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Background: Breast cancer has plagued women for many years and caused many deaths around the world. Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis, and nomogram were also conducted in this study. Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts. Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method and may improve the treatment stratification of breast cancer patients in the future work.

Idiopathic pulmonary fibrosis (IPF) is a lung disease involving chronic progressive fibrosis, with unclear pathogenesis. In recent years, people have paid increasing attention to the role of immune mechanism. In this study, bioinformatics analysis was used to determine the potential immune-related biomarkers for the diagnosis of IPF, and further analyze the role of immune cell infiltration in the pathogenesis of IPF.The IPF data set (GSE150910) was downloaded from the Gene Expression Omnibus (GEO) database. We used R software to screen differential immune-related genes (IRGs). Least absolute shrinkage and selection operator (LASSO) regression, random forest algorithm, and support vector machine (SVM) were used to screen and determine IPF IRGs to be diagnostic biomarkers. The GSE32537 and GSE10667 data sets were combined into 1 data set to verify the diagnostic efficacy of biomarkers. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) was used to evaluate the infiltration of immune cells in IPF tissues, and analyze the relationship between diagnostic markers and immune cell infiltration. Meanwhile, clinical specimens were used to verify the diagnostic efficacy of biomarkers and their relationship with immune cell infiltration.In this study, 408 participants were involved in the screening to find that PLXNA4 and SLIT2 can be used as diagnostic biomarkers of IPF, and the results were verified by clinical samples. Immune cell infiltration analysis found that regulatory T cells (Tregs), memory B cells, plasma cells, and eosinophils might be involved in the process of IPF. In addition, Tregs were most closely related to PLXNA4 and SLIT2. In clinical samples, forkhead box p3 (FOXP3), a specific marker of Tregs, was positively correlated with PLXNA4 and negatively correlated with SLIT2, which is consistent with the results of bioinformatics analysis.The genes PLXNA4 and SLIT2 can be used as diagnostic markers of IPF, and immune cell infiltration plays an important role in the occurrence and development of IPF.

Aims: The aim of this study was to identify the immune- and locus-associated genes in pancreatic ductal adenocarcinoma and evaluate their value in prognosis. Methods: The pancreatic ductal adenocarcinoma stromal and immune scores were calculated with the estimation of stromal and immune cells in malignant tumor tissues using expression data algorithm. The authors screened the differentially expressed genes to generate immune- and stromal-related differentially expressed genes. Next, the authors conducted weighted correlation network analysis to find the gene sets related to tumor sites. Results: IL1R1 and LAMA2 were identified as the site- and immune-related genes in pancreatic ductal adenocarcinoma, and their high expression in pancreatic head cancer exhibited high immune scores and predicted unfavorable prognosis. Conclusion: The authors identified IL1R1 and LAMA2 as immune- and locus-associated genes, and their high expression predicted a poor prognosis.

Background: Bladder cancer (BLCA) is the major tumor of the urinary system, and immune-related genes (IRGs) contribute significantly to its initiation and prognosis. Results: A total of 51 prognostic IRGs significantly associated with overall survival were identified. Functional enrichment analysis revealed that these genes were actively involved in tumor-related functions and pathways. Using multivariate Cox regression analysis, we detected 11 optimal IRGs (ADIPOQ, PPY, NAMPT, TAP1, AHNAK, OLR1, PDGFRA, IL34, MMP9, RAC3, and SH3BP2). We validated the prognostic value of this signature in two validation cohorts: {"type":"entrez-geo","attrs":{"text":"GSE13507","term_id":"13507"}}GSE13507 (n = 165) and {"type":"entrez-geo","attrs":{"text":"GSE32894","term_id":"32894"}}GSE32894 (n = 224). Furthermore, we performed a western blot and found that the expression of these IRGs matched their mRNA expression in TCGA. Moreover, correlations between risk score and immune-cell infiltration indicated that the prognostic signature reflected infiltration by several types of immune cells. Conclusion: We identified and validated an 11-IRG-based risk signature that may be a reliable tool to evaluate the prognosis of BLCA patients and help to devise individualized immunotherapies. Methods: Bioinformatics analysis was performed using TCGA and ImmPort databases. Cox regression was used to identify prognostic signatures. Two external GEO cohorts and western blotting of samples were performed to validate the IRG signature.

Background Immune-related genes (IRGs) are highly relevant to the progression and prognosis of esophageal squamous cell carcinoma (ESCC). A prognostic signature could be reliable in stratifying ESCC patients according to the risk score, which may help manage systematic treatments. In this study, a systematic and reliable immune signature was developed to estimate the prognostic stratification in ESCC. Methods Ribonucleic acid (RNA) expression data of 79 ESCC samples from the Cancer Genome Atlas (TCGA) database and 269 normal esophageal mucosal samples from the Genotype-Tissue Expression (GTEx) project database were downloaded from the University of California, Santa Cruz (UCSC) website to form a TCGA-GTEx dataset. First, we screened differentially expressed genes (DEGs) and then filtered IRGs based on the Immunology Database and Analysis Portal (ImmPort) database to obtain immune-related DEGs (IRDEGs). Next, a novel prognostic signature based on IRDEGs was developed using multivariable Cox analysis. Immune infiltration status was evaluated via single-sample gene set enrichment analysis (ssGSEA). ESCC tissues were grouped into three clusters in terms of immune infiltration (Immunity-L, Immunity-M, and Immunity-H) by applying an unsupervised hierarchical clustering algorithm. Finally, the samples were divided into high- and low-risk groups using the median of the risk score scores for GSEA pathway enrichment analysis in the three clusters. Results The prognostic signature based on IRDEGs (FCER1G, ISG20, and EGFR) performed moderately in prognostic predictions, with a concordance index (C-index) value of 0.73 [95% (confidence interval) CI: 0.63–0.84, P=2.02E-05] and an area under the curve (AUC) value of 0.817. The xenobiotic metabolism pathway was significantly enriched and up-regulated both in the high-risk group of the immunity-M and immunity-H clusters. Conclusions The novel immune-related prognostic signature we constructed has a good prognostic, predictive ability and can be used as an independent prognostic indicator. Our study provides clinicians with a quantitative tool to predict the probability of individual survival time and helps clinicians select targets for immunotherapies and individualized treatment strategies for ESCC patients.

