Integrated network toxicology, molecular docking, and in vivo experiments to elucidate molecular mechanism of aflatoxin B1 hepatotoxicity.

Due to the rise in temperature and sea level caused by climate change, the detection rate of aflatoxin B1 (AFB1) in food crops has increased dramatically, and the frequency and severity of aflatoxicosis in humans and animals are also increasing. AFB1 has strong hepatotoxicity, causing severe liver damage and even cancer. However, the mechanism of AFB1 hepatotoxicity remains unclear. By integrating network toxicology, molecular docking and in vivo experiments, this research was designed to explore the potential hepatotoxicity mechanisms of AFB1. Thirty-three intersection targets for AFB1-induced liver damage were identified using online databases. PI3K/AKT1, MAPK, FOXO1 signaling pathways, and apoptosis were significantly enriched. In addition, the proteins of ALB, AKT1, PIK3CG, MAPK8, HSP90AA1, PPARA, MAPK1, EGFR, FOXO1, and IGF1 exhibited good affinity with AFB1. In vivo experiments, significant pathological changes occurred in the liver of mice. AFB1 induction increased the expression levels of EGFR, ERK, and FOXO1, and decreased the expression levsls of PI3K and AKT1. Moreover, AFB1 treatment caused an increase in Caspase3 expression, and a decrease in Bcl2/Bax ratio. By combining network toxicology with in vivo experiments, this study confirms for the first time that AFB1 promotes the FOXO1 signaling pathway by inactivating PI3K/AKT1 and activating EGFR/ERK signaling pathways, hence aggravating hepatocyte apoptosis. This research provides new strategies for studying the toxicity of environmental pollutants and new possible targets for the development of hepatoprotective drugs.

A novel metallothionein gene HcMT from halophyte shrub Halostachys caspica respond to cadmium and sodium stress.

Cadmium (Cd) and sodium (Na) are two of the most phytotoxic metallic elements causing environmental and agricultural problems. Metallothioneins (MTs) play an important role in the adaptation to abiotic stress. We previously isolated a novel type 2 MT gene from Halostachys caspica (H. caspica), named HcMT, which responded to metal and salt stress. To understand the regulatory mechanisms controlling HcMT expression, we cloned the HcMT promoter and characterized its tissue-specific and spatiotemporal expression patterns. ß-Glucuronidase (GUS) activity analysis showed that the HcMT promoter was responsive to CdCl2, CuSO4, ZnSO4 and NaCl stress. Therefore, we further investigated the function of HcMT under abiotic stress in yeast and Arabidopsis thaliana (Arabidopsis). In CdCl2, CuSO4 or ZnSO4 stress, HcMT significantly enhanced the metal ions tolerance and accumulation in yeast through function as a metal chelator. Moreover, the HcMT protein also protected yeast cells from NaCl, PEG and hydrogen peroxide (H2O2) toxicity with less effectiveness. However, transgenic Arabidopsis carrying HcMT gene only displayed tolerance to CdCl2 and NaCl, accompanying by higher content of Cd2+ or Na+ and lower H2O2, compared to wild-type (WT) plants. Next, we demonstrated that the recombinant HcMT protein has the ability to bind Cd2+ and the potential of scavenging ROS (reactive oxygen species) in vitro. This result further confirmed that the role of HcMT to influence plants to CdCl2 and NaCl stress may bind metal ions and scavenge ROS. Overall, we described the biological functions of HcMT and developed a metal- and salt-inducible promoter system for using in genetic engineering.

Protection of taraxasterol against acetaminophen-induced liver injury elucidated through network pharmacology and in vitro and in vivo experiments.

