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Abstract Background Papillary thyroid carcinoma (PTC) is the most common thyroid cancer. While many patients survive, a portion of PTC cases display high aggressiveness and even develop into refractory differentiated thyroid carcinoma. This may be alleviated by developing a novel model to predict the risk of recurrence. Ferroptosis is an iron-dependent form of regulated cell death (RCD) driven by lethal accumulation of lipid peroxides, is regulated by a set of genes and shows a variety of metabolic changes. To elucidate whether ferroptosis occurs in PTC, we analyse the gene expression profiles of the disease and established a new model for the correlation. Methods The thyroid carcinoma (THCA) datasets were downloaded from The Cancer Genome Atlas (TCGA), UCSC Xena and MisgDB, and included 502 tumour samples and 56 normal samples. A total of 60 ferroptosis related genes were summarised from MisgDB database. Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA) were used to analyse pathways potentially involving PTC subtypes. Single sample GSEA (ssGSEA) algorithm was used to analyse the proportion of 28 types of immune cells in the tumour immune infiltration microenvironment in THCA and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells. Spearman correlation analysis was performed on the ferroptosis gene expression and the correlation between immune infiltrating cells proportion. We established the WGCNA to identify genes modules that are highly correlated with the microenvironment of immune invasion. DEseq2 algorithm was further used for differential analysis of sequencing data to analyse the functions and pathways potentially involving hub genes. GO and KEGG enrichment analysis was performed using Clusterprofiler to explore the clinical efficacy of hub genes. Univariate Cox analysis was performed for hub genes combined with clinical prognostic data, and the results was included for lasso regression and constructed the risk regression model. ROC curve and survival curve were used for evaluating the model. Univariate Cox analysis and multivariate Cox analysis were performed in combination with the clinical data of THCA and the risk score value, the clinical efficacy of the model was further evaluated. Results We identify two subtypes in PTC based on the expression of ferroptosis related genes, with the proportion of cluster 1 significantly higher than cluster 2 in ferroptosis signature genes that are positively associated. The mutations of Braf and Nras are detected as the major mutations of cluster 1 and 2, respectively. Subsequent analyses of TME immune cells infiltration indicated cluster 1 is remarkably richer than cluster 2. The risk score of THCA is in good performance evaluated by ROC curve and survival curve, in conjunction with univariate Cox analysis and multivariate Cox analysis results based on the clinical data shows that the risk score of the proposed model could be used as an independent prognostic indicator to predict the prognosis of patients with papillary thyroid cancer. Conclusions Our study finds seven crucial genes, including Ac008063.2, Apoe, Bcl3, Acap3, Alox5ap, Atxn2l and B2m, and regulation of apoptosis by parathyroid hormone-related proteins significantly associated with ferroptosis and immune cells in PTC, and we construct the risk score model which can be used as an independent prognostic index to predict the prognosis of patients with PTC.

Abstract 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.

Abstract 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.

Abstract Background Considerable evidence has indicated an association between the immune microenvironment and clinical outcome in ccRCC. The purpose of this study is to extensively figure out the influence of immune-related genes of tumors on the prognosis of patients with ccRCC. Methods Files containing 2498 immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort), and the transcriptome data and clinical information relevant to patients with ccRCC were identified and downloaded from the TCGA data-base. Univariate and multivariate Cox regression analyses were used to screen out prognostic immune genes. The immune risk score model was established in light of the regression coefficient between survival and hub immune-related genes. We eventually set up a nomogram for the prediction of the overall survival for ccRCC. Kaplan-Meier (K-M) and ROC curve was used in evaluating the value of the predictive risk model. A P value of

Abstract 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.

BackgroundImmune-related genes (IRGs) are closely connected to the occurrence and development of tumors. Their influence on the prognosis of patients with HCC, however, remains unclear.MethodsFrom the TCGA database, we integrated 365 liver cancer tissues and 50 normal tissues to identify differential immune genes related to prognosis. Multivariate COX analysis was used to establish a new prognostic index on account of IRGs, whereby risk score = (Expression level of HSPA4*0.022) + (Expression level of PSMD14*0.042) + (Expression level of RBP2*0.019) + (Expression level of MAPT*0.197) + (Expression level of TRAF3*0.146) + (Expression level of NDRG1*(0.006) + (Expression level of NRAS*0.027) + (Expression level of IL17D*0.075).ResultsThe risk score was clearly correlated with an unfavorable survival rate and with clinical characteristics. By integrating the immune-related risk score model with clinical features, a nomogram was constructed to predict the survival rate of HCC patients (1-, 3- and 5-year AUC of 0.721, 0.747 and 0.781, respectively).ConclusionWe have established a valuable prognostic risk score for HCC patients that may be a better predictor of survival than the present method. With the risk score's strong predictive value for immune cells and functions, it may provide clinical guidance for the diagnosis and prognosis of different immunophenotypes, and provide multiple therapeutic targets for the treatment of HCC patients based on subtype-specific immune molecules.

Aeromonas are autochthonous bacteria of aquatic environments that are considered to be emerging pathogens to humans, producing diarrhea, bacteremia, and wound infections. Genetic identification shows that 95.4% of the strains associated with clinical cases correspond to the species Aeromonas caviae (37.26%), Aeromonas dhakensis (23.49%), Aeromonas veronii (21.54%), and Aeromonas hydrophila (13.07%). However, few studies have investigated the human immune response against some Aeromonas spp. such as A. hydrophila, Aeromonas salmonicida, and A. veronii. The present study aimed to increase the knowledge about the innate human immune response against six Aeromonas species, using, for the first time, an in vitro infection model with the monocytic human cell line THP-1, and to evaluate the intracellular survival, the cell damage, and the expression of 11 immune-related genes (TLR4, TNF-α, CCL2, CCL20, JUN, RELA, BAX, TP53, CASP3, NLRP3, and IL-1β). Transcriptional analysis showed an upregulated expression of a variety of the monocytic immune-related genes, with a variable response depending upon the Aeromonas species. The species that produced the highest cell damage, independently of the strain origin, coincidentally induced a higher expression of immune-related genes and corresponded to the more prevalent clinical species A. dhakensis, A. veronii, and A. caviae. Additionally, monocytic cells showed an overexpression of the apoptotic and pyroptotic genes involved in cell death after A. dhakensis, A. caviae, and Aeromonas media infection. However, the apoptosis route seemed to be the only way of producing cell damage and death in the case of the species Aeromonas piscicola and Aeromonas jandaei, while A. veronii apparently only used the pyroptosis route.

Introduction Negative symptoms (NS) are a core feature of schizophrenia. NS show heterogeneity proven by factor analysis, which revealed two distinct negative symptoms subdomains: diminished expression (DE) and avolition/apathy (AA) (Marder et al. 2017, Fleischhacker et al. 2019). Some studies showed the different effects of these subdomains on clinical features of schizophrenia that suggest different pathophysiological mechanisms for their development (Stanculete 2021). It has been also shown that the levels of peripheral interleukins (IL) specifically correlate with NS (Enache et al. 2021), in particular, increased IL levels were determined in patients with deficit syndrome compared to non-deficit schizophrenia (Goldsmith et al. 2018). Objectives To search for the association of genes for IL-4, IL-6, IL-10 and C-reactive protein (CRP) with NS subdomains. Methods The total sample included 551 patients (women 51,4%, aged 18-72 years) with ICD-10 diagnosis of schizophrenia. NS were assessed by PANSS. PANSS-derived factors include AA (items N2, N4, G16) and DE (N1, N3, N6, G5, G7, G13). Genotyping was performed for the following polymorphisms: C-589T IL-4, C-174G IL-6, C-592A IL-10, G-1082A IL-10, CRP (rs2794521). Results There are effects of C-592A IL-10 (p=0.017) and G-1082A IL-10 (p=0.012) on AA subdomain. Post-hoc Bonferroni corrected comparisons show that carriers of the haplotype AA (C-592A)- AA (G-1082A) have the highest AA score. A significant effect of CRP (rs2794521) on AA is identified (p=0.007). There is a trend towards the association of C-589T IL-4 and C-174G IL-6 with AA. No association of these polymorphisms with DE was found. Conclusions AA and DE may have different genetic background. Disclosure No significant relationships.

