Diagnostic biomarker for type 2 diabetic peripheral neuropathy via comprehensive bioinformatics analysis

BACKGROUND

Diabetic peripheral neuropathy (DPN) is a common complication of Type 2 diabetes mellitus (T2DM), which frequently results in disabling neuropathic pain and lower-limb amputation. The identification of noninvasive biomarkers for DPN may help early detection and individualized treatment of DPN.

METHODS

In this study, we identified differentially expressed genes (DEGs) between DPN and the control based on blood-source (GSE95849) and tissue-source gene expression profiles (GSE143979) from the Gene Expression Omnibus (GEO) database using limma, edgeR, and DESeq2 approaches. KEGGG and GO functional enrichments were performed. Hub genes and their correlation with infiltrating immune cells were analyzed. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to quantify hub gene expression.

RESULTS

In total, 144 DEGs between DPN and the control were identified. Functional enrichment revealed that the DEGs were mainly enriched in immune-related pathways like the Fc epsilon receptor Ig signaling pathway. By protein-protein interaction (PPI) network analysis, FCER1G, SYK, ITGA4, F13A1, MS4A2, and PTK2B were screened as hub genes with higher expression in DPN patients, among which half were immune genes (FCER1G, PTK2B, and SYK). RT-qPCR demonstrated that mRNA expression of FCER1G, PTK2B, and SYK was significantly increased in patients with DPN compared with both diabetic nonperipheral neuropathy (DNN) and normal subjects. The area under the receiver operating characteristic (ROC) curve of FCER1G, PTK2B, and SYK was 0.84, 0.81, and 0.73, respectively, suggesting their great advantages as diagnostic biomarkers to predict the progression of neuropathy in T2DM. Further analysis indicated that the expression of FCER1G, PTK2B, and SYK was negatively correlated with the cell proportion of significantly altered resting natural killer cells, T follicular helper cells, and activated mast cells, but positively correlated with monocytes.

CONCLUSIONS

Our findings demonstrated FCER1G, PTK2B, and SYK are potential diagnostic biomarkers and therapeutic targets for DPN, which provides new insight into DPN pathogenesis and therapies.

Research progress of aphid immunity system: Potential effective target for green pest management

Due to the absence of acquired immunity, insects primarily rely on their innate immune system to resist pathogenic microorganisms and parasitoids in natural habitats. This innate immune system can be classified into cellular immunity and humoral immunity. Cellular immunity is mediated by hemocytes, which perform phagocytosis, aggregation, and encapsulation to fight against invaders, whereas the humoral immunity primarily activates the immune signaling pathways and induces the generation of immune effectors. Existing studies have revealed that the hemipteran aphids lack some crucial immune genes compared to other insect species, indicating the different immune mechanisms in aphids. The current review summarizes the adverse impacts of pathogenic microorganisms and parasitoids on aphids, introduces the cellular and humoral immune systems in insects, and analyzes the differences between aphids and other insect species. Furthermore, our review also discussed the existing prospects and challenges in aphid immunity research, and proposed the potential application of immune genes in green pest management.

Establishment and verification of a prognostic risk score model based on immune genes for hepatocellular carcinoma in an Asian population

Background

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, with the highest incidence in East Asia, and hepatitis B virus (HBV) infection is the most common cause of HCC in Asian population. The immune system is closely related to the development of HCC and plays an important role in the treatment of this disease. In this study, we analyzed the data of HCC from The Cancer Genome Atlas (TCGA) database and constructed a risk-score prognostic model based on immune genes of an Asian HCC population, aiming to provide new perspectives for clinical treatment and management of HCC in Asian population.

Methods

Data concerning clinical attributes and transcriptomic profiles of individuals in the Asian population diagnosed with HCC were retrieved from the TCGA database. Concurrently, immune-related genes were sourced from the Immport database for incorporation into our analysis. A total of 265 immune-related genes displaying differential expression were identified through wilcoxTest analysis in R. Further refinement using univariate and multivariate Cox regression analysis led to the identification of 15 genes that exhibited strong associations with prognosis. MICB/PSMD14/TRAF3/SP1/NDRG1/HDAC1/HRAS/NRAS/SEMA5B/GMFB/ACVR2B/BRD8/MMP12/KITLG/DCK, and a prognostic risk score model was constructed based on the above genes.

Results

The findings demonstrated notable differences in survival rates between the low-risk and high-risk groups, as depicted by the Kaplan-Meier (K-M) survival curves (P<0.001). Furthermore, the model's predictive capability was evidenced by receiver operating characteristic (ROC) curves, with area under the curve (AUC) =0.901. Finally, the relationship of the model with each clinical trait and immune cells was assessed by correlation analysis.

Conclusions

The prognostic risk score model constructed by immune genes based on the Asian HCC population has certain predictive capacity.

Erratum: Analysis of the regulatory mechanisms of prognostic immune factors in thyroid cancer

[This corrects the article DOI: 10.3389/fonc.2022.1059591.].

Effects of Scallop Visceral Mass and Mantle as Dietary Supplements on the Growth, Immune Response and Intestinal Microflora of Juvenile Sea Cucumber Apostichopus japonicus

Scallop visceral mass and mantle are aquatic byproducts and waste, but they have high contents of protein. In this study, scallop visceral mass and mantle were used as supplements in the diet of juvenile sea cucumber (A. japonicus) and their effects on the growth, fatty acid and amino acid compositions, the non-specific immune responses and the intestinal microflora of A. japonicus were investigated through a 40 d feeding experiment. The results showed that dietary supplementation of scallop visceral mass significantly accelerated the specific growth rate (SGR) of juvenile A. japonicus by 3 times within 20 days, and also raised the contents of ω-3 fatty acids including EPA and DHA and the ω-3/ω-6 ratio of the sea cucumber tissue, which is favorable to the health and commercial value of the sea cucumber. Furthermore, it was found that the supplementation of scallop visceral mass and mantle stimulated the expression of immune-related genes and enhanced the immune defense in A. japonicus. Scallop visceral mass and mantle supplementation also increased the microbial diversity and the abundance of beneficial microbes including Bifidobacteriaceae, Streptomycetaceae, Clostridiaceae and Rhizobiales in the gut of A. japonicus. This study reveals the beneficial effects of dietary supplementation of scallop visceral mass and mantle on the growth of juvenile A. japonicus, which might be a promising way to reutilize this scallop waste and raise its economic value.