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

Ovarian cancer (OV) is the most common gynaecological cancer worldwide. Immunotherapy has recently been proven to be an effective treatment strategy. The work here attempts to produce a prognostic immune‐related gene pair (IRGP) signature to estimate OV patient survival. The Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) databases provided the genetic expression profiles and clinical data of OV patients. Based on the InnateDB database and the least absolute shrinkage and selection operator (LASSO) regression model, we first identified a 17‐IRGP signature associated with survival. The average area under the curve (AUC) values of the training, validation, and all TCGA sets were 0.869, 0.712, and 0.778, respectively. The 17‐IRGP signature noticeably split patients into high‐ and low‐risk groups with different prognostic outcomes. As suggested by a functional study, some biological pathways, including the Toll‐like receptor and chemokine signalling pathways, were significantly negatively correlated with risk scores; however, pathways such as the p53 and apoptosis signalling pathways had a positive correlation. Moreover, tumour stage III, IV, grade G1/G2, and G3/G4 samples had significant differences in risk scores. In conclusion, an effective 17‐IRGP signature was produced to predict prognostic outcomes in OV, providing new insights into immunological biomarkers.

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

Background Immunotherapy has made great strides in cancer treatment. Endometrial carcinoma (EC) has been 1 of the most common tumors among women. This study aimed to screen immune-related prognosis biomarkers for EC. Methods The transcriptome profiling and clinical data of EC were downloaded from The Carcinoma Genome Atlas (TCGA) public database, and differentially expressed genes (DEGs) were obtained through the limma package in R software. An immune-related genes (IRGs) list was collected from the ImmPort database. We constructed a free-scale gene co-expression network via weighted gene co-expression network analysis (WGCNA). Then, the intersection genes of the module genes which significantly related to EC, along with IRGs and DEGs were screened as the candidate genes for further analysis. We identified the hub gene via Venn analysis of the protein-protein interaction (PPI) network genes and the prognostic genes, and verified expression of the hub gene through Human Protein Atlas (HPA) and Gene Expression Omnibus (GEO) databases which provided the GSE17025 dataset. Furthermore, we used the CIBERSORT deconvolution algorithm to explore tumor immune cells infiltration in EC, and investigated correlations between the hub gene and immune cells. Results The differential expression analysis demonstrated that there were 900 up-regulated genes and 1,008 down-regulated genes in TCGA-UCEC (Uterine Corpus Endometrial Carcinoma) cohort. There were 74 candidate intersection genes in blue module genes, IRGs, and DEGs. Finally, angiopoietin 1 (ANGPT1) was identified as the hub gene in EC. Low expression of ANGPT1 was associated with better overall survival (OS) in EC patients. The expression of ANGPT1 was negatively correlated with regulatory T cells (Tregs), but positively correlated with resting memory cluster of differentiation 4 (CD4) T cells, activated dendritic cells (DCs), activated natural killer (NK) cells, and activated memory CD4 T cells (P

Background Although the outcome of breast cancer patients has been improved by advances in early detection, diagnosis and treatment. Due to the heterogeneity of the disease, prognostic assessment still faces challenges. The accumulated data indicate that there is a clear correlation between the tumor immune microenvironment and clinical outcomes. Objective Construct immune-related gene pairs to evaluate the prognosis of breast cancer and patient survival rate. Methods From the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, the Gene expression profiles and clinical data of breast cancer samples were downloaded. TCGA cohort were further divided into a training set (n = 764) and internal validation sets (n = 325). The GEO cohort was analyzed as an external validation cohort (n = 327). In the training set, differently expressed immune-relevant genes (IRGs) were screened firstly, and they were used to construct immune-relevant gene pairs (IRGPs). Then, the prognostic IRGPs were identified via univariate Cox regression analysis. Finally, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to constituted the IRGP prognostic signature. Kaplan-Meier (KM) survival curves, receiver operating characteristic (ROC) curve analysis, univariate and multivariate Cox regression analysis were used to estimate the predictive value of the IRGP prognostic signature. And the IRGP prognostic signature was validated in the internal validation cohort and external validation cohort. We used gene set enrichment analysis (GSEA) to elucidate the biological functions of the IRGP prognostic signature. Results A total of 474 differently expressed IRGs and 2942 prognostic IRGPs were identified. Finally, we generated a IRGP prognostic signature consisting of 33 IRGPs. Subsequently, the 33 IRGPs grouped BRCA patients into high- and low-risk groups. Kaplan-Meier curves shown a significantly different overall survival in risk groups. Time-dependent ROC curves indicated that the IRGP prognostic signature possessed a high specificity and sensitivity in all the sets. Univariate and multivariate Cox regression analysis showed a statistical significance for the prognostic value of IRGP prognostic signature and the IRGP prognostic signature was a strong independent risk factor. The functional enrichment analysis indicated that low IRGP value was correlated with biological processes related to immune. Immune cell infiltration analysis indicated a significant difference in percentage of M2 macrophages between high- and low-risk groups. Conclusion The 33-IRGPs prognostic signature was developed to provide new insights for the identification of high-risk breast cancer and the evaluation of the possibility of immunotherapy in personalized breast cancer treatment.

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

Immune-related genes (IRGs) are responsible for osteosarcoma (OS) initiation and development. We aimed to develop an optimal IRGs-based signature to assess of OS prognosis. Sample gene expression profiles and clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases. IRGs were obtained from the ImmPort database. R software was used to screen differentially expressed IRGs (DEIRGs) and functional correlation analysis. DEIRGs were analyzed by univariate Cox regression and iterative LASSO Cox regression analysis to develop an optimal prognostic signature, and the signature was further verified by independent cohort (GSE39055) and clinical correlation analysis. The analyses yielded 604 DEIRGs and 10 hub IRGs. A prognostic signature consisting of 13 IRGs was constructed, which strikingly correlated with OS overall survival and distant metastasis (p

Background Laryngeal squamous cell carcinoma (LSCC) is a very common cancer in head and neck. A large amount of evidence indicated that immune genes take a non-negligible part in the occurrence and development of LSCC. We aimed to evaluate the prognostic value of the immune-related genes (IRGs) in LSCC. Methods We analyzed the expression profiles of IRGs and clinical information of patients from The Cancer Genome Atlas (TCGA) dataset, including one hundred and fourteen LSCC and eleven non-tumor tissues. The uncover molecular mechanisms of these survival-associated IRGs (SAIRGs) were explored by the bioinformatics analyses. A novel prognostic model based on IRGs was developed through multivariate Cox regression. Results Twenty-seven differentially expressed SAIRGs in LSCC patients. Copy number alterations, protein-protein interaction (PPI) network, transcription factors (TFs) regulate network were used to identify molecular characteristics of these IRGs. We established an IRGs (TLR2, XCL2, CYSLTR2 and FCGR3B) based prognostic model (IRGPM), which had good performance to predict the prognosis of LSCC (AUC =0.820, P

The immune microenvironment plays a vital role in the progression of hepatocellular carcinoma (HCC). Thousands of immune-related genes (IRGs) have been identified, but their effects on HCC are not fully understood. In this study, we identified the differentially expressed IRGs and analyzed their functions in HCC in a systematic way. Furthermore, we constructed a diagnostic and a prognostic model using multiple statistical methods, and both models had good distinguishing performance, which we verified in several independent datasets. This diagnostic model was also adaptable to proteomic data. The combination of a prognostic risk model and classic clinical staging can effectively distinguish patients in high- and low-risk groups. Furthermore, we systematically explore the differences in the immune microenvironment between the high-risk group and the low-risk group to help clinical decision-making. In summary, we systematically analyzed immune-related genes in HCC, explored their functions, constructed a diagnostic and a prognostic model and investigated potential therapeutic schedules in high-risk patients. The model performance was verified in multiple databases. Our findings can provide directions for future research.