BackgroundColorectal cancer (CRC) is one of the most common malignancies and its incidence and mortality are increasing yearly. 5-Fluorouracil (5-FU) has long been used as a standard first-line treatment for CRC patients. Although 5-FU-based chemotherapy is effective for advanced CRC, the consequent resistance remains a key problem and causes the poor prognosis of CRC patients. Thus, there is an urgent need to identify new biomarkers to predict the response to 5-FU-based chemotherapy.MethodsCRC samples were retrieved from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). The immune-related genes were retrieved from the ImmPort database. Single-cell sequencing results from colorectal cancer were obtained by the ArrayExpress database. 5-FU resistance-related genes were filtered and validated by R packages. ESTIMATE algorithms were used to assess the tumor microenvironment (TME). KEGG and GO analysis were performed to explore the biological signaling pathway for resistant-response patients and sensitive-response patients in the tumor microenvironment. pRRophetic algorithms were used to predict 5-FU sensitivity. GSEA and GSVA analysis was performed to excavate the biological signaling pathway of the RBP7 gene.ResultsNine immune-related genes were identified to be associated with 5-FU resistance and poor disease-free survival (DFS) of CRC patients and the signature of these genes was developed in a DFS-prognostic model. Four immune-related genes were determined to be associated with 5-FU resistance and overall survival (OS) of CRC patients. The signature of these genes was developed an OS-prognostic model. ESTIMATE scores showed a significant difference between 5-FU resistant and 5-FU sensitive CRC patients. Resistant-response patients and sensitive-response patients to 5-FU based chemotherapy showed different GO and KEGG enrichment on the tumor microenvironment. RBP7, as a tumor immune microenvironment (TIME) related gene, was found to have the potential of predicting chemotherapy resistance and poor prognosis of CRC patients. GSEA analysis showed multiple signaling differences between the high and low expression of RBP7 in CRC patients. Hypoxia and TNFα signaling via NFκB gene sets were significantly different between chemotherapy resistant (RBP7High) and chemotherapy sensitive (RBP7Low) patients. Single-cell RNA-seq suggested RBP7 was centrally distributed in endothelial stalk cells, endothelial tip cells, and myeloid cells.ConclusionsImmune-related genes will hopefully be potential prognostic biomarkers to predict chemotherapy resistance for CRC. RBP7 may function as a tumor microenvironment regulator to induce 5-FU resistance, thereby affecting the prognosis of CRC patients.

Breast cancer (BRCA) has the highest incidence rate among female tumours. The function of the immune system affects treatment efficacy and prognosis in patients with BRCA. However, the exact role of immune-related genes (IRGs) in stage N+M0 BRCA is unknown. We constructed a predictive risk scoring model with five IRGs (CDH1, FGFR3, INHBA, S100B, and SCG2) based on the clinical, mutation, and RNA sequencing data of individuals with stage N+M0 BRCA sourced from The Cancer Genome Atlas. Results from the Shandong Cancer Hospital and Institute validation cohort suggested that regardless of clinical stage, tumour size, or the number of lymph node metastases, this model was able to reliably discriminate low-risk patients from high-risk ones and assess the prognosis of patients with stage N+M0 BRCA, and low-risk patients could benefit more from immunotherapy than high-risk patients. In addition, significant inter-group variations in immunocyte infiltration and the tumour microenvironment were observed. Moreover, risk score and age were found to be independent factors in multivariate COX regression analysis, which influenced the outcome of patients with stage N+M0 BRCA. Based on the above findings, we plotted a prognostic nomogram. Finally, we constructed a lncRNA KCNQ1OT1-LINC00665-TUG1/miR-9-5p/CDH1 regulatory axis of the ceRNA network to explore the mechanism of BRCA progression. In summary, we conducted a systemic and extensive bioinformatics investigation and established an IRG-based prognostic scoring model. Finally, we constructed a ceRNA regulatory axis that might play a significant role in BRCA development. More research is required to confirm this result. Scoring system-based patient grouping can help predict the outcome of patients with stage N+M0 BRCA more effectively and determine their sensitivity to immunotherapies, which will aid the development of personalised therapeutic strategies and inspire the research and development of novel medications.

BackgroundLocally advanced rectal cancers (LARC) show a highly variable response to neoadjuvant chemoradiotherapy (nCRT), and the impact of the tumor immune response in this process is poorly understood. This study aimed to characterize the immune-related gene expression profiles (GEP), pathways, and cell types associated with response or resistance to neoadjuvant chemoradiotherapy.MethodsThe transcriptomic and clinical data of Rectal carcinoma from the Gene Expression Omnibus database and Immune-related genes (IRGs) from ImmPort were downloaded to identify the differentially expressed immune-related genes (DEIRGs) between responder and non-responder to neoadjuvant chemoradiotherapy. Gene set enrichment analyses were performed to uncover significantly enriched GO terms and KEGG pathways. Immune cell infiltration was estimated from RNA-sequencing data using ImmuCellAI. Afterward, we constructed an immune-related gene-based predictive model (IRGPM) by Support Vector Machine and validated it in an external cohort.ResultA 15-gene signature (HLA-DPB1, HLA-DQA1, CXCL9, CXCL10, TAP2, INHBB, BMP2, CD74, IL33, CCL11, CXCL11, DEFB1, HLA-DPA1, CCN3, STAT1) was identified as DEIRGs and found to be significantly associated with nCRT outcomes. Gene set enrichment analyses indicated that the 15 genes play active roles in inflammation-related biological processes. In addition, ImmuCellAI revealed that CD4 naive T cells, Tex, Th1 were significantly up-regulated (p=0.035, p=0.02, p=0.0086, respectively), while Tfh were significantly down-regulated (p=0.015) in responder subgroup. Finally, a novel predictive model was developed by SVM based on DEIRGs with an AUC of 80% (internal validation) and 73.5% (external validation).ConclusionOur team conducted a genomic study of the relationship between gene expression profile and response to nCRT in LARC. Our data suggested that the DEIRGs signature could help predict the efficacy of nCRT. And a DEIRGs‐based SVM model was developed to monitor the outcomes of nCRT in LARC.

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

Abstract 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.

Abstract Objective To screen for immune genes that play a major role in Kawasaki disease and to investigate the pathogenesis of Kawasaki disease through bioinformatics analysis. Methods Kawasaki disease‐related datasets GSE18606, GSE68004, and GSE73461 were downloaded from the Gene Expression Omnibus database. Three microarrays were integrated and standardized to include 173 Kawasaki disease samples and 101 normal samples. The samples were analyzed using CIBERSORT to obtain the infiltration of 22 immune cells and analyze the differential immune cells in the samples and correlations. The distribution of the samples was analyzed using principal component analysis (PCA). Immune‐related genes were downloaded, extracted from the screened samples and analyzed for differential analysis (different expression genes [DEG]) and weighted gene co‐expression network analysis (WGCNA). We constructed coexpression networks, and used the cytohobbe tool in Cytoscape to analyze the coexpression networks and select the immune genes that played a key role in them. Results Immune cell infiltration analysis showed that B cells naive, T cells CD8, natural killer (NK) cells activated, and so forth were highly expressed in normal samples. T cells CD4 memory activated, monocytes, neutrophils, and so forth were highly expressed in Kawasaki disease samples. PCA results showed a significant difference in the distribution of normal and Kawasaki disease samples. From the screened samples, 97 upregulated and 103 downregulated immune‐related genes were extracted. WGCNA analysis of DEG yielded 10 gene modules, of which the three most relevant to Kawasaki disease were red, yellow, and gray modules. They were associated with cytokine regulation, T‐cell activation, presentation of T‐cell receptor signaling pathways, and NK cell‐mediated cytotoxicity. CXCL8, CCL5, CCR7, CXCR3, and CCR1 were identified as key genes by constructing a coexpression network. Conclusion Our study shows that we can distinguish normal samples from Kawasaki disease samples based on the infiltration of immune cells, and that CXCL8, CCL5, CCR7, CXCR3, and CCR1 may play important roles in the development of Kawasaki disease.

Tumor microenvironment and alternative polyadenylation (APA) have drawn more attention in cancer research. However, their roles in grade II and III gliomas, termed as lower-grade glioma (LGG) in this study, remain to be fully elucidated. Here, we conducted this study and found that stromal and immune scores were elevated in higher grade and isocitrate dehydrogenase (IDH) wild-type glioma. Besides, higher stromal and immune scores indicated a poor prognosis in patients with LGG. APA events in immune-related genes were associated with overall survival, RNA expression, IDH mutation, and disease-free survival. Patients in the high-risk group had poor prognoses, and the risk score could be used to predict the survival rate. The risk score was positively correlated with the expression of immune checkpoints, inflammatory cytokines, and infiltrated immune cells. Moreover, risk stratification could predict the efficacy of radiotherapy and provide a reference for the treatment of grade III glioma. Our study revealed that immune-related genes with APA events in the microenvironment could predict risk stratification and clinical prognosis in patients with LGG.

Abstract Background Function of the immune system is correlated with the prognosis of the tumor. The effect of immune microenvironment on esophageal cancer (EC) development has not been fully investigated. Methods This study aimed to explore a prognostic model based on immune-related genes (IRGs) for EC. We obtained the RNA-seq dataset and clinical information of EC from the Cancer Genome Atlas (TCGA). Results We identified 247 upregulated IRGs and 56 downregulated IRGs. Pathway analysis revealed that the most differentially expressed IRGs were enriched in Cytokine-cytokine receptor interaction. We further screened 13 survival-related IRGs and constructed regulatory networks involving related transcription factors (TFs). Finally, a prognostic model was constructed with 9 IRGs (HSPA6, S100A12, CACYBP, NOS2, DKK1, OSM, STC2, NGPTL3 and NR2F2) by multivariate Cox regression analysis. The patients were classified into two subgroups with different outcomes. When adjusted with clinical factors, this model was verified as an independent predictor, which performed accurately in prognostic prediction. Next, M0 and M2 macrophages and activated mast cells were significantly enriched in high-risk group, while CD8 T cells and regulatory T cells (Tregs) were significantly enriched in low-risk group. Conclusions Prognosis related IRGs were identified and a prognostic signature for esophageal cancer based on nine IRGs was developed.