Effects of the pyrE deletion mutant from Bacillus thuringiensis on gut microbiota and immune response of Spodoptera exigua

The gut microbiota is essential for the growth and development of insects, and the intestinal immune system plays a critical role in regulating the homeostasis of intestinal microorganisms and their interactions with pathogenic bacteria. Infection with Bacillus thuringiensis (Bt) can disrupt the gut microbiota of insects, but the regulatory factors governing the interaction between Bt and gut bacteria are not well understood. Uracil secreted by exogenous pathogenic bacteria can activate DUOX-mediated reactive oxygen species (ROS) production, which helps maintain intestinal microbial homeostasis and immune balance. To elucidate the regulatory genes involved in the interaction between Bt and gut microbiota, we investigate the effects of uracil derived from Bt on gut microbiota, and host immunity using a uracil deficient Bt strain (Bt GS57△pyrE) obtained by homologous recombination. We analyze the biological characteristics of the uracil deficient strain and found that the deletion of uracil in Bt GS57 strain changed the diversity of gut bacteria in Spodoptera exigua, as investigated using Illumina HiSeq sequencing. Furthermore, qRT-PCR analysis showed that compared with Bt GS57 (control), the expression of the SeDuox gene and the level of ROS were significantly decreased after feeding with Bt GS57△pyrE. Adding uracil to Bt GS57△pyrE restored the expression level of DUOX and ROS to a higher level. Additionally, we observed that PGRP-SA, attacin, defensin and ceropin genes were significant different in the midgut of S. exigua infected by Bt GS57 and Bt GS57△pyrE, with a trend of increasing first and then decreasing. These results suggest that uracil regulates and activates the DUOX-ROS system, affects the expression of antimicrobial peptide genes, and disturb intestinal microbial homeostasis. We preliminarily speculate that uracil is a key factor in the interaction between Bt and gut microbiota, and these findings provide a theoretical basis for clarifying the interaction between Bt, host, and intestinal microorganisms, as well as for gaining new insights into the insecticidal mechanism of B. thuringiensis in insects.

Regularized survival learning and cross-database analysis enabled identification of colorectal cancer prognosis-related immune genes

Colon adenocarcinoma is the most common type of colorectal cancer. The prognosis of advanced colorectal cancer patients who received treatment is still very poor. Therefore, identifying new biomarkers for prognosis prediction has important significance for improving treatment strategies. However, the power of biomarker analyses was limited by the used sample size of individual database. In this study, we combined Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases to expand the number of healthy tissue samples. We screened differentially expressed genes between the GTEx healthy samples and TCGA tumor samples. Subsequently, we applied least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis to identify nine prognosis-related immune genes: ANGPTL4, IDO1, NOX1, CXCL3, LTB4R, IL1RL2, CD72, NOS2, and NUDT6. We computed the risk scores of samples based on the expression levels of these genes and divided patients into high- and low-risk groups according to this risk score. Survival analysis results showed a significant difference in survival rate between the two risk groups. The high-risk group had a significantly lower overall survival rate and poorer prognosis. We found the receiver operating characteristic based on the risk score was showed to accurately predict patients' prognosis. These prognosis-related immune genes may be potential biomarkers for colorectal cancer diagnosis and treatment. Our open-source code is freely available from GitHub at https://github.com/gutmicrobes/Prognosis-model.git.

Identification of immune-related hub genes and analysis of infiltrated immune cells of idiopathic pulmonary artery hypertension

Objectives

Idiopathic pulmonary artery hypertension (IPAH) is a rare but life-threaten disease. However, the mechanism underlying IPAH is unclear. In this study, underlying mechanism, infiltration of immune cells, and immune-related hub genes of IPAH were analyzed via bioinformatics.

Methods

GSE15197, GSE48149, GSE113439, and GSE117261 were merged as lung dataset. Weighted gene correlation network analysis (WGCNA) was used to construct the co-expression gene networks of IPAH. Gene Ontology and pathway enrichment analysis were performed using DAVID, gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA). Infiltration of immune cells in lung samples was analyzed using CIBERSORT. GSE22356 and GSE33463 were merged as peripheral blood mononuclear cells (PBMCs) dataset. Immune-related differentially expressed genes (IRDEGs) of lung and PBMCs dataset were analyzed. Based on the intersection between two sets of IRDEGs, hub genes were screened using machine learning algorithms and validated by RT-qPCR. Finally, competing endogenous RNA (ceRNA) networks of hub genes were constructed.

Results

The gray module was the most relevant module and genes in the module enriched in terms like inflammatory and immune responses. The results of GSEA and GSVA indicated that increasement in cytosolic calcium ion, and metabolism dysregulation play important roles in IPAH. The proportions of T cells CD4 memory resting and macrophage M1 were significantly greater in IPAH group, while the proportions of monocytes and neutrophils were significantly lower in IPAH group. IRDEGs of two datasets were analyzed and the intersection between two set of IRDEGs were identified as candidate hub genes. Predictive models for IPAH were constructed using data from PBMCs dataset with candidate hub genes as potential features via LASSO regression and XGBoost algorithm, respectively. CXCL10 and VIPR1 were identified as hub genes and ceRNA networks of CXCL10 was constructed.

Conclusion

Inflammatory response, increasement in cytosolic calcium ion, and metabolism dysregulation play important roles in IPAH. T cells CD4 memory resting and macrophage M1 were significantly infiltrated in lung samples from patients with IPAH. IRDEGs of lung dataset and PBMCs dataset were analyzed, and CXCL10 and VIPR1 were identified as hub genes.

Analysis of the regulatory mechanisms of prognostic immune factors in thyroid cancer

Objective

To explore the regulatory mechanism of immune prognostic factors in thyroid cancer.

Methods

Based on the TCGA database and GEO database, this study used bioinformatics methods to study the potential regulatory mechanism of thyroid cancer prognosis, analyzed the differentially expressed genes and differential miRNAs between thyroid cancer and normal paracancerous tissues by R software, and constructed lasso risk factors. The immune prognostic factors of thyroid cancer were obtained from the model, and the miRDB website was used to predict the possibility of differential miRNA target binding of the immune prognostic factors and correlation analysis was performed, and finally verified by cell experiments.

Results

There were 1413 differentially expressed genes between thyroid cancer and normal paracancerous tissues, among which 21 immune-related genes were prognostic factors with significant differences in expression; lasso risk model obtained AKAP12, APOC1, TIMP3, ADAMTS9, ANK2, HTRA3, SYNDIG1 , ADAMTS5 and DACT1 were nine prognostic factors. A total of 58 differential miRNAs were found in thyroid cancer tissues and non-cancerous tissues. The possibility of differential miRNA targeting and binding of immune prognostic factors on the miRDB website and cell experiments was analyzed.

Conclusions

The potential miRNA regulatory mechanism of immune prognostic factors in thyroid cancer has been explored.

Bioinformatics Analysis and Experimental Identification of Immune-Related Genes and Immune Cells in the Progression of Retinoblastoma

Purpose

Retinoblastoma (RB) is the most common type of aggressive intraocular malignancy in children. The alteration of immunity during RB progression and invasion has not yet been well defined. This study investigated significantly altered immune-associated genes and cells related to RB invasion.

Methods

The differentially expressed immune-related genes (IRGs) in noninvasive RB and invasive RB were identified by analysis of two microarray datasets (GSE97508 and GSE110811). Hub IRGs were further identified by real time PCR. The single-sample gene set enrichment analysis algorithm and Pearson correlation analysis were used to define immune cell infiltration and the relationships between hub IRGs and immune cells. Cell viability and migration were evaluated by CCK-8 and Transwell assays. A xenograft mouse model was used to verify the relationship between Src homology 3 (SH3) domain GRB2-like 2 (SH3GL2) expression and myeloid-derived suppressor cells (MDSCs).

Results

Eight upregulated genes and six downregulated IRGs were identified in invasive RB. Seven IRGs were confirmed by real-time PCR. Moreover, the proportions of MDSCs were higher in invasive RB tissues than in noninvasive RB tissues. Furthermore, correlation analysis of altered immune genes and cells suggested that SH3GL2, Langerhans cell protein 1 (LCP1) and transmembrane immune signaling adaptor TYROBP have strong connections with MDSCs. Specifically, decreased SH3GL2 expression promoted the migration of RB cells in vitro, increased the tumor size and weight, and increased the numbers of MDSCs in the tumor and spleen in vivo.