The tumor microenvironment is closely related to the progression and immune escape of tumor cells. Tumor-infiltrating immune cells (TIICs) and immune-related genes (IRGs) are indispensable components of the tumor microenvironment and have been demonstrated to be highly valuable in determining the prognosis of multiple cancers. To elucidate the prognostic value of TIICs and IRGs in gastric cancer, we conducted a comprehensive analysis focusing on the abundances of 22 types of TIICs and differentially expressed IRGs based on a dataset from The Cancer Genome Atlas (TCGA). The results showed that great composition differences in TIICs and immune cell subfractions were associated with survival outcomes in different stages. Additionally, 29 hub genes were characterized from 345 differentially expressed IRGs and found to be significantly associated with survival outcomes. Then, an independent prognostic indicator based on ten IRGs was successfully constructed after multivariate adjustment for some clinical parameters. Further validation revealed that these hub IRGs could reflect the infiltration levels of immune cells. Thus, our results confirmed the clinical significance of TIICs and IRGs in gastric cancer and may establish a foundation for further exploring immune cell and gene targets for personalized treatment.

Colorectal adenocarcinoma (COAD) is the most common type of colon cancer, and its occurrence is intimately associated with immune-related gene (IRG) expression. In this research, the IRG expression was analyzed and 484 were found significantly different between COAD and normal tissue. To construct a COAD patient IRGs-prognosis model, 11 of the IRGs were selected by multivariate Cox analysis. IRGs in the model were closely related to tumor invasion depth, lymphoid involvement, distant metastasis, stage, and age. This work lays the foundation for future scientific research and clinical work related to COAD.

BACKGROUND Gastric cancer (GC) is the most commonly diagnosed malignancy worldwide. Increasing evidence suggests that it is necessary to further explore genetic and immunological characteristics of GC. AIM To construct an immune-related gene (IRG) signature for accurately predicting the prognosis of patients with GC. METHODS Differentially expressed genes (DEGs) between 375 gastric cancer tissues and 32 normal adjacent tissues were obtained from The Cancer Genome Atlas (TCGA) GDC data portal. Then, differentially expressed IRGs from the ImmPort database were identified for GC. Cox univariate survival analysis was used to screen survival-related IRGs. Differentially expressed survival-related IRGs were considered as hub IRGs. Genetic mutations of hub IRGs were analyzed. Then, hub IRGs were selected to conduct a prognostic signature. Receiver operating characteristic (ROC) curve analysis was used to evaluate the prognostic performance of the signature. The correlation of the signature with clinical features and tumor-infiltrating immune cells was analyzed. RESULTS Among all DEGs, 70 hub IRGs were obtained for GC. The deletions and amplifications were the two most common types of genetic mutations of hub IRGs. A prognostic signature was identified, consisting of ten hub IRGs (including S100A12, DEFB126, KAL1, APOH, CGB5, GRP, GLP2R, LGR6, PTGER3, and CTLA4). This prognostic signature could accurately distinguish patients into high- and low- risk groups, and overall survival analysis showed that high risk patients had shortened survival time than low risk patients (P < 0.0001). The area under curve of the ROC of the signature was 0.761, suggesting that the prognostic signature had a high sensitivity and accuracy. Multivariate regression analysis demonstrated that the prognostic signature could become an independent prognostic predictor for GC after adjustment for other clinical features. Furthermore, we found that the prognostic signature was significantly correlated with macrophage infiltration. CONCLUSION Our study proposed an immune-related prognostic signature for GC, which could help develop treatment strategies for patients with GC in the future.

Melanoma is the most invasive type of skin cancer, in which the immune system plays a vital role. In this study, we aimed to establish a prognostic prediction nomogram for melanoma patients that incorporates immune-related genes (IRGs). Ninety-seven differentially expressed IRGs between melanoma and normal skin were screened using gene expression omnibus database (GEO). Among these IRGs, a two-gene signature consisting of CCL8 and DEFB1 was found to be closely associated with patient prognosis using the cancer genome atlas (TCGA) database. Survival analysis verified that the IRGs score based on the signature gene expressions efficiently distinguished between high- and low-risk patients, and was identified to be an independent prognostic factor. A nomogram integrating the IRGs score, age and TNM stage was established to predict individual prognosis for melanoma. The prognostic performance was validated by the TCGA/GEO-based concordance indices and calibration plots. The area under the curve demonstrated that the nomogram was superior than the conventional staging system, which was confirmed by the decision curve analysis. Overall, we developed and validated a nomogram for prognosis prediction in melanoma based on IRGs signatures and clinical parameters, which could be valuable for decision making in the clinic.

Heterogeneity in patients with colorectal cancer (CRC) leads to different strategies in clinical decision making. Identifying distinctive subgroups in patients contributes to develop more individualized treatments. This study constructed a novel prediction model for the prognosis of CRC patients based on the value of risk score combining the expression status of immune-related genes and coefficients. In this study, we built an interactive network of prognosis-related immune genes and transcription factors and adopted several methods to verify the accuracy of model. Moreover, we assessed the correlation between risk score and immune infiltration. The results suggested that the model was well fit and the risk score could be an independent predictive factor for CRC patients. This model has high application value in the clinic.