Abstract Background Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC. Methods RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT. Results A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment. Conclusions Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

There is a correlation between tumors and immunity with the degree of immune cell infiltration in tumors being closely related to tumor growth and progression. Therefore, the present study identified immune-related prognostic genes and evaluated the immune infiltration level in lung adenocarcinoma (LUAD). This study performed Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and Gene Set Enrichment Analysis (GSEA) enrichment analyses on differential immune-associated genes. A risk model was created and validated using six immune-related prognostic genes. Reverse transcription-quantitative PCR was used to assess the prognostic gene expression in non-small cell lung cancer cells. Immune cell infiltration in LUAD was analyzed using the CIBERSORT method. Single sample GSEA was used to compare Tumor Immune Dysfunction and Exclusion (TIDE) scores between high and low-risk groups and to assess the activation of thirteen immune-related pathways. Multifactor Cox proportional hazards model analysis identified six prognostic risk genes (S100A16, FURIN, FGF2, LGR4, TNFRSF11A and VIPR1) to construct a risk model. The survival and receiver operating characteristic curves indicated that patients with higher risk scores had lower overall survival rates. The expression levels of prognostic genes S100A16, FURIN, LGR4, TNFRSF11A and VIPR1 were significantly increased in LUAD. B cells naive, plasma cells, T cells CD4 memory activated, T cells follicular helper, T cells regulatory, NK cells activated, macrophages M1, macrophages M2, and Dendritic cells resting cells showed elevated expression in LUAD. The prognostic genes were differentially associated with individual immune cells. Immune-related function scores, such as those for antigen presenting cell (APC) co-stimulation, APC co-inhibition, check-point, Cytolytic-activity, chemokine receptor, parainflammation, major histocompatibility complex-class-I, type-I–IFN-reponse and T-cell-co-inhibition, were higher in the high-risk group compared with the low-risk group. Furthermore, the TIDE score of the high-risk group was significantly lower than the low-risk group. This immune-related gene prognostic model has the potential to predict the prognosis of LUAD patients, supporting the development of a personalized clinical diagnosis and treatment plan. [ABSTRACT FROM AUTHOR]

Japanese eel, Anguilla japonica , holds significant importance in Taiwanese aquaculture. With the intensification of eel farming, the impact of Edwardsiella tarda has become increasingly severe. Consequently, the abusive use of antibiotics has risen. Bacillus subtilis natto NTU-18, a strain of Bacillus with a high survival rate in feed processing, plays a crucial role in promoting intestinal health through competitive rejection, enhancing immune responses against bacterial pathogens, and improving intestinal health by modulating gastrointestinal microbiota to produce beneficial metabolites of mice and grass carp, Ctenopharyngodon idella. This study investigated the effects of different proportions (control, 0.25 %, 0.5 %, 1 %, and 2 %) of B. subtilis natto NTU-18 added to paste feed on the growth performance, intestinal morphology, and microbiota, expression of immune-related genes, and resistance to E. tarda in Japanese glass eel. The results indicated that the growth performance of all groups with B. subtilis natto NTU-18 added was significantly higher than that of the control group and did not impact the villi morphology. The expression of immune-related genes in the kidney, specifically HSP70 and SOD, was significantly higher from 0.5 % and above than the control; however, no significant differences were observed in CAT, POD, and HSP90. In the liver, significant differences were found in HSP70 and IgM above 0.25 % compared to the control group, with no significant differences in SOD, CAT, POD, and HSP90 among all groups. Additionally, intestinal microbiota analysis revealed that the 2 % additional group had significantly lower diversity than other groups, with Cetobacterium as the dominant species. The challenge test observed that the survival rates of the 0.5 % and 1 % groups were significantly higher. This research suggests that adding 0.5 % and 1 % of B. subtilis natto NTU-18 to the diet is beneficial for Japanese glass eel's immunity, growth performance, and disease resistance. • Bacillus subtilis natto NTU-18 showed as great eel paste feed additive. • 0.5–1 % B. subtilis natto NTU-18 supplement was the most optimal for Anguilla japonica glass eel. • Overdosed of B. subtilis natto NTU-18 would reduce the gastrointestinal microbiota diversity of Japanese glass eel. [ABSTRACT FROM AUTHOR]

Recent studies have increasingly linked miRNAs with the modulation of inflammatory responses and immunosuppressive activities. This investigation reveals that mir-30e-3p selectively binds to and modulates gimap8 , as demonstrated by luciferase reporter assays and qPCR analyses. Upon LPS stimulation of CIK cells, mir-30e-3p expression was notably elevated, inversely correlating with a decrease in gimap8 mRNA levels. Overexpression of mir-30e-3p attenuated the mRNA levels of pro-inflammatory cytokines beyond the effect of LPS alone, suggesting a regulatory role of mir-30e-3p in inflammation mediated by the gimap8 gene. These insights contribute to our understanding of the complex mechanisms governing inflammatory and immune responses. • Mir-30e-3p can regulate the inflammatory response by targeting the gimap8 gene. • Mir-30e-3p can reduce pro-inflammatory cytokine mRNA levels beyond LPS alone. • Mir-30e-3p can weaken the cellular migration of CIK cells. [ABSTRACT FROM AUTHOR]

Cancer immunotherapy plays a crucial role in bladder cancer (BC) progression. Increasing evidence has elucidated the clinicopathologic significance of the tumor microenvironment (TME) in predicting outcomes and therapeutic efficacy. This study sought to establish a comprehensive analysis of the immune-gene signature combined with TME to assist in BC prognosis. We selected sixteen immune-related genes (IRGs) after a weighted gene co-expression network and survival analysis. Enrichment analysis revealed that these IRGs were actively involved in Mitophagy and Renin secretion pathways. After multivariable COX analysis, the IRGPI comprising NCAM1, CNTN1, PTGIS, ADRB3, and ANLN was established to predict the overall survival of BC, which was validated in both TCGA and GSE13507 cohorts. In addition, a TME gene signature was developed for molecular and prognosis subtyping with unsupervised clustering, followed by a panoramic landscape characterization of BC. In summary, the IRGPI model developed in our study provided a valuable tool with an improved prognosis for BC.

Sarcomas are a group of malignant tumors derived from mesenchymal tissues that display complex and variable pathological types. The impact of the immune properties of the tumor microenvironment (TME) on the prognosis, treatment, and management of sarcomas has attracted attention, requiring the exploration of sensitive and accurate signatures. In this study, The Cancer Genome Atlas (TCGA) database was searched to screen for an RNA sequencing dataset, retrieving 263 sarcoma and 2 normal samples with survival data. Genes associated with immune regulation in sarcomas were retrieved from the Tumor Immune Estimation Resource database to estimate tumor purity and immune cell infiltration levels. The samples were then divided into the immune-high and immune-low groups. Then, we screened for differentially expressed genes (DEGs) between the two groups. The intersection between immune-related genes and DEGs was then determined. Univariate Cox and least absolute shrinkage and selection operator analyses were used to select ideal genes for prognostic prediction and subsequent construction of a risk signature. A survival analysis was performed to reveal the dissimilarity in survival between the high- and low-score groups. Finally, a nomogram was generated to verify the accuracy and reliability of the signature. Through Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression (ESTIMATE) analysis, high ESTIMATE, and low tumor purity were significantly associated with a favorable prognosis. Moreover, a total of 5259 DEGs were retrieved, the majority of which were downregulated. In total, 590 immune-associated genes overlapped with the DEGs, among which nine hub genes were identified. Finally, two candidate genes, ACVR2B and NFYA, were identified, based on which a risk signature was constructed. The risk signature constructed in this study is accurate and reliable for the prognostic prediction and phenotyping of sarcomas.