Conclusions

This study indicated that SH3GL2 and MDSCs play a critical role in RB progression and invasion and provide candidate targets for the treatment of RB.

Best genome sequencing strategies for annotation of complex immune gene families in wildlife

BACKGROUND

The biodiversity crisis and increasing impact of wildlife disease on animal and human health provides impetus for studying immune genes in wildlife. Despite the recent boom in genomes for wildlife species, immune genes are poorly annotated in nonmodel species owing to their high level of polymorphism and complex genomic organisation. Our research over the past decade and a half on Tasmanian devils and koalas highlights the importance of genomics and accurate immune annotations to investigate disease in wildlife. Given this, we have increasingly been asked the minimum levels of genome quality required to effectively annotate immune genes in order to study immunogenetic diversity. Here we set out to answer this question by manually annotating immune genes in 5 marsupial genomes and 1 monotreme genome to determine the impact of sequencing data type, assembly quality, and automated annotation on accurate immune annotation.

RESULTS

Genome quality is directly linked to our ability to annotate complex immune gene families, with long reads and scaffolding technologies required to reassemble immune gene clusters and elucidate evolution, organisation, and true gene content of the immune repertoire. Draft-quality genomes generated from short reads with HiC or 10× Chromium linked reads were unable to achieve this. Despite mammalian BUSCOv5 scores of up to 94.1% amongst the 6 genomes, automated annotation pipelines incorrectly annotated up to 59% of manually annotated immune genes regardless of assembly quality or method of automated annotation.

CONCLUSIONS

Our results demonstrate that long reads and scaffolding technologies, alongside manual annotation, are required to accurately study the immune gene repertoire of wildlife species.

External Validation of the Prognostic Value of an Immune-Associated Gene Panel for Clear Cell Renal Cell Carcinomas

Clear cell renal cell carcinomas (ccRCCs) are highly immune infiltrates, and many of them respond to immunotherapy with checkpoint inhibitors including anti-PD-L1 or anti-PD1 agents. However, the effect of immune genes on clinical outcomes in ccRCCs has not been fully studied. Here, we show in this study that an immune-associated gene panel has a prognostic value for clear cell renal cell carcinomas. We performed single-sample gene set enrichment analysis (ssGSEA) and cell type identification by estimating subsets of RNA transcripts (CIBERSORT) algorithms on patient-matched normal renal and RCC tissues to characterize two immunophenotypes and immunological characteristic subpopulations. Furthermore, LASSO Cox regression was applied to develop a novel prognosis-associated model for ccRCC patients based on an immune-gene panel. The results were verified by the Gene Expression Omnibus (GEO) dataset and coordinated with the clinicopathological characteristics of ccRCCs, along with genomic signatures. Finally, based on the above perspectives, we generated a nomogram with a high prognostic efficiency for ccRCC patients. Overall, this study offers a unique perspective that can contribute to improving the accuracy of prognosis prediction and treatment with immunotherapy.

Essential Oils Improve the Survival of Gnotobiotic Brine Shrimp (Artemia franciscana) Challenged With Vibrio campbellii

The halophilic aquatic bacterium Vibrio campbellii is an important aquatic pathogen, capable of causing vibriosis in shrimp and fish resulting in significant economic losses. In a previous work, essential oils (EOs) extracts from Melaleuca alternifolia, Litsea citrata, and Eucalyptus citriodora were found to inhibit the growth of V. campbellii in vitro. This study aimed to determine in vivo EOs’ potential protective effect towards gnotobiotic brine shrimp Artemia franciscana, challenged with V. campbellii. The study showed that brine shrimp larvae supplemented with EOs of M. alternifolia (0.0008%) and L. citrata (0.002%) displayed significantly increased survival against V. campbellii. The results indicated that supplementation of these EOs increased the expression of immune-related genes (either in the presence or absence of the pathogen), probably contributing to enhanced protection. Furthermore, in vitro studies indicated that some EOs modulated the expression of virulence factors including swimming motility, biofilm formation, and gelatinase and lipase activity, while flow cytometry data and regrowth assay indicated that these EOs do not exhibit antimicrobial activity as V. campbellii grew at the tested concentrations [M. alternifolia (0.0008%) and L. citrata (0.002%)]. Our findings suggest that EOs extracted from M. alternifolia and L. citrata, can modulate virulence factor production and immunological responses and might hence become part of an intervention strategy to control vibriosis in a fish or shrimp aquaculture setting, a hypothesis that needs to be validated in the future.

A seven-gene signature and the C-C motif chemokine receptor family genes are the sarcoma-related immune genes

Cells of the tumor microenvironment exert a vital influence on sarcoma prognosis. This study aimed to analyze and identify differentially expressed genes (DEGs) related to immunity and their significance as immune biomarkers for the accurate prediction of overall survival of patients with sarcoma. The Cancer Genome Atlas was adopted for obtaining sarcoma gene microarray and corresponding clinical information. ESTIMATE algorithm was used to calculate tumor immune microenvironment indices. Immune-associated DEGs were identified using the limma packages and were further analyzed using the ClusterProfiler package and STRING website. Based on the results of these analyses, we constructed a prognostic model. Furthermore, we assessed the prognosis prediction model through functional evaluation and analysis of GSE17674. The functional analysis revealed that the upregulated immune DEGs were related to immune-related aspects. Chemokine ligands/receptors and immune-related genes were found to be vital for sarcoma formation and progression. We established a prognostic signature of seven genes, which indicated that high-risk cases exhibit poor prognostic outcome. The prognostic signature constructed in this study can accurately predict the overall prognosis in patients with sarcoma. Moreover, the novel immune gene expression analysis may provide clinical guidance for predicting prognosis in patients with sarcoma.

Significance of DMBT1 in Papillary Thyroid Carcinoma Concurrent With Hashimoto's Thyroiditis

Background

Papillary thyroid carcinoma (PTC) concurrent with Hashimoto’s thyroiditis (HT) was associated with a better clinical prognosis. This study aimed to investigate a potential mRNA gene that affects the development of PTC, which helps PTC concurrent with HT patients have a better prognosis.

Material/Methods

PTC data were obtained from The Cancer Genome Atlas (TCGA) database. And the validation data of tissue specimens were collected from Guangzhou First People’s Hospital. The thyroid tissue sections were hybridized with deleted in malignant brain tumor 1 (DMBT1) probes by situ hybridization. Survival rates were analyzed using Kaplan-Meier curves, and the log-rank test was used to compare group survival rates. Prognosis clinicopathological factors were analyzed by Cox regression. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) pathway enrichment analyses were performed using single-sample gene set enrichment analysis (ssGSEA). Finally, the correlation of deletion in DMBT1 expression with overall immune status, tumor purity, and human leukocyte antigen (HLA) gene expression profile was analyzed.

Results

HT was significantly associated with sex, tumor foci, extrathyroidal extension (ETE), residual tumor, and tumor stage (T stage). Moreover, PTC concurrent with HT had a lower risk of recurrence versus non-HT groups. A total of 136 differentially expressed mRNAs (DEMs) were identified between HT and non-HT groups. Among them, the expression level of DMBT1 in HT groups was statistically higher than that in non-HT groups. A significant association with ETE and recurrence was revealed in the high expression and the low expression of DMBT1. Furthermore, DMBT1 was an independent predictor of survival. The overall immune activity of high expression of DMBT1 was higher than that of the low-expression group.