Renal clear cell carcinoma (ccRCC) is the most common type of renal cell carcinoma, which has strong immunogenicity. A comprehensive study of the role of immune-related genes (IRGs) in ccRCC is of great significance in finding ccRCC treatment targets and improving patient prognosis. In this study, we comprehensively analyzed the expression of IRGs in ccRCC based on The Cancer Genome Atlas datasets. The mechanism of differentially expressed IRGs in ccRCC was analyzed by bioinformatics. In addition, Cox regression analysis was used to screen prognostic related IRGs from differentially expressed IRGs. We also identified a four IRGs signature consisting of four IRGs (CXCL2, SEMA3G, PDGFD, and UCN) through lasso regression and multivariate Cox regression analysis. Further analysis results showed that the four IRGs signature could effectively predict the prognosis of patients with ccRCC, and its predictive power is independent of other clinical factors. In addition, the correlation analysis of immune cell infiltration showed that this four IRGs signature could effectively reflect the level of immune cell infiltration of ccRCC. We also found that the expression of immune checkpoint genes CTLA-4, LAG3, and PD-1 in the high-risk group was higher than that in the low-risk group. Our research revealed the role of IRGs in ccRCC, and developed a four IRGs signature that could be used to evaluate the prognosis of patients with ccRCC, which will help to develop personalized treatment strategies for patients with ccRCC and improve their prognosis. In addition, these four IRGs may be effective therapeutic targets for ccRCC.

The study mainly aimed at the effects of dietary Senecio scandens buch-ham extract (SSBE) on the growth performance, body composition, plasma biochemical index, intestinal and liver histology and the expression of antioxidant, apoptosis and inflammatory related genes in hybrid grouper (Epinephelus lanceolatus♂ × Epinephelus fuscoguttatus♀). Basal diets supplemented with SSBE (10:1) 0%, 0.05%, 0.1%, 0.2% and 0.4% were fed hybrid grouper for 8 weeks. The results showed that WGR and SGR were significantly increased in the week 2 and week 4 in Diet 0.05% group (P 0.05). The total protein, globulin and albumin significantly increased whereas alanine aminotransferase, triglyceride and alkaline phosphate in the plasma were significantly decreased in Diet 0.1% group (P 0.05). The villi length, width, muscle thickness and the cross-sectional area of intestine were improved in Diet 0.05% and Diet 0.1% group. The expression levels of PPAR-α and CPT-1 in the liver of hybrid grouper were significantly increased following the supplementation of SSBE (P 0.05). The expression levels of antioxidant related genes (CAT, GPX, GR and Keap1) and anti-inflammatory factor (IL-10) in liver, head kidney and spleen of hybrid grouper decreased significantly (P 0.05). In addition, diets supplemented with 0.05%-0.1% SSBE had a good liver-protecting effect, but it would have a detrimental effect on hepatocytes when the content exceeds 0.2%. The above results indicated that the suitable additive amount of SSBE in hybrid grouper feed was 0.05%-0.1%.

Objective Hepatocellular carcinoma (HCC) has become the second most common tumor type that contributes to cancer‐related death worldwide. The study aimed to establish a robust immune‐related gene pair (IRGP) signature for predicting the prognosis of HCC patients. Methods Two RNA‐seq datasets (The Cancer Genome Atlas Program and International Cancer Genome Consortium) and one microarray dataset (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520) were included in this study. We used a series of immune‐related genes from the ImmPort database to construct gene pairs. Lasso penalized Cox proportional hazards regression was employed to develop the best prognostic signature. We assigned patients into two groups with low immune risk and high immune risk. Then, the prognostic ability of the signature was evaluated by a log‐rank test and a Cox proportional hazards regression model. Results After 1000 iterations, the 33‐immune gene pair model obtained the highest frequency. As a result, we chose the 33 immune gene pairs to establish the immune‐related prognostic signature. As we expected, the immune‐related signature accurately predicted the prognosis of HCC patients, and high‐risk groups showed poor prognosis in the training datasets and testing datasets as well as in the validation datasets. Furthermore, the immune‐related gene pair (IRGP) signature also showed higher predictive accuracy than three existing prognostic signatures. Conclusion Our prognostic signature, which reflects the link between the immune microenvironment and HCC patient outcome, is promising for prognosis prediction in HCC., We used a series of immune‐related genes to construct an immune‐related gene pair. Then the lasso‐penalized Cox proportional hazards regression was applied to develop the best prognosis signature. Finally, we validated our immune‐related gene pair signature.

The effects of parasites, pathogens and agrochemical exposure combined with the cold temperatures of winter in temperate-climate regions contribute to the immunosuppression of honey bees. Recently,...

BACKGROUND Hepatocellular carcinoma (HCC) is a common cancer with a poor prognosis. Previous studies revealed that the tumor microenvironment (TME) plays an important role in HCC progression, recurrence, and metastasis, leading to poor prognosis. However, the effects of genes involved in TME on the prognosis of HCC patients remain unclear. Here, we investigated the HCC microenvironment to identify prognostic genes for HCC. AIM To identify a robust gene signature associated with the HCC microenvironment to improve prognosis prediction of HCC. METHODS We computed the immune/stromal scores of HCC patients obtained from The Cancer Genome Atlas based on the ESTIMATE algorithm. Additionally, a risk score model was established based on Differentially Expressed Genes (DEGs) between high‐ and low‐immune/stromal score patients. RESULTS The risk score model consisting of eight genes was constructed and validated in the HCC patients. The patients were divided into high- or low-risk groups. The genes (Disabled homolog 2, Musculin, C-X-C motif chemokine ligand 8, Galectin 3, B-cell-activating transcription factor, Killer cell lectin like receptor B1, Endoglin and adenomatosis polyposis coli tumor suppressor) involved in our risk score model were considered to be potential immunotherapy targets, and they may provide better performance in combination. Functional enrichment analysis showed that the immune response and T cell receptor signaling pathway represented the major function and pathway, respectively, related to the immune-related genes in the DEGs between high- and low-risk groups. The receiver operating characteristic (ROC) curve analysis confirmed the good potency of the risk score prognostic model. Moreover, we validated the risk score model using the International Cancer Genome Consortium and the Gene Expression Omnibus database. A nomogram was established to predict the overall survival of HCC patients. CONCLUSION The risk score model and the nomogram will benefit HCC patients through personalized immunotherapy.

