HCC patient survival. Methods Three sets of immune-related genes (IRGs) were integrated to identify the overlapping IRGs. Weighted gene co-expression network analysis was performed to obtain the survival-related IRGs. Further, the prognostic immunoscore model was constructed via LASSO-penalized Cox regression analysis. Then the prognostic performance of immunoscore was evaluated. In addition, ESTIMATE and CIBERSORT algorithms were applied to explore the relationship between immunoscore and tumor immune microenvironment.