Conclusions

The PTC patients with HT had better behavior features and prognosis than those with simple PTC. DMBT1 in PTC-HT patients was a potential possible factor that inhibits tumors. High expression of DMBT1 may improve PTC prognosis by immune-related pathways.

A novel recurrence associated immune gene signature and its clinical significance in liver cancer

Hepatocellular carcinoma (HCC) is one of the most common malignancies across nations. Although the outcome of HCC has been improved significantly with the advances in comprehensive treatment, patients remain suffered from recurrence as well as metastasis. Therefore, it is urgent to identify reliable biomarkers for predicting the recurrence of HCC, by which the treating strategy can be made to restrain tumor progress. Increasing evidence has shown the association between immune signature and prognosis of HCC. Thus, we aimed to discover an immune-related gene signature that can estimate the recurrence rates of HCC. We collected gene expression profiles and clinical information of patients from GEO and TCGA dataset. Furthermore, we conducted a lasso regression analysis and established a recurrence-related model consisting of 36 immune-related gene pairs (IRGPs) with 54 genes. We validated the IRGPs in the validation cohort and observed that the immune-related signature robustly stratified patients with HCC into high- and low-risk groups in terms of recurrence (P < 0.001). Multivariant Cox regression analysis showed the relationship between the model and recurrence outcomes (Hazard Ratio: 3.81 95% Confidence Interval: 2.90-5.00). Gene Ontology and KEGG enrichment analyses revealed that those genes were enriched in important signaling pathways. In summary, we developed a robust model based on the signature of immune-related genes for forecasting the recurrence outcome of patients with HCC, which holds the potential to assist clinical practice.

Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System

Background: The tumour immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. Artificial intelligence medicine studies based on big data and advanced algorithms are helpful for improving the accuracy of prediction models of tumour prognosis. The current research aims to explore potential prognostic immune biomarkers and develop a predictive model for the overall survival of ovarian cancer (OC) based on artificial intelligence algorithms.

Methods: Differential expression analyses were performed between normal tissues and tumour tissues. Potential prognostic biomarkers were identified using univariate Cox regression. An immune regulatory network was constructed of prognostic immune genes and their highly related transcription factors. Multivariate Cox regression was used to identify potential independent prognostic immune factors and develop a prognostic model for ovarian cancer patients. Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system.

Results: The current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes between tumour samples and normal samples. Further univariate Cox regression identified 84 prognostic immune gene biomarkers for ovarian cancer patients in the model dataset (GSE32062 dataset and GSE53963 dataset). An immune regulatory network was constructed involving 63 immune genes and 5 transcription factors. Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL, ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5, and LTN1) were recognised as independent risk factors by multivariate Cox analyses. Kaplan-Meier survival curves showed that these 14 prognostic immune genes were closely related to the prognosis of ovarian cancer patients. A prognostic nomogram was developed by using these 14 prognostic immune genes. The concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3-, and 5-year overall survival, respectively. This prognostic model could differentiate high-risk patients with poor overall survival from low-risk patients. According to three artificial intelligence algorithms, the current study developed an artificial intelligence survival predictive system that could provide three individual mortality risk curves for ovarian cancer.

Conclusion: In conclusion, the current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes in ovarian cancer patients. Multivariate Cox analyses identified fourteen prognostic immune biomarkers for ovarian cancer. The current study constructed an immune regulatory network involving 63 immune genes and 5 transcription factors, revealing potential regulatory associations among immune genes and transcription factors. The current study developed a prognostic model to predict the prognosis of ovarian cancer patients. The current study further developed two artificial intelligence predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.io/Smart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_17_OC_F1001/. An artificial intelligence survival predictive system could help improve individualised treatment decision-making.

Establishment of a Novel Risk Score System of Immune Genes Associated With Prognosis in Esophageal Carcinoma

Background

Few studies have addressed the role of immune-related genes in the survival and prognosis of different esophageal cancer (EC) sub-types. We established two new prognostic model indexes by bioinformatics analysis to select patients with esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) who may benefit from immunotherapy.

Methods

Based on TCGA and ImmPort data sets, we screened immune genes differentially expressed between tumor and normal tissues in ESCC and EAC and analyzed the relationship between these genes and patient survival outcomes. We established the risk score models of immune-related genes in ESCC and EAC by multivariate COX regression analysis.

Results

We identified 12 and 11 immune-related differentially expressed genes associated with the clinical prognosis of ESCC and EAC respectively, based on which two prognostic risk score models of the two EC sub-types were constructed. It was found that the survival probability of patients with high scores was significantly lower than that of patients with low scores (p < 0.001). BMP1, EGFR, S100A12, HLA-B, TNFSF18, IL1B, MAPT and OXTR were significantly related to sex, TNM stage or survival outcomes of ESCC or EAC patients (p < 0.05). In addition, the risk score of ESCC was significantly correlated with the level of B cell infiltration in immune cells (p < 0.05).

Conclusions

The prognosis-related immune gene model indexes described herein prove to be useful prognostic biomarkers of the two EC sub-types in that they may provide a reference direction for looking for the beneficiaries of immunotherapy for EC patients.

Immune Interaction Map of Human SARS-CoV-2 Target Genes: Implications for Therapeutic Avenues

There exists increasing evidence that people with preceding medical conditions, such as diabetes and cancer, have a higher risk of infection with SARS-CoV-2 and are more vulnerable to severe disease. To get insights into the possible role of the immune system upon COVID-19 infection, 2811 genes of the gene ontology term “immune system process GO: 0002376” were selected for coexpression analysis of the human targets of SARS-CoV-2 (HT-SARS-CoV-2) ACE2, TMPRSS2, and FURIN in tissue samples from patients with cancer and diabetes mellitus. The network between HT-SARS-CoV-2 and immune system process genes was analyzed based on functional protein associations using STRING. In addition, STITCH was employed to determine druggable targets. DPP4 was the only immune system process gene, which was coexpressed with the three HT-SARS-CoV-2 genes, while eight other immune genes were at least coexpressed with two HT-SARS-CoV-2 genes. STRING analysis between immune and HT-SARS-CoV-2 genes plotted 19 associations of which there were eight common networking genes in mixed healthy (323) and pan-cancer (11003) tissues in addition to normal (87), cancer (90), and diabetic (128) pancreatic tissues. Using this approach, three commonly applicable druggable connections between HT-SARS-CoV-2 and immune system process genes were identified. These include positive associations of ACE2—DPP4 and TMPRSS2—SRC as well as a negative association of FURIN with ADAM17. Furthermore, 16 drugs were extracted from STITCH (score <0.8) with 32 target genes. Thus, an immunological network associated with HT-SARS-CoV-2 using bioinformatics tools was identified leading to novel therapeutic opportunities for COVID-19.

Development and Validation of an Immune-Related Gene-Pair Model of High-Grade Serous Ovarian Cancer After Platinum-Based Chemotherapy

Background

High-grade serous ovarian cancer (HGSOC) is a common cause of death from gynecological cancer, with an overall survival rate that has not significantly improved in decades. Reliable bio-markers are needed to identify high-risk HGSOC to assist in the selection and development of treatment options.