The clinical heterogeneity of autism spectrum disorders majorly challenges their genetic study. Autism spectrum disorders symptoms occur in milder forms in the general population, as autistic-like traits, and share genetic factors with autism spectrum disorders. Here, we investigate the genetics of individual autistic-like traits to improve our understanding of autism spectrum disorders. We meta-analysed four population-based genome-wide association studies investigating four autistic-like traits – ‘attention-to-detail’, ‘imagination’, ‘rigidity’ and ‘social-skills’ ( n = 4600). Using autism spectrum disorder summary statistics from the Psychiatric Genomic Consortium ( N = 46,350), we applied polygenic risk score analyses to understand the genetic relationship between autism spectrum disorders and autistic-like traits. Using MAGMA, we performed gene-based and gene co-expression network analyses to delineate involved genes and pathways. We identified two novel genome-wide significant loci – rs6125844 and rs3731197 – associated with ‘attention-to-detail’. We demonstrated shared genetic aetiology between autism spectrum disorders and ‘rigidity’. Analysing top variants and genes, we demonstrated a role of the immune-related genes RNF114, CDKN2A, KAZN, SPATA2 and ZNF816A in autistic-like traits. Brain-based genetic expression analyses further linked autistic-like traits to genes involved in immune functioning, and neuronal and synaptic signalling. Overall, our findings highlight the potential of the autistic-like trait–based approach to address the challenges of genetic research in autism spectrum disorders. We provide novel insights showing a potential role of the immune system in specific autism spectrum disorder dimensions. Lay abstract Autism spectrum disorders are complex, with a strong genetic basis. Genetic research in autism spectrum disorders is limited by the fact that these disorders are largely heterogeneous so that patients are variable in their clinical presentations. To address this limitation, we investigated the genetics of individual dimensions of the autism spectrum disorder phenotypes, or autistic-like traits. These autistic-like traits are continuous variations in autistic behaviours that occur in the general population. Therefore, we meta-analysed data from four different population cohorts in which autistic-like traits were measured. We performed a set of genetic analyses to identify common variants for autistic-like traits, understand how these variants related to autism spectrum disorders, and how they contribute to neurobiological processes. Our results showed genetic associations with specific autistic-like traits and a link to the immune system. We offer an example of the potential to use a dimensional approach when dealing with heterogeneous, complex disorder like autism spectrum disorder. Decomposing the complex autism spectrum disorder phenotype in its core features can inform on the specific biology of these features which is likely to account to clinical variability in patients.

Background:To identify the immune-related genes of bladder cancer (BLCA) based on immunological characteristics and explore their correlation with the prognosis. Methods:We downloaded the gene and clinical data of BLCA from the Cancer Genome Atlas (TCGA) as the training group, and obtained immune-related genes from the Immport database. We downloaded GSE31684 and GSE39281 from the Gene Expression Omnibus (GEO) as the external validation group. R (version 4.0.5) and Perl were used to analyze all data. Result:Univariate Cox regression analysis and Lasso regression analysis revealed that 9 prognosis-related immunity genes (PIMGs) of differentially expressed immune genes (DEIGs) were significantly associated with the survival of BLCA patients (p < 0.01), of which 5 genes, including NPR2, PDGFRA, VIM, RBP1, RBP1 and TNC, increased the risk of the prognosis, while the rest, including CD3D, GNLY, LCK, and ZAP70, decreased the risk of the prognosis. Then, we used these genes to establish a prognostic model. We drew receiver operator characteristic (ROC) curves in the training group, and estimated the area under the curve (AUC) of 1-, 3- and 5-year survival for this model, which were 0.688, 0.719, and 0.706, respectively. The accuracy of the prognostic model was verified by the calibration chart. Combining clinical factors, we established a nomogram. The ROC curve in the external validation group showed that the nomogram had a good predictive ability for the survival rate, with a high accuracy, and the AUC values of 1-, 3-, and 5-year survival were 0.744, 0.770, and 0.782, respectively. The calibration chart indicated that the nomogram performed similarly with the ideal model. Conclusion:We had identified nine genes, including PDGFRA, VIM, RBP1, RBP1, TNC, CD3D, GNLY, LCK, and ZAP70, which played important roles in the occurrence and development of BLCA. The prognostic model based on these genes had good accuracy in predicting the OS of patients and might be promising candidates of therapeutic targets. This study may provide a new insight for the diagnosis, treatment and prognosis of BLCA from the perspective of immunology. However, further experimental studies are necessary to reveal the underlying mechanisms by which these genes mediate the progression of BLCA.

Glioblastoma (GBM) is the most common and aggressive primary tumor of the central nervous system with high recurrence and extremely poor prognosis. Multiple recent studies have indicated a pivotal correlation between GBM prognosis and immune-related risk signature. Nevertheless, the potential value of endothelial cells (ECs) immune-related genes (EIRGs) in prognosis, immune infiltration, and their correlation with therapeutic response to immunotherapy and TMZ chemotherapy remain obscure, especially in GBM. Here, we screened out 11 EIRGs after intersecting the identified 59 GBM ECs related prognostic genes and the identified 438 immune-related prognostic genes. A prognostic-related 6-EIRGs signature was established through univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and patients in the high-risk group were significantly worse overall survival (OS) compared to those in the low-risk group. Additionally, univariate and multivariate Cox regression analysis confirmed that risk score was an independent predictor of OS in patients with GBM. The nomogram which comprised age, gender, IDH mutation status, radiation therapy, and risk score yielded a strong predictive ability of 0.5-, 1-, and 2-year OS for GBM patients. Our results demonstrate that the EIRGs signature, which is associated with immune cell infiltration, may play a regulatory role in the immunobiological process of TIME. Prognostic-related 6-EIRGs signature is a promising classification index for predicting the drug sensitivity to immunotherapy and TMZ chemotherapy, suggesting that EIRGs signature may serve as a biomarker to stratify patients who will benefit from immunotherapy and chemotherapy.

Background: Glioblastoma (GBM) multiforme is a common malignant brain tumor with high mortality. It is urgently necessary to develop a new treatment because traditional approaches have reached a bottleneck.Purpose: Here we created an immune-related gene (IRGs)-based prognostic signature to comprehensively define the prognosis of glioblastoma (GBM).Methods: Glioblastoma samples were abstracted from the Chinese Glioma Genome Atlas (CGGA) and the Gene Expression Omnibus (GEO). We retrieved IRGs from the ImmProt data resource. Univariate Cox analysis was adopted to determine the prognostically remarkable IRGs for individual with GBM. The prognostically optimal IRGs were determined via LASSO regression, and predictive model created. Besides, the association of specific factors with the overall survival (OS) of individuals with GBM was explored via multivariate Cox-regression. Lastly, we constructed a predictive nomogram integrating the independent predictive factors to determine the one-, two-, and three-year OS likelihoods of individuals with GBM. Additionally,gene set enrichment analysis(GSEA) and single sample GSEA(ssGSEA) were performed to understand the correlation between the risk score and immune activity.Results: Overall, 273 IRGs which exhibited differential expression were identified in GBM tumor in contrast with the non-malignant samples. Of these 273 IRGs, only six were remarkably linked to OS of individuals with GBM, which were employed in constructing the predictive signature. The GBM were categorized into either the high-risk GBM group or the low-risk GBM group. There were remarkable differences between the high-risk GBM and the low-risk GBM groups regarding OS. The AUC for predicting one-,two-, and three-year OS in training set was 0.610,0.698 and 0.694.In line with the AUC of validation set was 0.608,0.692 and 0.678.Besides,the results of ssGSEA showed the score of prognostic signature is closely related to immune activity.Conclusion: Herein, a robust predictive model based on IRGs was created to estimate the diversity of OS likelihoods in GBM patients, as well as aid future clinical research.