BACKGROUND: Drug-induced liver injury (DILI) is primarily caused by drugs or their metabolites. Acetaminophen (APAP) is an over-the-counter antipyretic analgesic that exhibits high hepatotoxicity when used for long-term or in overdoses. Taraxasterol is a five-ring triterpenoid compound extracted from traditional Chinese medicinal herb Taraxacum officinale. Our previous studies have demonstrated that taraxasterol exerts protective effects on alcoholic and immune liver injuries. However, the effect of taraxasterol on DILI remains unclear. HYPOTHESIS/PURPOSE: This study aimed to elucidate the effects and mechanisms of action of taraxasterol on APAP-induced liver injury using network pharmacology and in vitro and in vivo experiments. METHODS: Online databases of drug and disease targets were used to screen the targets of taraxasterol and DILI, and a protein-protein interaction network (PPI) was constructed. Core target genes were identified using the tool of Analyze of Cytoscape, gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses were performed. Oxidation, inflammation and apoptosis were evaluated to determine the effect of taraxasterol on APAP-stimulated liver damage in AML12 cells and mice. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting were used to explore the potential mechanisms of taraxasterol against DILI. RESULTS: Twenty-four intersection targets for taraxasterol and DILI were identified. Among them, 9 core targets were identified. GO and KEGG analysis showed that core targets are closely related to oxidative stress, apoptosis, and inflammatory response. The in vitro findings showed that taraxasterol alleviated mitochondrial damage in AML12 cells treated with APAP. The in vivo results revealed that taraxasterol alleviated pathological changes in the livers of mice treated with APAP and inhibited the activity of serum transaminases. Taraxasterol increased the activity of antioxidants, inhibited the production of peroxides, and reduced inflammatory response and apoptosis in vitro and in vivo. Taraxasterol promoted Nrf2 and HO-1 expression, suppressed JNK phosphorylation, and decreased the Bax/Bcl-2 ratio and caspase-3 expression in AML12 cells and mice. CONCLUSION: By integrating network pharmacology with in vitro and in vivo experiments, this study indicated that taraxasterol inhibits APAP-stimulated oxidative stress, inflammatory response and apoptosis in AML12 cells and mice by regulating the Nrf2/HO-1 pathway, JNK phosphorylation, and apoptosis-related protein expression. This study provides a new evidence for the use of taraxasterol as a hepatoprotective drug.

Assessment of shape-based features ability to predict the ascending aortic aneurysm growth.

The current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a method to compute a set of local shape features that, in addition to the maximum diameter D, are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment. Apart from D, these are the ratio DCR between D and the length of the ascending aorta centerline, the ratio EILR between the length of the external and the internal lines and the tortuosity T. 50 patients with two 3D acquisitions at least 6 months apart were segmented and the growth rate (GR) with the shape features related to the first exam computed. The correlation between them has been investigated. After, the dataset was divided into two classes according to the growth rate value. We used six different classifiers with input data exclusively from the first exam to predict the class to which each patient belonged. A first classification was performed using only D and a second with all the shape features together. The performances have been evaluated by computing accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC) and positive (negative) likelihood ratio LHR+ (LHR-). A positive correlation was observed between growth rate and DCR (r = 0.511, p = 1.3e-4) and between GR and EILR (r = 0.472, p = 2.7e-4). Overall, the classifiers based on the four metrics outperformed the same ones based only on D. Among the diameter-based classifiers, k-nearest neighbours (KNN) reported the best accuracy (86%), sensitivity (55.6%), AUROC (0.74), LHR+ (7.62) and LHR- (0.48). Concerning the classifiers based on the four shape features, we obtained the best accuracy (94%), sensitivity (66.7%), specificity (100%), AUROC (0.94), LHR+ (+∞) and LHR- (0.33) with support vector machine (SVM). This demonstrates how automatic shape features detection combined with risk classification criteria could be crucial in planning the follow-up of patients with ascending aortic aneurysm and in predicting the possible dangerous progression of the disease.

Taraxasterol alleviates aflatoxin B1-induced liver damage in broiler chickens via regulation of oxidative stress, apoptosis and autophagy.