Method

The study included ten HGSOC cohorts, which were merged into four separate cohorts including a total of 1,526 samples. We used the relative expression of immune genes to construct the gene-pair matrix, and the least absolute shrinkage and selection operator regression was performed to build the prognosis model using the training set. The prognosis of the model was verified in the training set (363 cases) and three validation sets (of 251, 354, and 558 cases). Finally, the differences in immune cell infiltration and gene enrichment pathways between high and low score groups were identified.

Results

A prognosis model of HGSOC overall survival rate was constructed in the training set, and included data for 35 immune gene-related gene pairs and the regression coefficients. The risk stratification of HGSOC patients was successfully performed using the training set, with a p-value of Kaplan-Meier of < 0.001. A score from this model is an independent prognostic factor of HGSOC, and prognosis was evaluated in different clinical subgroups. This model was also successful for the other three validation sets, and the results of Kaplan-Meier analysis were statistically significant (p < 0.05). The model can also predict patient progression-free survival with HGSOC to reflect tumor growth status. There was a lower infiltration level of M1 macrophages in the high-risk group compared to that in the low-risk group (p < 0.001). Finally, the immune-related pathways were enriched in the low-risk group.

Conclusion

The prognostic model based on immune-related gene pairs developed is a potential prognostic marker for high-grade serous ovarian cancer treated with platinum. The model has robust prognostic ability and wide applicability. More prospective studies will be needed to assess the practical application of this model for precision therapy.

Effects of a blend of mannan and glucan on growth performance, apparent nutrient digestibility, energy status, and whole-blood immune gene expression of beef steers during a 42-d receiving period

We examined the effects of dietary supplementation of a blend of mannan and glucan on the growth performance, energy status, and whole-blood immune gene expression of newly weaned beef steers during a 42-d receiving period. Forty-eight newly weaned Angus crossbred steers (2-d post-weaning; 199 ± 13 kg of initial body weight [BW]) from a single source were stratified by BW and randomly assigned to one of the two treatments: basal diet with no additive (CON; n = 24) or a basal diet top-dressed with 5 g of a blend of mannan and glucan (MANGLU; n = 24). Average daily gain (ADG) and feed efficiency (FE) from days 1 to 14, 15 to 42, and 1 to 42 were calculated from daily dry matter intake (DMI) and weekly BW. Blood samples were collected on days 0, 14, and 42 for measurement of plasma glucose and nonesterified fatty acids (NEFA). Blood samples collected on days 14 and 42 were composited for each steer for untargeted carbonyl-metabolome analysis (measurement of carbonyl-containing metabolites). Expression of 84 immune-related genes was analyzed on blood samples collected on day 42. Beginning on days 37 to 42, total mixed ration, refusals, and fecal samples were collected once daily to determine apparent total tract digestibility of DM, CP, NDF, and ADF using indigestible NDF as an internal marker. Over the 42-d feeding trial, supplemental MANGLU tended to increase final BW (P = 0.07) and ADG (P = 0.06). Compared to CON, beef steers fed supplemental MANGLU had greater (P = 0.01) DMI during the first 14 d, greater DM digestibility (P = 0.03), and tended to have greater NDF digestibility (P = 0.09). No treatment effects (P > 0.10) on plasma glucose and NEFA on days 14 and 42 were detected; however, carbonyl-metabolome analysis revealed increased (FDR ≤ 0.05) plasma concentrations of galactose and glyceraldehydes, and altered (FDR ≤ 0.05) concentrations of some microbiome-derived metabolites in beef steers fed MANGLU. Compared with CON, MANGLU increased (P ≤ 0.05) the expression of five immune-related genes involved in recognition of and mounting immune defense against microbial pathogens. In conclusion, the results of this study demonstrated that supplemental MANGLU enhances beef cattle immunocompetence and productivity during feedlot receiving period.

Development of an Immune-Related Gene Signature for Prognosis in Melanoma

Melanoma remains a potentially deadly malignant tumor. The incidence of melanoma continues to rise. Immunotherapy has become a new treatment method and is widely used in a variety of tumors. Original melanoma data were downloaded from TCGA. ssGSEA was performed to classify them. GSVA software and the "hclust" package were used to analyze the data. The ESTIMATE algorithm screened DEGs. The edgeR package and Venn diagram identified valid immune-related genes. Univariate, LASSO and multivariate analyses were used to explore the hub genes. The "rms" package established the nomogram and calibrated the curve. Immune infiltration data were obtained from the TIMER database. Compared with that of samples in the high immune cell infiltration cluster, we found that the tumor purity of samples in the low immune cell infiltration cluster was higher. The immune score, ESTIMATE score and stromal score in the low immune cell infiltration cluster were lower. In the high immune cell infiltration cluster, the immune components were more abundant, while the tumor purity was lower. The expression levels of TIGIT, PDCD1, LAG3, HAVCR2, CTLA4 and the HLA family were also higher in the high immune cell infiltration cluster. Survival analysis showed that patients in the high immune cell infiltration cluster had shorter OS than patients in the low immune cell infiltration cluster. IGHV1-18, CXCL11, LTF, and HLA-DQB1 were identified as immune cell infiltration-related DEGs. The prognosis of melanoma was significantly negatively correlated with the infiltration of CD4+ T cells, CD8+ T cells, dendritic cells, neutrophils and macrophages. In this study, we identified immune-related melanoma core genes and relevant immune cell subtypes, which may be used in targeted therapy and immunotherapy of melanoma.

A novel immunogenomic prognostic signature in lung squamous carcinoma

Lung squamous carcinoma (LUSC) is a common subtype of lung cancer with limited available therapy and is thus associated with poor survival. Immune infiltrating cells and immune-related genes (IRGs) play a key role in the clinical outcomes of LUSC. In the present study, we aimed to develop a potential immunogenomic prognostic signature for patients with LUSC. The transcriptional profiles of 501 LUSC samples from The Cancer Genome Atlas (TCGA) and 2498 IRGs from the ImmPort database were used to develop the signature by Cox regression analysis. Ten differentially expressed and survival-associated IRGs were used to develop the risk signature, which could serve as an independent prognostic and predictive factor for patients with LUSC. Furthermore, this risk signature correlated with overall survival and clinical features, including age, in patients with LUSC. In addition, we identified 25 transcription factors that may regulate 15 survival-associated IRGs, using a regulatory network. Collectively, this immunogenomic signature could be a robust prognostic tool for patients with LUSC and holds great promise as individualized immunotherapy for LUSC.

The Combined Detection of Immune Genes for Predicting the Prognosis of Patients With Non-Small Cell Lung Cancer

Lung cancer is one of the leading causes of cancer-related death. In recent years, there has been an increasing interest in the fields of tumor and immunity. This study focused on the possible prognostic value of immune genes in non-small cell lung cancer patients. We used The Cancer Genome Atlas (TCGA) to download gene expression data and clinical information of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). The immune gene list was downloaded from the Immport database. We then constructed immune gene prognostic models on the basis of Cox regression analysis. We further evaluated the clinical significance of the models via survival analysis, receiver operating characteristic (ROC) curves, and independent prognostic factor analysis. Moreover, we analyzed the associations of prognostic models with both mutation burdens and neoantigens. Using the Gene Expression Omnibus (GEO) and Kaplan-Meier plotter databases, we evaluated the validity of the prognostic models. The prognostic model of LUAD included 13 immune genes, and the prognostic model of LUSC contained 10 immune genes. High-risk patients based on prognostic models had a lower 5-year survival rate than did low-risk patients. The ROC curve analysis demonstrated the prediction accuracy of the prognostic models, as the area under the curve (AUC) was 0.742, 0.707, and 0.711 for LUAD, and 0.668, 0.703, and 0.668 for LUSC, when the predicted survival times were 1, 3, and 5 years, respectively. The mutation burden analysis showed that mutation level was associated with the risk score in patients with LUAD. The analysis based on GEO and Kaplan-Meier plotter demonstrated the prognostic validity of the models. Therefore, immune gene-related models of LUAD and LUSC can predict prognosis. Further study of these genes may enable us to better distinguish between LUAD and LUSC and lead to improvement in immunotherapy for lung cancer.