Colon cancer poses a great threat to human health. Currently, there is no effective treatment for colon cancer due to its complex causative factors. Immunotherapy has now become a new method for tumor treatment. In this study, 487 DEGs were screened from The Cancer Genome Atlas (TCGA) database and ImmPort database, and GeneOntology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed. Hierarchical clustering of all samples revealed a significant correlation between colon cancer and immunity. The weighted gene co-expression network analysis (WGCNA) algorithm was used to identify key gene modules associated with immunity in colon cancer, here, module grey60 showed the highest correlation. A protein-protein interaction (PPI) network was constructed using the STRING database to screen hub genes, and subsequently, 7 immune-related genes the most closely associated with colon cancer were identified by differential expression in cancer and paracancer. Finally, a risk prediction model was developed using least absolute shrinkage and selection operator (LASSO) COX analysis, and the accuracy of the model was validated by GSE14333. This study determined that IRF4 and TNFRSF17 were immune-related genes in colon cancer, providing immune-related prognostic biomarkers for colon cancer.

Objective: To identify feature immune-related genes (IRGs) in patients with hypertrophic cardiomyopathy (HCM) and verify their ability to diagnose HCM. Methods: The {"type":"entrez-geo","attrs":{"text":"GSE160997","term_id":"160997"}}GSE160997 dataset on cardiac tissue from 18 HCM patients and 5 controls was downloaded from the Gene Expression Omnibus database. A false discovery rate 1 were the filters applied to identify the differentially expressed genes (DEGs). The differentially expressed IRGs were the intersection results between the DEGs and an IRG dataset from the IMMPORT database. The protein-protein interaction network of differentially expressed IRGs was constructed, and the top 20 hub genes with the most adjacent nodes in the network were selected. The least absolute shrinkage and selection operator regression algorithm and a random forest algorithm were used to identify the feature IRGs as biomarkers that were then verified against {"type":"entrez-geo","attrs":{"text":"GSE36961","term_id":"36961"}}GSE36961. Results: A total of 1079 DEGs were identified in {"type":"entrez-geo","attrs":{"text":"GSE160997","term_id":"160997"}}GSE160997. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses indicated that immune-related mechanisms play an important role in the pathogenesis of HCM. A total of 121 differentially expressed IRGs were identified, and 5 feature IRGs were selected, 4 of which were confirmed as potential biomarkers of HCM by external verification with excellent discrimination ability. A diagnosis model of HCM based on the four feature IRGs was developed and visualized as a nomogram with a C-index of 0.925 (95% confidence interval 0.869–0.981). Conclusion: Our study identified four feature IRGs as biomarkers for the diagnosis of HCM, offering an innovative perspective of the underlying immune-related pathological molecular mechanisms.

Aim: The prognosis of patients with ovarian cancer (OC) is highly heterogeneous which hinders to make an appropriate clinical decision. The study aimed to stratify patients’ prognoses by establishing a risk assessment model in the context of mRNA levels of immune-related genes (IRGs). Methods: Comprehensive bioinformatics analyses were done using datasets from The UCSC Xena platform, ICGC Data Portal, The Cancer Genome Atlas (TCGA), and the Genotype‐Tissue Expression (GTEx) project. LASSO regression was done to determine the independence of associations of specific factors with overall survival (OS). Nomogram that combined the independent prognostic factors was constructed to predict the OS of OC patients. The tumor microenvironment and immune response were estimated by cell type identification via estimating the relative subset of known RNA transcripts (CIBERSORT) and immunophenoscore (IPS). Results: Overall ten IRGs were significantly associated with the OS probability and were used for the prognostic model construction of OC patients. According to the prognostic model, ovarian cancer samples were identified as high- or low-risk group. A nomogram containing risk score, stage, histological grade and age could significantly predict 1-year, 3-years, and 5-years OS probability of OC patients, as well as a higher IPS score and a higher immunoreactivity phenotype, which were probably correlated with better immunotherapy response and good prognosis. Conclusions: We established a reliable IRGs-based risk model with potential prognostic value for patients with ovarian cancer. Further studies are needed to confirm these prognosis-associated biomarkers.

Background: Recently, immune system has been shown to be indispensable for ovarian cancer progression. The key immune-related genes (IRGs) related to the overall survival of ovarian cancer patients should be taken seriously. Here, we screened 9 survival-related IRGs in high-grade serous ovarian cancer (HGSOC) and build a prognostic signature to predict the outcome of HGSOC patients.Methods: We downloaded RNA-sequence profiles from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed genes between normal fallopian tube and HGSOC. Among these genes, IRGs were filtered based on the Immunology Database and Analysis Portal (ImmPort). Using univariate Cox regression, Lasso regression and multivariate Cox regression, we selected 9 survival-related IRGs and established a prognostic signature to compute the risk score. Patients were divided into a low-risk group and a high-risk group, and the immunological feature differences between them were analysed with the ESTIMATE R package, TIMER and GSEA software. Moreover, the prognostic signature was validated by data from Gene Expression Omnibus (GEO) datasets.Results: We obtained 1544 differentially expressed genes in HGSOC compared with normal fallopian tube, among which 99 genes were related to immunology. After univariate Cox regression, Lasso regression and multivariate Cox regression, nine IRGs (HLA-F, PSMC1, PI3, CXCL10, CXCL9, CXCL11, LRP1, STAT1 and OGN) were identified as optimal survival-related IRGs and used to establish a prognostic signature for calculating the risk scores of HGSOC patients. The prognostic signature showed its efficiency in predicting the overall survival of HGSOC patients in TCGA training cohort (p=1.018e-8) and GEO test cohort (p=2.632e-2). Age and risk scores were independent risk factors for overall survival. As the risk scores increased, the proportions of neutrophil, dendritic cells, CD8+ T cells, CD4+ T cells and B cells decreased (p values were 0.026, 1.909e-4, 9.165e-10, 0.003 and 2.658e-4, respectively). In addition, 21 out of 24 HLA-related genes were highly expressed in the low-risk group than in the high-risk group. The above might prompt a stronger immune response in the low-risk group.Conclusions: Our study constructed a nine-IRG-based prognostic signature that could effectively predict the overall survival of HGSOC patients and become a promising therapeutic target for HGSOC treatments.