Aflatoxin B1 (AFB1) is the most dangerous and abundant mycotoxin, which is toxic to almost all animals, and poultry is more sensitive to AFB1 toxicity. Ingesting AFB1-contaminated feed can cause significant liver damage and brings serious harm to poultry, which greatly restricts the development of the poultry industry. The present research was implemented to explore the intervention effect and its mechanism of taraxasterol on liver damage induced by AFB1 in broiler chickens. The liver damage model in broiler chickens was established by feeding 0.5 mg/kg AFB1 feed, and taraxasterol (25, 50 and 100 mg/kg BW, respectively) was given in the drinking water for 21 days. The growth performance, liver function, oxidative stress, apoptosis and autophagy were evaluated. The results showed that taraxasterol increased BW and reduced feed-to-gain ratio of broiler chickens induced by AFB1. Taraxasterol improved the levels of serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyltransferase (GGT), total bilirubin (TBIL) and alkaline phosphatase (ALP), and attenuated hepatic histopathological changes induced by AFB1. Meantime, taraxasterol down-regulated cytochrome P450 (CYP450) enzyme system CYP1A1 and CYP2A6 mRNA expression, inhibited the overproduction of reactive oxygen species (ROS) and malondialdehyde (MDA), and enhanced the activities of antioxidant enzymes glutathione (GSH) and catalase (CAT) and the content of antioxidant superoxide dismutase (SOD) of the liver in broiler chickens induced by AFB1. Furthermore, taraxasterol up-regulated the mRNA and protein expression of hepatic nuclear factor E2 related factor 2 (Nrf2), heme oxygenase 1 (HO-1) and NAD(P)H: quinone oxidoreductase 1 (NQO1), and down-regulated the expression of hepatic kelch like ECH associated protein 1 (Keap1) induced by AFB1 in Keap1/Nrf2 signaling pathway. The ultrastructural observation and RT-qPCR results found that taraxasterol inhibited apoptosis of hepatocytes, up-regulated the expression of B-cell lymphoma-2 (Bcl-2) mRNA and down-regulated the expression of Bax and caspase3 mRNA. Further, taraxasterol restored the autophagy of hepatocytes and down-regulated the mRNA expression of phosphatidylinositol 3-kinase K (PI3K), protein kinase B (AKT) and mammalian target of rapamycin (mTOR) in AFB1-induced liver of broiler chickens. The above results indicate that taraxasterol alleviates liver damage induced by AFB1 in broiler chickens through regulation of Keap1/Nrf2 signaling pathway to exert its antioxidant effect, mitochondrial apoptosis pathway to improve anti-apoptotic ability and PI3K/AKT/mTOR pathway to restore autophagy.

IP-score correlated to endogenous tumour antigen peptide processing: A candidate clinical response score algorithm of immune checkpoint inhibitors therapy in multiple cohorts.

The processing of endogenous tumour antigen peptides was essential for anti-tumour immunity in the tumour microenvironment. A high degree of Endogenous tumour antigen peptide processing has been demonstrated to improve the prognosis of carcinoma patients. However, there is insufficient evidence to prove its effect on the clinical response to immune checkpoint inhibitor therapy. To undertake a more in-depth analysis of the effects of the aforementioned genes on immunotherapy, we constructed a gene set evaluation score system relevant to tumour endogenous antigen peptide therapy using the GSVA approach. This rating mechanism is known as IP score (IPs). Immediately afterwards, we used the TCGA pan-cancer cohorts to conduct a comprehensive analysis of 6 genes in the IPs, and the analysis results showed that these six genes were related to the proportion of CD8+ T lymphocytes in a variety of solid tumours. As a prognostic protective factor for solid tumours, patients had better prognosis outcomes in the group with high expression levels of the above genes. We analysed the differential expression of six genes between immune checkpoint inhibitor treatment response and disease progression groups using several treatment cohorts. The results revealed that after treatment with PD-1 or CTLA4 inhibitors, the expression levels of the above six genes were comparatively high in the effective group, but the expression of the signature genes was dramatically downregulated in the ICI-insensitive groups. This indicates that the 6 genes are related to the clinical response to ICI treatment. Finally, we used the GSVA method to evaluate the above signatures, and the results showed that PDCD1, CTAL4, CD274 and LAG3 were significantly higher expressed in the IPs high-expression group; therefore, based on the processing of endogenous antigenic peptides in tumours, a predictive score of clinical response to immune checkpoint inhibitor therapy composed of 6 genes(PSMB8/PSMB9/PSMB10/PSME1/PSME2/IRF1) was constructed, and the role of each independent variable in the signature in the solid tumour microenvironment and the impact on ICI treatment were comprehensively analysed. This study provides a candidate evaluation score for predicting clinical response to immune checkpoint inhibitor therapy.

9-Gene Signature Correlated With CD8+ T Cell Infiltration Activated by IFN-γ: A Biomarker of Immune Checkpoint Therapy Response in Melanoma.