Temporospatial modulation of Lymantria dispar immune system against an entomopathogenic fungal infection

BACKGROUND

Lymantria dispar is an economically impactful forest pest worldwide. The entomopathogenic fungi Beauveria bassiana shows great promise in pest management due to its high lethality in Lymantria dispar. A complete understanding of the immune interactions between the pest and the pathogenic fungus is essential to actualizing biological pest management.

RESULTS

Following the infection of Lymantria dispar by Beauveria bassiana spores, we performed a time-course analysis of transcriptome in Lymantria dispar fat bodies and hemocytes to explore host immune response. A total of 244 immunity-related genes including pattern recognition receptors, extracellular signal modulators, immune pathways (Toll, IMD, JNK and JAK/STAT), and response effectors were identified. We observed contrasting tissue and time-specific differences in the expression of immune genes. At the early stage of infection, several recognition receptors and effector genes were activated, while the signal modulation and effector genes were suppressed at later stages. Further enzyme activity-based assays coupled with gene expression analysis of prophenoloxidase revealed a significant upregulation of phenoloxidase activity at 48- and 72-h post-infection. Moreover, fungal infection led to dysbiosis in gut microbiota that seems to be partially attributed to reduced gut hydrogen peroxide (H₂ O₂ ) amount, which indicates a significant impact of fungal infection on host gut microbes.

CONCLUSION

Our study provides a comprehensive sequence resource and crucial new insights about an economically important forest pest. Specifically, we elucidate the complicated multipartite interaction between host and fungal pathogen and contribute to a better understanding of Lymantria dispar anti-fungal immunity, resulting in better tools for biological pest control. © 2020 Society of Chemical Industry.

Interaction network of immune-associated genes affecting the prognosis of patients with glioblastoma

Glioblastoma multiforme (GBM) is a common malignant tumor type of the nervous system. The purpose of the present study was to establish a regulatory network of immune-associated genes affecting the prognosis of patients with GBM. The GSE4290, GSE50161 and GSE2223 datasets from the Gene Expression Omnibus database were screened to identify common differentially expressed genes (co-DEGs). A functional enrichment analysis indicated that the co-DEGs were mainly enriched in cell communication, regulation of enzyme activity, immune response, nervous system, cytokine signaling in immune system and the AKT signaling pathway. The co-DEGs accumulated in immune response were then further investigated. For this, the intersection of those co-DEGs and currently known immune-regulatory genes was obtained and a differential expression analysis of these overlapping immune-associated genes was performed. A risk model was established using immune-regulatory genes that affect the prognosis of patients with GBM. The risk score was significantly associated with the prognosis of patients with GBM and had a significant independent predictive value. The risk model had high accuracy in predicting the prognosis of patients with GBM [area under the receiver operating characteristic curve (AUC)=0.764], which was higher than that of a previously reported model of prognosis-associated biomarkers (AUC=0.667). Furthermore, an interaction network was constructed by using immune-regulatory genes and transcription factors affecting the prognosis of patients with GBM and the University of California Santa Cruz database was used to perform a preliminary analysis of the transcription factors and immune genes of interest. The interaction network of immune-regulatory genes constructed in the present study enhances the current understanding of mechanisms associated with poor prognosis of patients with GBM. The risk score model established in the present study may be used to evaluate the prognosis of patients with GBM.

Identification of prognosis-associated immune genes and exploration of immune cell infiltration in colorectal cancer

Aim: To identify prognosis-related immune genes (PRIGs) and construct a prognosis model of colorectal cancer (CRC) patients for clinical use. Materials & methods: Expression profiles were obtained from The Cancer Genome Atlas database and identified differentially expressed PRIGs of CRC. Results: A prognostic model was conducted based on nine PRIGs. The risk score, based on prognosis model, was an independent prognostic predictor. Five PRIGs and risk score were significantly associated with the clinical stage of CRC and five immune cells related to the risk score. Conclusion: The risk score was an independent prognostic biomarker for CRC patients. The research excavated immune genes that were associated with survival and that could be potential biomarkers for prognosis and treatment for CRC patients.

Development and Validation of a Novel 8 Immune Gene Prognostic Signature Based on the Immune Expression Profile for Hepatocellular Carcinoma

Background

The immune microenvironment plays a vital role in the development of hepatocellular carcinoma (HCC). This study explored novel immune-related biomarkers to predict the prognosis of patients with HCC.

Methods

RNA-Seq data were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to identify prognosis-related genes; the Lasso method was used to construct the prognosis risk model. Validation was performed on the International Cancer Genome Consortium (ICGC) cohort, and the C-index was calculated to evaluate its overall predictive performance. Western blots were conducted to evaluate the expression of genes.

Results

There were 320 immune-related genes, 40 of which were significantly related to prognosis. Eight immune gene signatures (CKLF, IL12A, CCL20, PRELID1, GLMN, ACVR2A, CD7, and FYN) were established by Lasso Cox regression analysis. This immune signature performed well in different cohorts and can be an independent risk factor for prognosis. In addition, the overall predictive performance of this model was higher than the other models reported previously.

Conclusion

The predictive immune model will enable patients with HCC to be more accurately managed in immunotherapy.

Identification of Immune Regulatory Genes in Apis mellifera through Caffeine Treatment

Plants and pollinators are mutually beneficial: plants provide nectar as a food source and in return their pollen is disseminated by pollinators such as honeybees. Some plants secrete chemicals to deter herbivores as a protective measure, among which is caffeine, a naturally occurring, bitter tasting, and pharmacologically active secondary compound. It can be found in low concentrations in the nectars of some plants and as such, when pollinators consume nectar, they also take in small amounts of caffeine. Whilst caffeine has been indicated as an antioxidant in both mammals and insects, the effect on insect immunity is unclear. In the present study, honeybees were treated with caffeine and the expression profiles of genes involved in immune responses were measured to evaluate the influence of caffeine on immunity. In addition, honeybees were infected with deformed wing virus (DWV) to study how caffeine affects their response against pathogens. Our results showed that caffeine can increase the expression of genes involved in immunity and reduce virus copy numbers, indicating that it has the potential to help honeybees fight against viral infection. The present study provides a valuable insight into the mechanism by which honeybees react to biotic stress and how caffeine can serve as a positive contributor, thus having a potential application in beekeeping.