Xu Zhang,1,&ast; Shuai Ping,2,&ast; Anni Wang,3 Can Li,4 Rui Zhang,5 Zimu Song,3 Caibin Gao,3 Feng Wang6 1Department of Neurosurgery, Baoding No.1 Central Hospital, Baoding, People’s Republic of China; 2Department of Orthopaedics, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 3Department of Neurosurgery, General Hospital of Ningxia Medical University, Yinchuan, People’s Republic of China; 4Department of Neurosurgery, Chengdu Sixth People’s Hospital, Chengdu, People’s Republic of China; 5Ningxia Key Laboratory of Cerebrocranial Disease, Incubation Base of National Key Laboratory, Ningxia Medical University, Yinchuan, People’s Republic of China; 6Department of Neurosurgery, People’s Hospital of Ningxia Hui Autonomous Region Yinchuan, Yinchuan, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Feng Wang Email nxwwang@163.comBackground: Gliomas are prevalent primary intracerebral malignant tumors. Increasing evidence indicates an association between the immune signature and Grade II/III glioma prognosis. Thus, we aimed to develop an immune-related gene pair (IRGP) signature that can be used as a prognostic tool in Grade II/III glioma.Methods: The gene expression levels and clinical information of Grade II/III glioma patients were collected from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. The TCGA data were randomly divided into a training cohort (n = 249) and a validation cohort (n = 162), and a CGGA dataset served as an external validation group (n = 605). IRGPs significantly associated with prognosis were selected by Cox regression. Gene set enrichment analysis and filtration were performed with the IRGPs.Results: Within a set of 1991 immune genes, 8 IRGPs including 15 unique genes that significantly affect survival constituted a gene signature. In the validation datasets, the IRGP signature significantly stratified patients with Grade II/III glioma into low- and high-risk groups (P < 0.001), and the IRGP index was found to be an independent prognostic factor through univariate and multivariate analyses (P < 0.05). Additionally, 26 functional pathways were identified through the intersection of Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) enrichment analysis.Conclusion: The IRGP signature demonstrated good prognostic value for Grade II/III gliomas, which may provide new insights into individual treatment for glioma patients. The IRGPs might function through the identified 26 functional pathways.Keywords: glioma, prognosis, immune-related gene pairs, TCGA, CGGA

Background: Breast cancer (BC) is the cancer with the largest number of deaths in women. There is growing evidence that immunity plays an important role in the prognosis of breast cancer. Methods: In this study, we developed and validated an immune-related gene pair signature (IRGPs) to predict the survival of breast cancer patients. Screening immune-related genes from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database for the construction of IRGPs, and patients with breast cancer in these two cohorts were assigned to low- and high- risk subgroups. Additionally, we used Kaplan-Meier survival analysis, univariate and multivariate Cox analysis to investigate IRGPs and their individualized prognostic characteristics, and analysis of immune cell infiltration in breast cancer. Results: A 47-IRGP signature was constructed from 2498 immune genes, which could significantly predict the overall survival (OS) of breast cancer patients in the TCGA and GEO cohorts. Immune infiltration analysis showed that a variety of immune cells are significantly related to the prognostic effects of IRGP characteristics in breast cancer patients, especially CD8+ T cells and macrophages. Conclusions: The IRGP signature constructed in this study can help determine the prognosis of breast cancer and provide new ideas and basis for future research on the role of immune-related genes in breast cancer patients.

Oral lichen planus (OLP) is a chronic autoimmune oral mucosal disease which seriously affect the life quality of the patients. But till now, the exact etiology and pathogenesis of OLP still remains unclear. The aim of our study was to find the key molecules and pathway involved in the pathogenesis mechanisms of OLP, therefore, provide reference for identifying more effective therapeutic strategies for OLP. Data from GSE52130 were downloaded from GEO data sets for analysis, and a total of 255 differentially expressed genes were selected out. Then by means of a series of gene enrichment analysis, we illustrated that most of these important genes were enriched in the focal adhesion pathway and metabolism pathways. In addition, we also constructed a protein-protein interaction (PPI) network and identified 8 hub genes which may play the important role in the development of OLP, including THBS2, COL3A1, IGFBP3, COL1A2, COL1A1, PTGS2, IL1B, ARG1. Lastly, we also predicted transcription factors of these hub genes, then constructed the corresponding transcription regulatory network. Taken together, our data indicated that the focal adhesion pathway and metabolism pathways, as well as 8 crucial immune-related genes might be most likely involved in the development of OLP, which would provide important information and objective basis for elucidating the pathogenesis of OLP and providing more effective targeted immunotherapy strategies for OLP.

Cutaneous malignant melanoma (hereafter called melanoma) is one of the most aggressive cancers with increasing incidence and mortality rates worldwide. In this study, we performed a systematic investigation of the tumor microenvironmental and genetic factors associated with melanoma to identify prognostic biomarkers for melanoma. We calculated the immune and stromal scores of melanoma patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and found that they were closely associated with patients' prognosis. Then the differentially expressed genes were obtained based on the immune and stromal scores, and prognostic immune-related genes further identified. Functional analysis and the protein-protein interaction network further revealed that these genes enriched in many immune-related biological processes. In addition, the abundance of six infiltrating immune cells was analyzed using prognostic immune-related genes by TIMER algorithm. The unsupervised clustering analysis using immune-cell proportions revealed eight clusters with distinct survival patterns, suggesting that dendritic cells were most abundant in the microenvironment and CD8+ T cells and neutrophils were significantly related to patients' prognosis. Finally, we validated these genes in three independent cohorts from the Gene Expression Omnibus database. In conclusion, this study comprehensively analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for melanoma.