Purpose: To identify CD8+ T cell-related factors and the co-expression network in melanoma and illustrate the interactions among CD8+ T cell-related genes in the melanoma tumor microenvironment. Method: We obtained melanoma and paracancerous tissue mRNA matrices from TCGA-SKCM and GSE65904. The CIBERSORT algorithm was used to assess CD8+ T cell proportions, and the "estimate" package was used to assess melanoma tumor microenvironment purity. Weighted gene co-expression network analysis was used to identify the most related co-expression modules in TCGA-SKCM and GSE65904. Subsequently, a co-expression network was built based on the joint results in the two cohorts. Subsequently, we identified the core genes of the two most relevant modules of CD8+T lymphocytes according to the module correlation, and constructed the signature using ssGSEA. Later, we compared the signature with the existing classical pathways and gene sets, and confirmed the important prognostic significance of the signature in this paper. Results: Nine co-expressed genes were identified as CD8+ T cell-related genes enriched in the cellular response to interferon-gamma process and antigen processing and presentation of peptide antigen. In the low expression level group, inflammation and immune responses were weaker. Single-cell sequencing and immunohistochemistry indicated that these nine genes were highly expressed in CD8+ T cells group. Conclusion: We identified nine-gene signature, and the signature is considered as the biomarker for T lymphocyte response and clinical response to immune checkpoint inhibitors for melanoma.

Neuroinvasiveness of the MR766 strain of Zika virus in IFNAR-/- mice maps to prM residues conserved amongst African genotype viruses.

Zika virus (ZIKV) strains are classified into the African and Asian genotypes. The higher virulence of the African MR766 strain, which has been used extensively in ZIKV research, in adult IFNα/ß receptor knockout (IFNAR-/-) mice is widely viewed as an artifact associated with mouse adaptation due to at least 146 passages in wild-type suckling mouse brains. To gain insights into the molecular determinants of MR766's virulence, a series of genes from MR766 were swapped with those from the Asian genotype PRVABC59 isolate, which is less virulent in IFNAR-/- mice. MR766 causes 100% lethal infection in IFNAR-/- mice, but when the prM gene of MR766 was replaced with that of PRVABC59, the chimera MR/PR(prM) showed 0% lethal infection. The reduced virulence was associated with reduced neuroinvasiveness, with MR766 brain titers ≈3 logs higher than those of MR/PR(prM) after subcutaneous infection, but was not significantly different in brain titers of MR766 and MR/PR(prM) after intracranial inoculation. MR/PR(prM) also showed reduced transcytosis when compared with MR766 in vitro. The high neuroinvasiveness of MR766 in IFNAR-/- mice could be linked to the 10 amino acids that differ between the prM proteins of MR766 and PRVABC59, with 5 of these changes affecting positive charge and hydrophobicity on the exposed surface of the prM protein. These 10 amino acids are highly conserved amongst African ZIKV isolates, irrespective of suckling mouse passage, arguing that the high virulence of MR766 in adult IFNAR-/- mice is not the result of mouse adaptation.

Gene Instability-Related lncRNA Prognostic Model of Melanoma Patients via Machine Learning Strategy.

BACKGROUND: Melanoma is a common tumor characterized by a high mortality rate in its late stage. After metastasis, current treatment methods are relatively ineffective. Many studies have shown that long noncoding RNA (lncRNA) may participate in gene mutation and genomic instability in cancer. METHODS: We downloaded transcriptome data, mutation data, and clinical follow-up data of melanoma patients from The Cancer Genome Atlas. We divided samples into groups according to the number of somatic cell mutations and then performed a differential analysis to screen out the differentially expressed genes. We then divided samples into genomic unstable and genomic stable groups. We compared lncRNA expression profiles in these groups and constructed a protein-coding genes network coexpressed with selected lncRNA to analyze the pathways enriched by these genes. Two machine learning methods, least absolute shrinkage and selector operation (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to conduct the lncRNA-related prognostic model. Afterward, we performed survival analysis, risk correlation analysis, independent prognostic analysis, and clinical subgroup model validation. Finally, through wound healing assay and transwell assay, the function of AATBC was verified by A375 cell lines. RESULTS: We screened 61 prognostic-related lncRNAs and constructed an lncRNA-mRNA coexpression network based on these lncRNAs. Seven lncRNAs were selected as common characteristic factors based on the two machine learning methods. The model formula was as follows: risk score = 0.085∗AATBC + 0.190∗ AC026689.1-0.117∗AC083799.1 + 0.036∗ AC091544.6-0.039∗ LINC01287-0.291∗ SPRY4.AS1 + 0.056∗ ZNF667.AS1. The seven lncRNAs in this formula are key candidates. Cell experiments have verified that knocking down AATBC in A375 cell lines can reduce the proliferation and invasion ability of melanoma cells. CONCLUSION: The lncRNA we identified provides a new way to study lncRNA's role in the genomic instability of melanoma. Our findings may provide essential candidate biomarkers for the diagnosis and treatment of melanoma.