Bioinformatics Analysis Finds Immune Gene Markers Related to the Prognosis of Bladder Cancer

Bladder cancer is one of the most common malignant tumors of the urinary system that seriously threatens the health of a population. In recent years, the application of immunotherapy has significantly changed the treatment of bladder cancer, but only some patients can benefit from the treatment with immune-checkpoint inhibitors. Many problems are unsolved in the field of bladder cancer immunotherapy, especially in the search for genes that are critical to the level of immune cell infiltration and new effective therapeutic targets. We attempted to use bioinformatics analysis to identify immune gene markers related to the prognosis of bladder cancer and to establish a prognostic signature for bladder cancer patients based on their immune gene expression profiles. We used univariate Cox proportional hazards regression analysis, the least absolute shrinkage and selection operator (LASSO) Cox regression, and multivariate Cox proportional hazards regression analysis from The Cancer Genome Atlas bladder cancer cohort (TCGA-BLCA). Fifteen genes related to prognosis were screened using the survival analysis, correlation analysis, cancer and adjacent cancer differential expression analysis, and mutation analysis. The potential biological role of these genes was determined using survival analysis and principal component analysis (PCA). The receiver operating characteristic (ROC) curve assesses the prognostic value of the predictive signature. The gene ontology (GO), Kyoto Encyclopedia of Gene and Genome (KEGG), Gene set enrichment analysis (GSEA), and other methods were used to reveal the differential gene enrichment in the signaling pathways and cellular processes of high- and low-risk groups. The single-sample GSEA (ssGSEA) method was used to quantify the infiltration levels of 24 immune cells in the tumor immune microenvironment and these immune genes were found to be closely related to the tumor immune microenvironment. In summary, we screened 15 immune genes that were closely related to bladder cancer overall survival (OS) and may be potential prognostic indicators of bladder cancer. They may have research and clinical application value in bladder cancer immunotherapy. We used 15 immune genes to construct a new immune-related gene signature that was verified and could be helpful in improving individualized prognosis of patients with bladder cancer.

High MHC gene copy number maintains diversity despite homozygosity in a Critically Endangered single-island endemic bird, but no evidence of MHC-based mate choice

Small population sizes can, over time, put species at risk due to the loss of genetic variation and the deleterious effects of inbreeding. Losing diversity in the major histocompatibility complex (MHC) could be particularly harmful, given its key role in the immune system. Here, we assess MHC class I (MHC-I) diversity and its effects on mate choice and survival in the Critically Endangered Raso lark Alauda razae, a species restricted to the 7 km² islet of Raso, Cape Verde, since ~1460, whose population size has dropped as low as 20 pairs. Exhaustively genotyping 122 individuals, we find no effect of MHC-I genotype/diversity on mate choice or survival. However, we demonstrate that MHC-I diversity has been maintained through extreme bottlenecks by retention of a high number of gene copies (at least 14), aided by cosegregation of multiple haplotypes comprising 2-8 linked MHC-I loci. Within-locus homozygosity is high, contributing to low population-wide diversity. Conversely, each individual had comparably many alleles, 6-16 (average 11), and the large and divergent haplotypes occur at high frequency in the population, resulting in high within-individual MHC-I diversity. This functional immune gene diversity will be of critical importance for this highly threatened species' adaptive potential.

Analysis of expression differences of immune genes in non-small cell lung cancer based on TCGA and ImmPort data sets and the application of a prognostic model

Background

There has been little investigation carried out into the activity of immune-related genes in the prognosis of non-small cell lung cancer (NSCLC). Our study set out to analyze the correlation between the differential expression of immune genes and NSCLC prognosis by screening the differential expression of immune genes. Based on the immune genes identified, we aimed to construct a prognostic risk model and explore some novel molecules which have predictive potential for therapeutic effect and prognosis in lung cancer.

Methods

Immune gene transcriptome data and clinical data of NSCLC samples were extracted from TCGA database, and transcription factors in the ImmPort dataset were obtained. The data were divided into two groups: normal tissues and tumor tissues. The expression levels of immune genes were compared using the edgeR algorithm, and then differential expression analysis was performed. The survival analysis was carried out by combining differential immune genes with clinical survival time, so that the immune genes influencing the prognosis of NSCLC could be determined. A risk score was calculated based on the expression levels of the immune genes related to the prognosis of NSCLC and their corresponding coefficients to construct a prognostic risk model. This model was used to calculate patient risk scores and perform clinical correlation analysis. The selected molecules were further verified by clinical samples.

Results

By comparing NSCLC tissues with normal tissues, a total of 6,778 differentially expressed genes were found (P<0.05), of which 490 were differential immune-related genes. Survival analysis determined 28 differential immune genes to be associated with prognosis (P<0.05). We calculated the patient risk value based on the immune gene prognosis model. The survival curve was drawn according to the patient risk score and showed that the survival prognosis was significantly different for the high-risk and the low-risk groups (P<0.05). The area under the receiver operating characteristic (ROC) curve (AUC) was 0.723, which represented a relatively high true-positive rate. All of the results proved the reliability of our immune gene risk prognostic model. After drawing the risk curve, S100A16, IGKV4, S100P, ANGPTL4, SEMA4B, and LGR4 were found to be the high-risk immune genes in NSCLC. Clinical correlation analysis of survival-related differential immune genes revealed that in patients with lymph node metastasis, ANGPTL4 was positively correlated with T stage, S100a16 and SEMA4B were upregulated, and VIPR1 was downregulated. Further analysis revealed that VIPR1 was decreased in metastatic lung cancer compared to non-metastatic lung cancer. Furthermore, the real-time PCR detection of the clinical samples showed that S100A16 expression in lung cancer was increased, while VIPR1 expression in lung cancer was downregulated, which was consistent with the results of our bioinformatics analysis.

Conclusions

Based on big data from the TCGA and ImmPort databases, our study analyzed the relationship between differential expression of immune-related genes and clinical data, and constructed a prognostic model based on the immune genes identified. Two novel molecules, S100A16 and VIPR1, were verified to possibly have significant biological function in NSCLC. Our research may provide us with new insight into the immune genes by which the malignant biological behavior of NSCLC is mediated.

Bioinformatics Identified 17 Immune Genes as Prognostic Biomarkers for Breast Cancer: Application Study Based on Artificial Intelligence Algorithms

An increasing body of evidence supports the association of immune genes with tumorigenesis and prognosis of breast cancer (BC). This research aims at exploring potential regulatory mechanisms and identifying immunogenic prognostic markers for BC, which were used to construct a prognostic signature for disease-free survival (DFS) of BC based on artificial intelligence algorithms. Differentially expressed immune genes were identified between normal tissues and tumor tissues. Univariate Cox regression identified potential prognostic immune genes. Thirty-four transcription factors and 34 immune genes were used to develop an immune regulatory network. The artificial intelligence survival prediction system was developed based on three artificial intelligence algorithms. Multivariate Cox analyses determined 17 immune genes (ADAMTS8, IFNG, XG, APOA5, SIAH2, C2CD2, STAR, CAMP, CDH19, NTSR1, PCDHA1, AMELX, FREM1, CLEC10A, CD1B, CD6, and LTA) as prognostic biomarkers for BC. A prognostic nomogram was constructed on these prognostic genes. Concordance indexes were 0.782, 0.734, and 0.735 for 1-, 3-, and 5- year DFS. The DFS in high-risk group was significantly worse than that in low-risk group. Artificial intelligence survival prediction system provided three individual mortality risk predictive curves based on three artificial intelligence algorithms. In conclusion, comprehensive bioinformatics identified 17 immune genes as potential prognostic biomarkers, which might be potential candidates of immunotherapy targets in BC patients. The current study depicted regulatory network between transcription factors and immune genes, which was helpful to deepen the understanding of immune regulatory mechanisms for BC cancer. Two artificial intelligence survival predictive systems are available at https://zhangzhiqiao7.shinyapps.io/Smart_Cancer_Survival_Predictive_System_16_BC_C1005/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_16_BC_C1005/. These novel artificial intelligence survival predictive systems will be helpful to improve individualized treatment decision-making.