The aim of this study is to evaluate the effects of Moringa oleifera (MO) on the performance, antioxidative status, and immune related gene expression in broilers raised under normal or heat stress conditions. Broiler chickens were distributed into 4 groups and fed diets with dietary MO at 0% or 5% (MO0 or MO5) and raised under ambient temperature 22 ± 1 °C (N) or 35±1 °C (HS). HS conditions negatively affected the weight gain and FCR, while feeding MO exhibited beneficial effects especially under HS conditions. Triglycerides, total cholesterol and low-density lipoprotein cholesterol (LDL) levels were significantly (P 0.05) higher in chickens raised in HS conditions and fed the basal diet than those in normal condition and fed with or without MO, while MO decreased triglycerides and total cholesterol levels in normal and HS conditions. Blood high-density lipoprotein cholesterol (HDL) was significantly decreased in broilers raised in HS conditions and fed diets without MO, while MO increased HDL level. Blood glutathione peroxidase (GSH-Px) was significantly (P 0.05) decreased in broilers raised in HS conditions and fed the basal diet without MO. mRNA expression of GSH-Px was significantly (P 0.05) downregulated in broilers raised in HS conditions and fed diets without MO. Broilers under normal or HS conditions and fed the basal diet exhibited significantly (P 0.05) downregulated mRNA expressions of superoxide dismutase (SOD) and catalase (CAT) compared to chickens under normal conditions and fed MO. Liver and muscle thiobarbituric acid reactive substance (TBARs) were significantly (P 0.05) increased in broilers under HS conditions and fed diet without MO. The expressions of interleukins (IL2 and IL6) were significantly (P 0.05) downregulated in broilers under normal or HS conditions and fed diets without MO. To sum up, HS conditions depressed the performance, antioxidative status, and immune related gene expression in broilers, while MO obviously alleviated these negative effects in broilers.

Introduction: To date, the role of immunotherapy with check point inhibitors and/or vaccines in the treatment of breast cancer (BC) is still debating, and the main focus of immunotherapy in BC is on triple negative subtype as a target population in many ongoing clinical trials. Translational research into identifying predictive and prognostic immune biomarkers is of particular clinical relevance, but, there are currently no definite prognostic and predictive immune biomarkers in BC, especially in triple negative breast cancer(TNBC). We investigated the expression profiles of immune genes in patients with TNBC to identify the prognostic value of immune genes in search of clinical implications. Methods : We investigated expression profiles of 770 pan-cancer immune related genes using the nCounter mRNA expression assay (NanoString®) from paraffin-embedded tumor tissues in 200 patients diagnosed as TNBC who received curative surgery at Samsung Medical Center from 2000 to 2004. We analyzed the relationship between stage adjusted level of gene expressions and patients' survival outcomes using Cox regression model. Results: Of 770 genes, 186 genes were selected from univariate analysis with clinical stage adjustment. In multivariate analysis using Cox regression, expressions of CD1B, CD45, CD53, CT45A1, GTF3C1, IL11RA, IL1RN, LRRN3, MAPK1, NEFL, PRKCE, SPACA3 and RANKL were associated with distant recurrence free survival (p Table 1ParameterParameter EstimateStandard Errorp-valueHazard Ratio95% Confidence Interval(a) distant recurrence free survival Stage2.487350.680570.000312.0293.169, 45.661CD1B1.141910.2753 Conclusion: High expression of IL1RN, PRKCE were associated with short distant recurrence free survival and overall survival in patients with TNBCs who received curative surgery. In contrast, RANKL expression resulted in prolonged distant recurrence free survival and overall survival. Citation Format: Kim J-Y, Jung HH, Lim JE, Cho EY, Lee SK, Yu JH, Lee JE, Kim SW, Nam SJ, Park YH, Ahn JS, Im Y-H. Prognostication of immune related gene expression in patients with triple negative breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-08-30.

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

Background: Although intrinsic molecular subtype has been extensively used, the risk stratification have not been fully elucidated in estrogen receptor (ER) or progesterone receptor (PR) positive and human epidermal growth factor receptor 2 (HER2) negative breast cancer. Methods: RNA transcriptional data from The Cancer Genome Atlas (TCGA), METABRIC and GEO were used. Immune-related genes were obtained from the datasets and literature search. Univariate, lasso regression and multivariate cox regression were employed to identify prognostic immune-related genes and establish the risk signature. Relationships between the risk signature and clinical parameters, tumor-infiltrating immune cell abundances and cancer phenotypes were further evaluated.Results: Noted, 102 immune-related prognostic genes were identified in METABRIC dataset by univariate cox analysis. Consecutively, 7 immune genes (SHMT2, AGA, COL17A1, FLT3, SLC7A2, ATP6AP1 and CCL19) were selected as risk signature by lasso regression and multivariate cox analysis. Its performance was further verified in TCGA,GSE20685 and GSE9195 datasets. Multivariate Cox regression indicated that the risk signature was an independent predictor. The prognostic signature showed significant correlation with intrinsic molecular subtypes, 70-gene signature and tamoxifen resistance signature. The CIBERSORT algorithm revealed that CD4+ memory T cells were significant higher in low-risk group. Conversely, M0-type macrophages were significant higher in high-risk group in both TCGA and METABRIC cohorts, which may have effect on the prognosis. Furthermore, we found that low-risk group may be associated with immune-related pathway and high-risk group was with cell cycle-related pathway, which also showed impact on the prognosis.Conclusion: The present study constructed a robust seven immune-related gene signature and established an effective method in risk stratification and prediction of clinical outcome in ER or PR positive and HER2 negative breast cancer.

The prognosis of acute myeloid leukemia (AML) is closely related to immune response changes. Further exploration of the pathobiology of AML focusing on immune-related genes would contribute to the development of more advanced evaluation and treatment strategies. In this study, we established a novel immune-17 signature based on transcriptome data from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) databases. We found that immune biology processes and transcriptional dysregulations are critical factors in the development of AML through enrichment analyses. We also formulated a prognostic model to predict the overall survival of AML patients by using LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis. Furthermore, we incorporated the immune-17 signature to improve the prognostic accuracy of the ELN2017 risk stratification system. We concluded that the immune-17 signature represents a novel useful model for evaluating AML survival outcomes and may be implemented to optimize treatment selection in the next future.

Colorectal cancer (CRC) is a common digestive tract tumor worldwide. In recent years, neoadjuvant chemoradiotherapy (CRT) has been the most comprehensive treatment for locally advanced rectal cancer (LARC). In this study, we explored immune infiltration in rectal cancer (RC) and identified immune-related differentially expressed genes (IRDEGs). Then, we identified response markers in datasets in GEO databases by principal component analysis (PCA). We also utilized three GEO datasets to identify the up- and downregulated response-related genes simultaneously and then identified genes shared between the PCA markers and three GEO datasets. Based on the hub IRDEGs, we identified target mRNAs and constructed a ceRNA network. Based on the ceRNA network, we explored prognostic biomarkers to develop a prognostic model for RC through Cox regression. We utilized the specimen to validate the expression of the two biomarkers. We also utilized LASSO regression to screen hub IRDEGs and built a nomogram to predict the response of LARC patients to CRT. All of the results show that the nomogram and prognostic model offer good prognostic value and that the ceRNA network can effectively highlight the regulatory relationship. hsa-mir-107 and WDFY3-AS2 may be prognostic biomarkers for RC.