Background: Laryngeal cancer represents a common malignancy that originates from the larynx, with unfavorable prognosis. Herein, this study systematically analyzed the immune signatures of laryngeal cancer and to evaluate their roles on tumor progression.Methods: Differentially expressed immune-related genes (IRGs) were screened between laryngeal cancer and normal tissues from TCGA dataset. Then, two prognosis-related IRGs AQP9 and ZAP70 were analyzed by a series of survival analysis. Based on them, molecular subtypes were constructed by unsupervised cluster analysis. Differences in survival outcomes, HLA expression and immune cell infiltrations were assessed between subtypes. Expression of AQP9 and ZAP70 was validated in laryngeal cancer tissues and cells by RT-qPCR and immunohistochemistry. After silencing and overexpressing AQP9 and ZAP70, CCK-8, EdU, wound healing and transwell assays were performed in TU212 and LCC cells.Results: Totally, 315 IRGs were abnormally expressed in laryngeal cancer. Among them, AQP9 and ZAP70 were distinctly correlated to patients' prognosis. Two subtypes were developed with distinct survival outcomes, HLA expression and immune microenvironment. Low expression of AQP9 and ZAP70 was confirmed in laryngeal cancer. AQP9 and ZAP70 up-regulation distinctly suppressed proliferation, migration, and invasion of laryngeal cancer cells. The opposite results were investigated when their knockdown.Conclusions: Our findings revealed the roles of AQP9 and ZAP70 in progression of laryngeal cancer, and suggested that AQP9 and ZAP70 could potentially act as candidate immunotherapeutic targets for laryngeal cancer. [ABSTRACT FROM AUTHOR]

Objective: To construct an immune-related gene prognostic index (IRGPI) for breast cancer (BC) and investigate its prognostic specificity and the molecular and immune characteristics. Methods: BC hub genes were identified from The Cancer Genome Atlas and immune-related databases using weighted gene co-expression network analysis (WGCNA). IRGPI was constructed using univariate, LASSO, and multivariate regression analyses, and was validated with GSE58812 and GSE97342 in the Gene Expression Omnibus database (GEO). At the same time, we evaluated the predictive ability of IRGPI for different BC subtypes. Subsequently, the molecular and immune characteristics, clinical relevance, and benefits of immune checkpoint inhibitor treatment were analyzed for different IRGPI subgroups. Results: IRGPI consisted of six genes: SOCS3, TCF7L2, TSLP NPR3, ANO6, and HMGB3. The IRGPI 1-, 5-, and 10-years area under curve (AUC) values were 0.635, 0.752, and 0.753, respectively, indicating that IRGPI has good potential in predicting the long-term survival of BC patients, consistent with the results in the GEO cohort. IRGPI showed good predictive power in four different breast cancer subtypes: ER positive, PR positive, HER2 positive and triple-negative (P <0.01). Compared with the low-IRGPI group, the high-IRGPI group had a worse prognosis and a lower degree of immune infiltrating cells (p < 0.05). IRGPI showed specificity in distinguishing age, TNM stage, ER, and HER2 statuses, and our study found that the high-IRGPI group had low tumor immune dysfunction and exclusion (TIDE), microsatellite instability (MSI), and T cell dysfunction scores (p < 0.05). In addition, compared with the TIDE and TIS models, showed that the AUCs of IRGPI were better during the 5-year follow-up. Conclusion: IRGPI can be used as an independent prognostic indicator of breast cancer, providing a method for monitoring the long-term treatment of BC. [ABSTRACT FROM AUTHOR]

The present study evaluated the growth performance, digestive enzyme activity, non-specific immunity, immunity and growth genes in Penaeus vannamei fed diets supplemented with Bovine lactoferricin (the basal diet without Bovine lactoferricin, the control; 1.0‰ Bovine lactoferricin，LCB1; 1.5‰ Bovine lactoferricin，LCB1.5; 2.0‰ Bovine lactoferricin, LCB2; 2.5‰ Bovine lactoferricin, LCB2.5) for 56 days. The feeding trial showed that the final weight, weight gain rate, and specific growth rate of the shrimp were improved significantly, while the feed conversion ratio was reduced significantly in the LCB1.5 group compared to the control (P < 0.05). The challenge test of Vibrio parahaemolyticus showed that the cumulative mortalities of shrimp in the LCB1.5, LCB2 and LCB2.5 groups were significantly lower than that in the control (P < 0.05). Compared with the control, Lipase and Trypsin activities in the hepatopancreas of LCB1.5 and LCB2 groups were significantly enhanced (P < 0.05). Compared with the control, alkaline phosphatase, acid phosphatase activities in the hepatopancreas and the relative expression levels of Relish , Toll , JAK , STAT , TOR , Raptor , 4E-BP , eIF4E1α , eIF4E2 genes in the hepatopancreas of LCB1.5, LCB2 and LCB2.5 groups were all significantly enhanced (P < 0.05). These results suggested that dietary Bovine lactoferricin could improve the growth performance, digestive capacity and immune responses of shrimp. When resistance against Vibrio parahaemolyticus in shrimp is considered, high dosage of Bovine lactoferricin showed a better effect than low dosage of Bovine lactoferricin. However, high dosage of Bovine lactoferricin can have a negative impact on the growth performance of shrimp. Considering collectively the above, Bovine lactoferricin could improve the growth performance, digestive enzymes activities, immune responses and disease resistance of P. vannamei. • 1.5–2.0‰ of LFcinB improved the growth performance and immune responses of shrimp. • 1.5–2.5‰ of LFcinB improved the resistance of shrimp against V. parahaemolyticus. • LFcinB promoted the expression of the mTOR signaling pathway. [ABSTRACT FROM AUTHOR]

Hong Kong oyster (Crassostrea hongkongensis) is one of the main species of economic shellfish cultivated in the coastal areas of southern China. The cultivation of this shellfish may be adversely impacted by Vibrio parahaemolyticus , a harmful pathogenic bacterium for many mariculture species, as it usually exists on the surface of Hong Kong oysters. Although previous studies have discovered that oysters rely on non-specific immune system to fight pathogen invasion, the genes corresponding to the complex immune system against Vibrio is still not fully elucidated. Therefore, we conducted a transcriptome analysis on the gill from Hong Kong oysters at two time points (i.e., 12 h and 24 h after V. parahaemolyticus or PBS challenge) to identify potential immune genes against V. parahaemolyticus infection. A total of 61779 unigenes with the average length of 1221 bp were obtained, and the annotation information of 39917 unigenes were obtained from Nr, SwissProt, KEGG and COG/KOG. After a pairwise comparison between V. parahaemolyticus or PBS challenge at the two time points, three groups of differentially expressed genes induced by V. parahaemolyticus were captured and analyzed. GO and KEGG analyses showed that multiple immune-related genes played an important role in pathogen infection, including HSP70, PCDP3 and TLR4. Furthermore, genes annotation indicated that LITAF, TNFSF10, Duox2 and big defensin family are also involved in immune regulation. Our study provides a reference for further exploration the molecular mechanism that defenses the pathogen infection regarding the identified immune-related genes in Hong Kong oysters. • RNA-seq was used to investigate the immune response of Hong Kong oyster against V. parahaemolyticus infection in our study. • We mainly focused on the analysis of three groups of differentially expressed genes induced by V. parahaemolyticus. •Our results reflected potential role of multiple immune-related genes in the immune response of Hong Kong oyster. [ABSTRACT FROM AUTHOR]

Objective: We aimed to identify feature immune-related genes that correlated with graft rejection and to develop a prognostic model based on immune-related genes in kidney transplantation. Methods: Gene expression profiles were obtained from the GEO database. The GSE36059 dataset was used as a discovery cohort. Then, differential expression analysis and a machine learning method were performed to select feature immune-related genes. After that, univariate and multivariate Cox regression analyses were used to identify prognosis-related genes. A novel Riskscore model was built based on the results of multivariate regression. The levels of these feature genes were also confirmed in an independent single-cell dataset and other GEO datasets. Results: 15 immune-related genes were expressed differently between non-rejection and rejection kidney allografts. Those differentially expressed immune-related genes (DE-IRGs) were mainly associated with immune-related biological processes and pathways. Subsequently, a 5-immune-gene signature was constructed and showed favorable predictive results in the GSE21374 dataset. Recipients were divided into the high-risk and low-risk groups according to the median value of RiskScore. The GO and KEGG analysis indicated that the differentially expressed genes (DEGs) between high-risk and low-risk groups were mainly involved in inflammatory pathways, chemokine-related pathways, and rejection-related pathways. Immune infiltration analysis demonstrated that RiskScore was potentially related to immune infiltration. Kaplan-Meier survival analysis suggested that recipients in the high-risk group had poor graft survival. AUC values of 1- and 3-year graft survival were 0.804 and 0.793, respectively. Conclusion: Our data suggest that this immune-related prognostic model had good sensitivity and specificity in predicting the 1- and 3-year kidney graft survival and might act as a useful tool for predicting kidney graft loss. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is among the major diseases threatening human health. Although the immune response plays an important role in tumor development, its exact mechanisms are unclear. Materials and methods: Here, we used CIBERSORT and ESTIMATE algorithms to determine the proportion of tumor-infiltrating immune cells (TICs) as well as the number of immune and mesenchymal components from the data of 474 lung cancer patients from the Gene Expression Omnibus database. And we used data from The Cancer Genome Atlas database (TCGA) for validation. Results: We observed that immune, stromal, and assessment scores were only somewhat related to survival with no statistically significant differences. Further investigations revealed these scores to be associated with different pathology types. GO and KEGG analyses of differentially expressed genes revealed that they were strongly associated with immunity in lung cancer. In order to determine whether the signaling pathways identified by GO and KEGG signaling pathway enrichment analyses were up- or down-regulated, we performed a gene set enrichment analysis using the entire matrix of differentially expressed genes. We found that signaling pathways involved in hallmark allograft rejection, hallmark apical junction, hallmark interferon gamma response, the hallmark P53 pathway, and the hallmark TNF-α signaling via NF-ĸB were up-regulated in the high-ESTIMATE-score group. CIBERSORT analysis for the proportion of TICs revealed that different immune cells were positively correlated with the ESTIMATE score. Cox regression analysis of the differentially expressed genes revealed that CPA3, C15orf48, FCGR1B, and GNG4 were associated with patient prognosis. A prognostic model was constructed wherein patients with high-risk scores had a worse prognosis (p < 0.001 using the log-rank test). The Area Under Curve (AUC)value for the risk model in predicting the survival was 0.666. The validation set C index was 0.631 (95% CI: 0.580–0.652). The AUC for the risk formula in the validation set was 0.560 that confirmed predictivity of the signature. Conclusion: We found that immune-related gene expression models could predict patient prognosis. Moreover, high- and low-ESTIMATE-score groups had different types of immune cell infiltration. [ABSTRACT FROM AUTHOR]