Comparative susceptibility and immune responses of Asian and European honey bees to the American foulbrood pathogen, Paenibacillus larvae

American foulbrood (AFB) disease is caused by Paenibacillus larvae. Currently, this pathogen is widespread in the European honey bee-Apis mellifera. However, little is known about infectivity and pathogenicity of P. larvae in the Asiatic cavity-nesting honey bees, Apis cerana. Moreover, comparative knowledge of P. larvae infectivity and pathogenicity between both honey bee species is scarce. In this study, we examined susceptibility, larval mortality, survival rate and expression of genes encoding antimicrobial peptides (AMPs) including defensin, apidaecin, abaecin, and hymenoptaecin in A. mellifera and A. cerana when infected with P. larvae. Our results showed similar effects of P. larvae on the survival rate and patterns of AMP gene expression in both honey bee species when bee larvae are infected with spores at the median lethal concentration (LC₅₀ ) for A. mellifera. All AMPs of infected bee larvae showed significant upregulation compared with noninfected bee larvae in both honey bee species. However, larvae of A. cerana were more susceptible than A. mellifera when the same larval ages and spore concentration of P. larvae were used. It also appears that A. cerana showed higher levels of AMP expression than A. mellifera. This research provides the first evidence of survival rate, LC₅₀ and immune response profiles of Asian honey bees, A. cerana, when infected by P. larvae in comparison with the European honey bee, A. mellifera.

The Influence of Allogrooming Behavior on Individual Innate Immunity in the Subterranean Termite Reticulitermes chinensis (Isoptera: Rhinotermitidae)

Insect societies have evolved a series of disease defenses against pathogens, including social sanitary behavior and individual innate immunity. However, whether sanitary behavior can affect individual innate immunity remains unknown. Here, we exposed the termite Reticulitermes chinensis Snyder to the entomopathogenic fungus Metarhizium anisopliae (Metchnikoff) Sorokin(Ascomycota: Hypocreales), and then measured their allogrooming behavior, conidia load, infection mortality, antifungal activity and immune gene expressions . Our results showed that most of the fungal conidia were fast removed from the cuticles of the grouped termites by intensive allogrooming behavior, resulting in low mortality. The antifungal activity and immune gene expressions (termicin and transferrin) in grouped exposed termites were significantly lower than those in single exposed termite but not significantly different from those in unexposed treatments. These results suggest that allogrooming behavior can fast remove fungal conidia from termite cuticles and then decrease their physiological investment in individual innate immunity.

Whole-body transcriptome of selectively bred, resistant-, control-, and susceptible-line rainbow trout following experimental challenge with Flavobacterium psychrophilum

Genetic improvement for enhanced disease resistance in fish is an increasingly utilized approach to mitigate endemic infectious disease in aquaculture. In domesticated salmonid populations, large phenotypic variation in disease resistance has been identified but the genetic basis for altered responsiveness remains unclear. We previously reported three generations of selection and phenotypic validation of a bacterial cold water disease (BCWD) resistant line of rainbow trout, designated ARS-Fp-R. This line has higher survival after infection by either standardized laboratory challenge or natural challenge as compared to two reference lines, designated ARS-Fp-C (control) and ARS-Fp-S (susceptible). In this study, we utilized 1.1 g fry from the three genetic lines and performed RNA-seq to measure transcript abundance from the whole body of naive and Flavobacterium psychrophilum infected fish at day 1 (early time-point) and at day 5 post-challenge (onset of mortality). Sequences from 24 libraries were mapped onto the rainbow trout genome reference transcriptome of 46,585 predicted protein coding mRNAs that included 2633 putative immune-relevant gene transcripts. A total of 1884 genes (4.0% genome) exhibited differential transcript abundance between infected and mock-challenged fish (FDR < 0.05) that included chemokines, complement components, tnf receptor superfamily members, interleukins, nod-like receptor family members, and genes involved in metabolism and wound healing. The largest number of differentially expressed genes occurred on day 5 post-infection between naive and challenged ARS-Fp-S line fish correlating with high bacterial load. After excluding the effect of infection, we identified 21 differentially expressed genes between the three genetic lines. In summary, these data indicate global transcriptome differences between genetic lines of naive animals as well as differentially regulated transcriptional responses to infection.

Targeting of chromatin readers: a novel strategy used by the Shigella flexneri virulence effector OspF to reprogram transcription

Shigella flexneri, a gram-negative bacterium responsible of bacillary dysentery, uses multiple strategies to overcome host immune defense. We have decrypted how this bacterium manipulates host-cell chromatin binders to take control of immune gene expression. We found that OspF, an injected virulence factor previously identified as a repressor of immune gene expression, targets the chromatin reader HP1γ. Heterochromatin Protein 1 family members specifically recognize and bind histone H3 methylated at Lys 9. Although initially identified as chromatin-associated transcriptional silencers in heterochromatin, their location in euchromatin indicates an active role in gene expression. Notably, HP1γ phosphorylation at Serine 83 defines a subpopulation exclusively located to euchromatin, targeted to the site of transcriptional elongation. We showed that OspF directly interacts with HP1γ, and causes HP1 dephosphorylation, suggesting a model in which this virulence effector “uses” HP1 proteins as beacons to target and repress immune gene expression (Harouz, et al. EMBO J (2014)). OspF alters HP1γ phosphorylation mainly by inactivating the Erk-activated kinase MSK1, spotlighting it as a new HP1 kinase. In vivo, infectious stresses trigger HP1γ phosphorylation in the colon, principally in the lamina propria and the intestinal crypts. Several lines of evidence suggest that HP1 proteins are modified as extensively as histones, and decrypting the impact of these HP1 post-translational modifications on their transcriptional activities in vivo will be the next challenges to be taken up.

Evidence for population-specific positive selection on immune genes of Anopheles gambiae

Host-pathogen interactions can be powerful drivers of adaptive evolution, shaping the patterns of molecular variation at the genes involved. In this study, we sequenced alleles from 28 immune-related loci in wild samples of multiple genetic subpopulations of the African malaria mosquito Anopheles gambiae, obtaining unprecedented sample sizes and providing the first opportunity to contrast patterns of molecular evolution at immune-related loci in the recently discovered GOUNDRY population to those of the indoor-resting M and S molecular forms. In contrast to previous studies that focused on immune genes identified in laboratory studies, we centered our analysis on genes that fall within a quantitative trait locus associated with resistance to Plasmodium falciparum in natural populations of A. gambiae. Analyses of haplotypic and genetic diversity at these 28 loci revealed striking differences among populations in levels of genetic diversity and allele frequencies in coding sequence. Putative signals of positive selection were identified at 11 loci, but only one was shared by two subgroups of A. gambiae. Striking patterns of linkage disequilibrium were observed at several loci. We discuss these results with respect to ecological differences among these strata as well as potential implications for disease transmission.