The molecular mechanism of AAA formation is still poorly understood and has not been fully elucidated. The study was designed to identify the immune-related genes, immune-RAS in AAA using bioinformatics methods. The GSE175683 datasets were downloaded from the GEO database. The DEseq2 software was used to identify differentially expressed genes (DEGs). SUVA pipeline was used to quantify AS events and RAS events. KOBAS 2.0 server was used to identify GO terms and KEGG pathways to sort out functional categories of DEGs. The CIBERSORT algorithm was used with the default parameter for estimating immune cell fractions. Nine samples from GSE175683 were used to construct the co-disturbed network between expression of SFs and splicing ratio of RAS events. PCA analysis was performed by R package factoextra to show the clustering of samples, and the pheatmap package in R was used to perform the clustering based on Euclidean distance. The results showed that there were 3,541 genes significantly differentially expressed, of which 177 immune-related genes were upregulated and 48 immune-related genes were downregulated between the WT and WTA group. Immune-RAS events were mainly alt5P and IR events, and about 60% of it was complex splicing events in AAA. The WT group and the WTA group can be clearly distinguished in the first principal component by using the splicing ratio of immune-RAS events. Two downregulated genes, Nr4a1 and Nr4a2, and eight upregulated genes, Adipor2, Akt2, Bcl3, Dhx58, Pparg, Ptgds, Sytl1, and Vegfa were identified among the immune-related genes with RAS and DEGs. Eighteen differentially expressed SFs were identified and displayed by heatmap. The proportion of different types of cells and ratio of the average ratio of different cells were quite different. Both M1 and M2 types of macrophages and plasma cells were upregulated, while M0 type was downregulated in AAA. The proportion of plasma cells in the WTA group had sharply increased. There is a correlation between SF expression and immune cells/immune-RAS. Sf3b1, a splicing factor with significantly different expression, was selected to bind on a mass of immune-related genes. In conclusion, our results showed that immune-related genes, immune-RAS, and SFs by genome-wide identification were involved in AAA. [ABSTRACT FROM AUTHOR]

Background: Immune response is prevalently related with major depressive disorder (MDD) pathophysiology. However, the study on the relationship between immune-related genes (IRGs) and immune infiltrates of MDD remains scarce. Methods: We extracted expression data of 148 MDD patients from 2 cohorts, and systematically characterized differentially expressed IRGs by using limma package in R software. Then, the LASSO and multivariate logistic regression analysis was used to identify the most powerful IRGs. Next, we analyzed the relationship between IRGs and immune infiltrates of MDD. Finally, GSE76826 was used to to verificate of IRGs as a diagnostic markers in MDD. Results: 203 different IRGs s in MDD has been identified (P < 0.05). GSEA revealed that the different IRGs was more likely to be enriched in immune-specific pathways. Then, a 9 IRGs was successfully established to predict MDD based on LASSO. Next, 4 IRGs was obtained by multivariate logistic regression analysis, and AUC for CD1C, SPP1, CD3D, CAMKK2, and IRGs model was 0.733, 0.767, 0.816, 0.800, and 0.861, suggesting that they have a good diagnostic performance. Furthermore, the proportion of T cells CD8, T cells γδ, macrophages M0, and NK cells resting in MDD group was lower than that in the healthy controls, suggesting that the immune system in MDD group is impaired. Simultaneously, CD3D was validated a reliable marker in MDD, and was positively correlated with T cells CD8. GSEA revealed high expression CD3D was more likely to be enriched in immune-specific pathways, and low expression CD3D was more likely to be enriched in glucose metabolism metabolism-specific pathways. Conclusions: We applied bioinformatics approaches to suggest that a 4 IRGs could serve as diagnostic markers to provide a novel direction to explore the pathogenesis of MDD. [ABSTRACT FROM AUTHOR]

Abstract Introduction Immune checkpoint inhibitors (ICIs) have become a frontier in the field of clinical technology for advanced non-small cell lung cancer (NSCLC). Currently, the predictive biomarker of ICIs mainly including the expression of PD-L1, TMB, TIICs, MMR and MSI-H. However, there are no official biomarkers to guide the treatment of ICIs and to determine the prognosis. Therefore, it is essential to explore a systematic nomogram to predict the prognosis of ICIs treatment in NSCLC Methods In this work, we obtained gene expression and clinical data of NSCLC patients from the TCGA database. Immune-related genes (IRGs) were downloaded from the ImmPort database. The detailed clinical annotation and response data of 240 advanced NSCLC patients who received ICIs treatment were obtained from the cBioPortal for Cancer Genomics. Kaplan–Meier survival analysis was used to perform survival analyses, and selected clinical variables to develop a novel nomogram. The prognostic significance of FGFR4 was validated by another cohort in cBioPortal for Cancer Genomics. Results 3% of the NSCLC patients harbored FGFR4 mutations. The mutation of FGFR4 were confirmed to be associated with PD-L1, and TMB. Patients harbored FGFR4 mutations were found to have a better prolonged progression-free survival (PFS) to ICIs treatment (FGFR4: P = 0.0209). Here, we built and verified a novel nomogram to predict the prognosis of ICIs treatment for NSCLC patients. Conclusion Our results showed that FGFR4 could serve as novel biomarkers to predict the prognosis of ICIs treatment of advanced NSCLC. Our systematic prognostic nomogram showed a great potential to predict the prognosis of ICIs for advanced NSCLC patients.

Foot-and-mouth disease is a highly infectious viral disease affecting all cloven-footed domestic animals. The three foot-and-mouth disease virus (FMDV) serotypes A, O and SAT2 are at present the greatest threat to susceptible animals in Egypt. The aim of the present study was, for the host factors associated with different FMDV infections in cattle during the acute phase, to compare these factors’ influence on the expression of the IL-10, TLR-2, TNF-α, CXCL10, CD48, NFATC4 and IFNG inflammatory and immune-related genes.

The aim of this study was to assess the effect of nucleotides administration on growth performance and immune response in post-weaning piglets. Twenty-eight male weaned piglets, homogeneous for age and weight were randomly allocated to two experimental treatments. Treated group (T) was daily orally administered 0.8 g/head of a mixture of nucleotides suspended in 2.1 mL water solution; while control group (C) received 2.1 mL saline solution. Body weight (BW) and average daily gain (ADG) were individually recorded weekly, while feed intake (FI), and gain:feed (G:F) were recorded and calculated on pen basis. Faecal score was evaluated every seven days. On day 0, 9, 18 and 27 blood samples were collected to determine IgA, IgG and haptoglobin concentration. At day 28 all piglets were sacrificed, and tissue samples of ileal Peyer’s patches were collected for the evaluation of IL1α, IL1β, IL6, IL10, TNFα, TLR2, TLR4 and PPARγ gene expression. Nucleotides supplementation significantly increased BW (17.37 vs. 19.00 kg/pig; p =

ObjectiveTo construct an immune-related gene prognostic index (IRGPI) for breast cancer (BC) and investigate its prognostic specificity and the molecular and immune characteristics.MethodsBC hub genes were identified from The Cancer Genome Atlas and immune-related databases using weighted gene co-expression network analysis (WGCNA). IRGPI was constructed using univariate, LASSO, and multivariate regression analyses, and was validated with GSE58812 and GSE97342 in the Gene Expression Omnibus database (GEO). At the same time, we evaluated the predictive ability of IRGPI for different BC subtypes. Subsequently, the molecular and immune characteristics, clinical relevance, and benefits of immune checkpoint inhibitor treatment were analyzed for different IRGPI subgroups.ResultsIRGPI consisted of six genes: SOCS3, TCF7L2, TSLP NPR3, ANO6, and HMGB3. The IRGPI 1-, 5-, and 10-years area under curve (AUC) values were 0.635, 0.752, and 0.753, respectively, indicating that IRGPI has good potential in predicting the long-term survival of BC patients, consistent with the results in the GEO cohort. IRGPI showed good predictive power in four different breast cancer subtypes: ER positive, PR positive, HER2 positive and triple-negative (P

Lung cancer has the highest tumor incidence in China. Lung squamous cell carcinoma (LUSC) is the most common type, accounting for 40–51% of primary lung cancers. LUSC is slow in growth and late in metastasis. Immune-related genes (IRGs) and immune infiltrating cells play a vital role in the clinical outcomes of LUSC. It is important to systematically study its immune gene map to help the prognosis of cancer patients. In this study, we combined the prognostic landscape and expression status of IRGs downloaded from the TCGA and InnatedDB databases and systematically analyzed the prognostic information of LUSC patients to obtain IRGs. After systematically exploring the survival analysis, prognosis-related genes were found, and the PPI network revealed that a total of 11 genes were hub genes. A two-gene prognosis risk model was established by multivariate Cox analysis. Two IRGs were closely correlated with the prognosis of LUSC. Based on these two genes, a new independent prognostic risk model was established, and this model was further verified in the GEO database. Moreover, the risk score of the model was correlated with sex, survival status, and lymphatic metastasis in LUSC patients, and the predictive risk of the prognostic risk model was significantly positively correlated with five kinds of immune cells (CD4 T cells, CD8 T cells, neutrophils, macrophages, and dendritic cells). This study comprehensively analyzed immunogenomics and presented immune-related prognostic biomarkers for LUSC.

Immunotherapy has shown excellent therapeutic effects on various malignant tumors; however, to date, immunotherapy for osteosarcoma is still suboptimal. In this study, we performed comprehensive bioinformatic analysis of immune-related genes (IRGs) and tumor-infiltrating immune cells (TIICs). Datasets of differentially expressed IRGs were extracted from the GEO database (GSE16088). The functions and prognostic values of these differentially expressed IRGs were systematically investigated using a series of bioinformatics methods. In addition, CCK8 and plate clone formation assays were used to explore the effect of PGF on osteosarcoma cells, and twenty-nine differentially expressed IRGs were identified, of which 95 were upregulated and 34 were downregulated. Next, PPI was established for Identifying Hub genes and biology networks by Cytoscape. Six IRGs (APLNR, TPM2, PGF, CD86, PROCR, and SEMA4D) were used to develop an overall survival (OS) prediction model, and two IRGs (HLA-B and PGF) were used to develop a relapse-free survival (RFS) prediction model. Compared with the low-risk patients in the training cohort (GSE39058) and TARGET validation cohorts, high-risk patients had poorer OS and RFS. Using these identified IRGs, we used OS and RFS prediction nomograms to generate a clinical utility model. The risk scores of the two prediction models were associated with the infiltration proportions of some TIICs, and the activation of memory CD4 T-cells was associated with OS and RFS. CD86 was associated with CTLA4 and CD28 and influenced the infiltration of different TIICs. In vitro experiments showed that the knockdown of PGF inhibited the proliferation and viability of osteosarcoma cells. In conclusion, these findings help us better understand the prognostic roles of IRGs and TIICs in osteosarcoma, and CD86 and PGF may serve as specific immune targets.

The molecular mechanism of AAA formation is still poorly understood and has not been fully elucidated. The study was designed to identify the immune-related genes, immune-RAS in AAA using bioinformatics methods. The GSE175683 datasets were downloaded from the GEO database. The DEseq2 software was used to identify differentially expressed genes (DEGs). SUVA pipeline was used to quantify AS events and RAS events. KOBAS 2.0 server was used to identify GO terms and KEGG pathways to sort out functional categories of DEGs. The CIBERSORT algorithm was used with the default parameter for estimating immune cell fractions. Nine samples from GSE175683 were used to construct the co-disturbed network between expression of SFs and splicing ratio of RAS events. PCA analysis was performed by R package factoextra to show the clustering of samples, and the pheatmap package in R was used to perform the clustering based on Euclidean distance. The results showed that there were 3,541 genes significantly differentially expressed, of which 177 immune-related genes were upregulated and 48 immune-related genes were downregulated between the WT and WTA group. Immune-RAS events were mainly alt5P and IR events, and about 60% of it was complex splicing events in AAA. The WT group and the WTA group can be clearly distinguished in the first principal component by using the splicing ratio of immune-RAS events. Two downregulated genes, Nr4a1 and Nr4a2, and eight upregulated genes, Adipor2, Akt2, Bcl3, Dhx58, Pparg, Ptgds, Sytl1, and Vegfa were identified among the immune-related genes with RAS and DEGs. Eighteen differentially expressed SFs were identified and displayed by heatmap. The proportion of different types of cells and ratio of the average ratio of different cells were quite different. Both M1 and M2 types of macrophages and plasma cells were upregulated, while M0 type was downregulated in AAA. The proportion of plasma cells in the WTA group had sharply increased. There is a correlation between SF expression and immune cells/immune-RAS. Sf3b1, a splicing factor with significantly different expression, was selected to bind on a mass of immune-related genes. In conclusion, our results showed that immune-related genes, immune-RAS, and SFs by genome-wide identification were involved in AAA.

ObjectiveWe aimed to identify feature immune-related genes that correlated with graft rejection and to develop a prognostic model based on immune-related genes in kidney transplantation.MethodsGene expression profiles were obtained from the GEO database. The GSE36059 dataset was used as a discovery cohort. Then, differential expression analysis and a machine learning method were performed to select feature immune-related genes. After that, univariate and multivariate Cox regression analyses were used to identify prognosis-related genes. A novel Riskscore model was built based on the results of multivariate regression. The levels of these feature genes were also confirmed in an independent single-cell dataset and other GEO datasets.Results15 immune-related genes were expressed differently between non-rejection and rejection kidney allografts. Those differentially expressed immune-related genes (DE-IRGs) were mainly associated with immune-related biological processes and pathways. Subsequently, a 5-immune-gene signature was constructed and showed favorable predictive results in the GSE21374 dataset. Recipients were divided into the high-risk and low-risk groups according to the median value of RiskScore. The GO and KEGG analysis indicated that the differentially expressed genes (DEGs) between high-risk and low-risk groups were mainly involved in inflammatory pathways, chemokine-related pathways, and rejection-related pathways. Immune infiltration analysis demonstrated that RiskScore was potentially related to immune infiltration. Kaplan-Meier survival analysis suggested that recipients in the high-risk group had poor graft survival. AUC values of 1- and 3-year graft survival were 0.804 and 0.793, respectively.ConclusionOur data suggest that this immune-related prognostic model had good sensitivity and specificity in predicting the 1- and 3-year kidney graft survival and might act as a useful tool for predicting kidney graft loss.

The present study evaluated the effects of dietary supplementation with poly-β-hydroxybutyrate (PHB) on the growth performance, non-specific immunity, immune-related gene expression in the Nuclear factor-κB (NF-κB) signaling pathway, and intestinal microbiota of the sea cucumber Apostichopus japonicus. During a 63-day feeding trial, the sea cucumber was fed with basal diet (control, C treatment) and treatment diets supplemented at different doses of PHB, i.e., 0.5% (P treatment), 1% (OP treatment), and 3% PHB (TP treatment) (w/w) followed by a stimulation test of inactivated Vibrio splendidus for 7 days. All PHB treatments significantly promoted the specific growth rate and weight gain rate of the sea cucumber (P < 0.05). The 1% PHB significantly enhanced phagocytic, respiratory burst, superoxide dismutase, alkaline phosphatase, acid phosphatase, catalase, and lysozyme activities in the coelomocytes of A. japonicus (P < 0.05). However, no elevated activities of catalase and lysozyme were observed in the P treatment (P < 0.05), and only increased catalase activity appeared in the TP treatment (P < 0.05). Quantitative real-time PCR revealed significantly higher expression levels of Aj-p105, Aj-p50, and Aj-rel in the mid-intestine tissue of the sea cucumber in the PHB treatments (P < 0.05), and the relative expression level Aj-lys gene was significantly higher in the OP treatment (P < 0.05) than that in the control. After injection of inactivated V. splendidus, the relative expression level of four immune-related genes in the OP treatment was significantly up-regulated at 24 h (P < 0.05). The richness of intestinal microbiota in PHB treatments significantly increased, while diversity in TP treatment significantly decreased (P < 0.05). The relative abundances of Rhodobacteraceae in the PHB treatments were significantly higher than that in the control (P < 0.05). Network analysis revealed that 0.5 and 1% PHB supplement enhanced the stability of the intestinal microbial ecosystem. Functional prediction revealed that the PHB diet significantly increased some potential functions of intestinal microbiota, involving amino acid, lipid, and nucleotide metabolisms. In summary, dietary supplementation with a suitable dose of PHB had shown multiple beneficial effects on A. japonicus. Considering collectively the above, the optimum dose of 1% PHB addition to the diet of A. japonicus is recommended.

Background: Liver cancer is one of the most common malignant tumors in the world. T cell-mediated antitumor immune response is the basis of liver cancer immunotherapy. Objective: To screen and analyze the RNAs associated with activated memory CD4 T cells and CD8 T cells in liver cancer. Methods : ESTIMATE was used to calculate the stromal and immune scores of tumor samples, which were downloaded from The Cancer Genome Atlas (TCGA). The differentially expressed genes (DEGs) in high and low stromal and immune scores were screened, followed by functional enrichment of overlapped DEGs. We then conducted a survival analysis to identify immune-related prognostic indicators and constructed protein-protein interaction (PPI) networks and ceRNA networks. Finally, chemical small-molecule–target interaction pairs associated with liver cancer were screened. Results: A total of 55,955 stromal-related DEGs and 1811 immune-related DEGs were obtained. The 1238 overlapped DEGs were enriched in 1457 biological process terms and 74 KEGG pathways. In addition, a total of 120 activated memory CD4 T cell-related genes and 309 CD8 T cell-related genes were identified. The survival analysis revealed that upregulated expression of T cell-related genes including EOMES, CST7, and CD5L indicated the favorable prognosis of liver cancer. EOMES was regulated by has-miR-23b-3p and has-miR-23b-3p was regulated by lncRNA AC104820.2 in the ceRNA network of activated memory CD4 T cell-related genes. In addition, EOMES was regulated by has-miR-23a-3p and has-miR-23a-3p was regulated by lncRNA AC000476.1 in the ceRNA network of CD8 T cells. Conclusion: T cell-related RNAs EOMES, CST7, CD5L, has-miR-23b-3p, and has-miR-23a-3p may be associated with the prognosis of liver cancer. And the molecular characteristics of these T cell-related genes were plotted. Highlights: A total of 309 CD8 T cell-related genes and 120 activated memory CD4 T cell-related genes were screened in liver cancer tumor samples. Forty-four chemical small-molecule–target interaction pairs associated with activated memory CD4 T cells and 276 pairs associated with CD8 T cells were screened. Upregulated expression of T cell-related genes including EOMES, CST7, and CD5L indicated the favorable prognosis of liver cancer. [ABSTRACT FROM AUTHOR]

The lack of high-throughput sequencing data makes the research progress of Heliocidaris crassispina slow. Therefore, we used PacBio single-molecule real-time sequencing to generate the first full-length transcriptome. Here, 31,181 isoforms were obtained, with an average length of 2383.20 and a N50 length of 2732 bp. Meanwhile, 764 alternative splicing (AS) events, 5098 long-noncoding RNAs (LncRNAs), 6978 simple sequence repeats (SSRs), and 950 hypothetical transcript factors (TFs) were identified. Moreover, five key innate immune pattern recognition receptors (PRRs), including toll-like receptor (TLR), NACHT domain and leucine-rich repeat (NLR), scavenger receptor cysteine-rich (SRCR), peptidoglycan recognition proteins (PGRP), and gram-negative binding proteins (GNBP), were searched in the transcriptome. In addition, 37 isoforms enriched in KEGG and GO immune systems were also detected. The study provid abundant data support for the current research on H. crassispina. • The first full-length transcriptome of sea urchin H. crassispina has been constructed. • A total of 31,181 isoforms with a N50 length of 2732 bp were obtained. • 764 AS events, 5098 LncRNAs, 6978 SSRs, and 950 TFs were identified in the transcriptome. • The multiplicity of key innate immune PRRs were identified in the study, including TLR, NLR, SRCR, PGRP, and GNBP. [ABSTRACT FROM AUTHOR]

The prognosis and immunotherapy response rates are unfavorable in patients with oral squamous cell carcinoma (OSCC). The tumor microenvironment is associated with tumor prognosis and progression, and the underlying mechanisms remain unclear. We obtained differentially expressed immune-related genes from OSCC mRNA data in The Cancer Genome Atlas (TCGA) database. Overall survival-related risk signature was constructed by univariate Cox regression analysis and LASSO Cox regression analysis. The prognostic performance was validated with receiver operating characteristic (ROC) analysis and Kaplan–Meier survival curves in the TCGA and Gene Expression Omnibus (GEO) datasets. The risk score was confirmed to be an independent prognostic factor and a nomogram was built to quantify the risk of outcome for each patient. Furthermore, a negative correlation was observed between the risk score and the infiltration rate of immune cells, as well as the expression of immunostimulatory and immunosuppressive molecules. Functional enrichment analysis between different risk score subtypes detected multiple immune-related biological processes, metabolic pathways, and cancer-related pathways. Thus, the immune-related gene signature can predict overall survival and contribute to the personalized management of OSCC patients. [ABSTRACT FROM AUTHOR]

Kawasaki disease (KD) is a systemic vasculitis that predominantly damages medium- and small-sized vessels, and mainly causes coronary artery lesions (CALs). The diagnostic criterion of KD mainly depends on clinical features, so children could be easily misdiagnosed and could suffer from CALs. Through analysis, a total of 14 immune-related DEGs were obtained, of which IL1B , ADM , PDGFC , and TGFA were identified as diagnostic markers of KD. Compared with the non-KD group, KD patients contained a higher proportion of naive B cells, activated memory CD4 T cells, gamma delta T cells, and neutrophils, while the proportions of memory B cells, CD8 T cells, activated memory CD4 T cells, and activated NK cells were relatively lower. In conclusion, immune-related genes can be used as diagnostic markers of KD, and the difference in immune cells between KD and non-KD might provide new insight into understanding the pathogenesis of KD. [ABSTRACT FROM AUTHOR]

The polysaccharide-fucoidan plays a fundamental role in shrimp aquaculture by being used as a natural immunostimulant and dietary supplementation. In this study, Cystoseira trinodis fucoidan (FCT) was extracted, its structure was determined using FT-IR analysis, and its effect of fucoidan on growth performance and WSSV resistance in Litopenaeus vannamei was evaluated. Four experimental diets, including the control (without FCT), 0.1, 0.2, and 0.4% FCT, were formulated and fed to shrimps for 60 days. Next, they were exposed to WSSV, and their mortality rate was noted daily up to 20 days. The results of the growth experiment demonstrated a significant increase in the final weight, WG, and SGR, and a lower FCR in treatments than in control. During the challenge trial, 100% mortality was recorded in control within ten days. However, FCT-treated shrimps indicated a mortality increase from 43.33 to 75.00% during 20 days of infection. The immunological and biochemical parameters and the expression of immune-related genes in individuals fed the FCT-incorporated diet considerably (p < 0.01) improved over control before the challenge (0 days) and after the challenge on days 5, 10, 15, and 20. It was concluded that L. vannamei fed on diets supplemented with all concentrations of FCT (especially 0.4%) had improved the growth, immunological and biochemical parameters, and the expression of immune-related genes, as well as enhancing the resistance against WSSV. • This is the first study to reveal the impact of fucoidan extracted from brown seaweed, Cystoseira trinodis on the immunological and biochemical parameters and the expression of immune-related genes in Litopenaeus vannamei. • High levels of fucoidan in feed can significantly improve the growth performance, immunological and biochemical responses and immune-related gene expression in white leg shrimp. • Application of this dietary supplementation evidently enhanced disease resistance against WSSV in treated shrimps [ABSTRACT FROM AUTHOR]

A 2-week research was carried out to assess water salinity (WS) effects including 0, 15, 35, and 50‰ on osmoregulatory mechanisms and stress indices in Asian sea bass (34.4 g) juveniles. Except for fish reared at 50‰, in the other treatments, it gradually decreased to the prescribed WS during a 10-day period (− 5‰ a day). After a 10-day acclimation period, fish were reared at the prescribed WS for 2 weeks. Fish reared at 15 and 35‰ had higher chloride cell (CC) counts in the interlamellar region. The number of CC in the interlamellar region elevated with increment of WS up to 35‰, but they were pronouncedly reduced in 50‰ group. The diameter of CC in the interlamellar region was not affected by WS. The smallest nucleus diameter of CC in the interlamellar region was observed in fish reared at 15‰ (P < 0.05). The largest and the smallest amounts of serum aspartate aminotransferase were observed in fish reared at freshwater and 15‰, respectively. Fish reared at 35‰ had the highest serum sodium and potassium contents. Serum chloride content and total osmolality increased with increment of WS (P < 0.05). Serum cortisol and glucose contents gradually increased with elevation of WS up to 35‰; then, their contents remarkably decreased. The relative expression of insulin like growth factor-1 in the liver of fish reared at 35‰ was strikingly higher than that in the other groups. The relative expression of HSP70 gene in fresh water group was pronouncedly elevated compared to other treatments. The relative expression of interleukin-1β in 15 and 35‰ groups was higher than that in the other groups; however, the relative expression of lysozyme gene in the liver of fish reared at fresh water was pronouncedly lower than that in the other treatments. The results of this study suggested rearing L. calcarifer at 15‰ closer to the isosmotic point and better provide its welfare. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is one of the malignant tumors with high morbidity and mortality worldwide. Immunotherapy has emerged as an increasingly important cancer treatment modality. However, the potential relationship between immune genes and HCC still needs to be explored. The purpose of this study is to construct a new prognostic risk signature to predict the prognosis of HCC patients based on the expression of immune-related genes (IRGs) and explore its potential mechanism. Methods: We analyzed the gene expression data of 332 HCC patient samples and 46 adjacent normal tissues samples (Solid Tissue Normal including cirrhotic tissue) in The Cancer Genome Atlas (TCGA) database and clinical characteristics. We analyzed the gene expression data, identified differentially expressed IRGs in HCC tissues, filtered IRGs with prognostic value to construct an IRG signature, and classified patients into high and low gene expression groups based on the expression of IRGs in their tumor tissues. We also investigated the potential molecular mechanisms of IRGs through a bioinformatics approach using Protein-Protein Interaction (PPI) network, Kyoto Encyclopedia of Genes and Genomes (KEGG) database analysis and Gene Ontology (GO) database analysis. Differentially expressed IRGs associated with significant clinical outcomes (SIRGs) were identified by univariate Cox regression analysis. An immune-related risk score model (IRRSM) was established based on Lasso Cox regression analysis and multivariate Cox regression analysis. Based on the IRRSM, the immune score of the patients was calculated, and the patients were divided into high-risk and low-risk patients according to the median score, and the differences in survival between the two groups were compared. Then, the correlation analysis between the IRRSM and clinical characteristics was performed, and the IRRSM was validated using the International Cancer Genome Consortium (ICGC) database. Results: The IRRSM was eventually constructed and confirmed to be an independent prognostic model for HCC patients. The IRRSM was shown to be positively correlated with the infiltration of four types of immune cells. Conclusion: Our results showed that some SIRGs have potential value for predicting the prognosis and clinical outcomes of HCC patients. IRGs affect the prognosis of HCC patients by regulating the tumor immune microenvironment (TIME). This study provides a new insight for immune research and treatment strategies in HCC patients. [ABSTRACT FROM AUTHOR]

Objective: Septic shock is the severe complication of sepsis, with a high mortality. The inflammatory response regulates the immune status and mediates the progression of septic shock. In this study, we aim to identify the key immune-related genes (IRGs) of septic shock and explore their potential mechanism. Methods: Gene expression profiles of septic shock blood samples and normal whole blood samples were retrieved from the Gene Expression Omnibus (GEO) and Genotype-Tissue Expression Portal (GTEx). The differential expression genes (DEGs) and septic shock-specific immune-related genes (SSSIRGs) were evaluated and identified, along with the immune components by "cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT, version x)" algorithm. Additionally, in order to explore the key regulatory network, the relationship among SSSIRGs, upstream transcription factors (TFs), and downstream signaling pathways were also identified by Gene Set Variation Analysis (GSVA) and co-expression analysis. Moreover, the Connectivity Map (CMap) analysis was applied to find bioactive small molecules against the members of regulation network while Chromatin Immunoprecipitation sequencing (ChIP-seq) and Assay for Targeting Accessible-Chromatin with high-throughput sequencing (ATAC-seq) data were used to validate the regulation mechanism of the network. Results: A total of 14,843 DEGs were found between 63 septic shock blood samples and 337 normal whole blood samples. Then, we identified septic shock-specific 839 IRGs as the intersection of DEGs and IRGs. Moreover, we uncovered the regulatory networks based on co-expression analysis and found 28 co-expression interaction pairs. In the regulation network, protein phosphatase 3, catalytic subunit, alpha isozyme (PPP3CA) may regulate late estrogen response, glycolysis and TNFα signaling via NFκB and HLA; Kirsten rat sarcoma viral oncogene homolog (KRAS) may be related to late estrogen response and HLA; and Toll-like receptor 8 (TLR8) may be associated with TNFα signaling via NFκB. And the regulation mechanisms between TFs and IRGs (TLR8, PPP3CA, and KRAS) were validated by ChIP-seq and ATAC-seq. Conclusion: Our data identify three SSSIRGs (TLR8, PPP3CA, and KRAS) as candidate therapeutic targets for septic shock and provide constructed regulatory networks in septic shock to explore its potential mechanism. [ABSTRACT FROM AUTHOR]

Abstract Background The incidence of stage I and stage II lung adenocarcinoma (LUAD) is likely to increase with the introduction of annual screening programs for high-risk individuals. We aimed to identify a reliable prognostic signature with immune-related genes that can predict prognosis and help making individualized management for patients with early-stage LUAD. Methods The public LUAD cohorts were obtained from the large-scale databases including 4 microarray data sets from the Gene Expression Omnibus (GEO) and 1 RNA-seq data set from The Cancer Genome Atlas (TCGA) LUAD cohort. Only early-stage patients with clinical information were included. Cox proportional hazards regression model was performed to identify the candidate prognostic genes in GSE30219, GSE31210 and GSE50081 (training set). The prognostic signature was developed using the overlapped prognostic genes based on a risk score method. Kaplan–Meier curve with log-rank test and time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic value and performance of this signature, respectively. Furthermore, the robustness of this prognostic signature was further validated in TCGA-LUAD and GSE72094 cohorts. Results A prognostic immune signature consisting of 21 immune-related genes was constructed using the training set. The prognostic signature significantly stratified patients into high- and low-risk groups in terms of overall survival (OS) in training data set, including GSE30219 (HR = 4.31, 95% CI 2.29–8.11; P = 6.16E−06), GSE31210 (HR = 11.91, 95% CI 4.15–34.19; P = 4.10E−06), GSE50081 (HR = 3.63, 95% CI 1.90–6.95; P = 9.95E−05), the combined data set (HR = 3.15, 95% CI 1.98–5.02; P = 1.26E−06) and the validation data set, including TCGA-LUAD (HR = 2.16, 95% CI 1.49–3.13; P = 4.54E−05) and GSE72094 (HR = 2.95, 95% CI 1.86–4.70; P = 4.79E−06). Multivariate cox regression analysis demonstrated that the 21-gene signature could serve as an independent prognostic factor for OS after adjusting for other clinical factors. ROC curves revealed that the immune signature achieved good performance in predicting OS for early-stage LUAD. Several biological processes, including regulation of immune effector process, were enriched in the immune signature. Moreover, the combination of the signature with tumor stage showed more precise classification for prognosis prediction and treatment design. Conclusions Our study proposed a robust immune-related prognostic signature for estimating overall survival in early-stage LUAD, which may be contributed to make more accurate survival risk stratification and individualized clinical management for patients with early-stage LUAD.

Abstract Background Immune-related genes (IRGs) have been confirmed to have an important role in tumorigenesis and tumor microenvironment formation. Nevertheless, a systematic analysis of IRGs and their clinical significance in soft tissue sarcoma (STS) patients is lacking. Methods Gene expression files from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) were used to select differentially expressed genes (DEGs). Differentially expressed immune-related genes (DEIRGs) were determined by matching the DEG and ImmPort gene sets, which were evaluated by functional enrichment analysis. Unsupervised clustering of the identified DEIRGs was conducted, and associations with prognosis, the tumor microenvironment (TME), immune checkpoints, and immune cells were analyzed simultaneously. Two prognostic signatures, one for overall survival (OS) and one for progression free survival (PFS), were established and validated in an independent set. Finally, two transcription factor (TF)-IRG regulatory networks were constructed, and a crucial regulatory axis was validated. Results In total, 364 DEIRGs and four clusters were identified. OS, TME scores, five immune checkpoints, and 12 types of immune cells were found to be significantly different among the four clusters. The two prognostic signatures incorporating 20 DEIRGs showed favorable discrimination and were successfully validated. Two nomograms combining signature and clinical variables were generated. The C-indexes were 0.879 (95%CI 0.832 ~ 0.926) and 0.825 (95%CI 0.776 ~ 0.874) for the OS and PFS signatures, respectively. Finally, TF-IRG regulatory networks were established, and the MYH11-ADM regulatory axis was verified in three independent datasets. Conclusion This comprehensive analysis of the IRG landscape in soft tissue sarcoma revealed novel IRGs related to carcinogenesis and the immune microenvironment. These findings have implications for prognosis and therapeutic responses, which reveal novel potential prognostic biomarkers, promote precision medicine, and provide potential novel targets for immunotherapy.

Abstract Background Bladder cancer (BCa) is one of the important tumors that have been proven to be treatable with immunotherapy. This study aims to identify and validate a molecular prognostic index of BCa based on immunogenomic landscape analysis. Methods The cancer genome atlas (TCGA) database and immunology database and analysis portal (ImmPort) database were used to identified differentially expressed immune-related genes (IRGs). Prognostic IRGs were screened and protein–protein interaction (PPI) network was constructed. Multivariate Cox analysis was performed to develop a molecular prognostic index of BCa. Internal and external validation were then performed in TCGA cohort and GEO cohort, respectively. Besides, we also explore the relationship between this index and clinical characteristics, immune cell infiltration and tumor microenvironment. Results A total of 61 prognostic IRGs were identified and a molecular prognostic index was developed. The top four hub genes included MMP9, IGF1, CXCL12 and PGF. The difference in overall survival between high-risk group and low-risk group was statistically significant. The area under curve of the receiver operating characteristic (ROC) curve was 0.757, suggesting the potential for this index. Besides, Internal validation using TCGA cohort and external validation using GEO cohort indicated that this index was of great performance in predicting outcome. T cells CD8, T cells CD4 memory activated, T cells follicular helper, macrophages M0, macrophages M2 and neutrophils were significantly associated with prognosis of BCa patients. Female, high grade, stage III&IV, N1-3 and T3-4 were associated significantly with higher risk score compared with male, low grade, stage I&II, N0 and T1-2, respectively. High risk score had a positive association with higher stromal score and ESTIMATE score while high risk score had a negative association with tumor purity. Conclusions This study identified several prognostic immune-related genes of clinical value. Besides, we developed and validated a molecular index based on immunogenomic landscape analysis, which performed well in predicting prognosis of BCa. Further researches are needed to verify the effectiveness of this index and these vital genes.