Genome instability-related long non-coding RNA in clear renal cell carcinoma determined using computational biology.

BACKGROUND: There is evidence that long non-coding RNA (lncRNA) is related to genetic stability. However, the complex biological functions of these lncRNAs are unclear. METHOD: TCGA - KIRC lncRNAs expression matrix and somatic mutation information data were obtained from TCGA database. "GSVA" package was applied to evaluate the genomic related pathway in each samples. GO and KEGG analysis were performed to show the biological function of lncRNAs-mRNAs. "Survival" package was applied to determine the prognostic significance of lncRNAs. Multivariate Cox proportional hazard regression analysis was applied to conduct lncRNA prognosis model. RESULTS: In the present study, we applied computational biology to identify genome-related long noncoding RNA and identified 26 novel genomic instability-associated lncRNAs in clear cell renal cell carcinoma. We identified a genome instability-derived six lncRNA-based gene signature that significantly divided clear renal cell samples into high- and low-risk groups. We validated it in test cohorts. To further elucidate the role of the six lncRNAs in the model's genome stability, we performed a gene set variation analysis (GSVA) on the matrix. We performed Pearson correlation analysis between the GSVA scores of genomic stability-related pathways and lncRNA. It was determined that LINC00460 and LINC01234 could be used as critical factors in this study. They may influence the genome stability of clear cell carcinoma by participating in mediating critical targets in the base excision repair pathway, the DNA replication pathway, homologous recombination, mismatch repair pathway, and the P53 signaling pathway. CONCLUSION SUBSECTIONS: These data suggest that LINC00460 and LINC01234 are crucial for the stability of the clear cell renal cell carcinoma genome.

The dinucleotide composition of the Zika virus genome is shaped by conflicting evolutionary pressures in mammalian hosts and mosquito vectors.

Most vertebrate RNA viruses show pervasive suppression of CpG and UpA dinucleotides, closely resembling the dinucleotide composition of host cell transcriptomes. In contrast, CpG suppression is absent in both invertebrate mRNA and RNA viruses that exclusively infect arthropods. Arthropod-borne (arbo) viruses are transmitted between vertebrate hosts by invertebrate vectors and thus encounter potentially conflicting evolutionary pressures in the different cytoplasmic environments. Using a newly developed Zika virus (ZIKV) model, we have investigated how demands for CpG suppression in vertebrate cells can be reconciled with potentially quite different compositional requirements in invertebrates and how this affects ZIKV replication and transmission. Mutant viruses with synonymously elevated CpG or UpA dinucleotide frequencies showed attenuated replication in vertebrate cell lines, which was rescued by knockout of the zinc-finger antiviral protein (ZAP). Conversely, in mosquito cells, ZIKV mutants with elevated CpG dinucleotide frequencies showed substantially enhanced replication compared to wild type. Host-driven effects on virus replication attenuation and enhancement were even more apparent in mouse and mosquito models. Infections with CpG- or UpA-high ZIKV mutants in mice did not cause typical ZIKV-induced tissue damage and completely protected mice during subsequent challenge with wild-type virus, which demonstrates their potential as live-attenuated vaccines. In contrast, the CpG-high mutants displayed enhanced replication in Aedes aegypti mosquitoes and a larger proportion of mosquitoes carried infectious virus in their saliva. These findings show that mosquito cells are also capable of discriminating RNA based on dinucleotide composition. However, the evolutionary pressure on the CpG dinucleotides of viral genomes in arthropod vectors directly opposes the pressure present in vertebrate host cells, which provides evidence that an adaptive compromise is required for arbovirus transmission. This suggests that the genome composition of arbo flaviviruses is crucial to maintain the balance between high-level replication in the vertebrate host and persistent replication in the mosquito vector.

M2 Macrophage Co-Expression Factors Correlate With Immune Phenotype and Predict Prognosis of Bladder Cancer.

PURPOSE: Therapeutic targets of tumor-associated macrophages have been discovered and used clinically as immunotherapy. M2 macrophages are tumor-associated macrophages that promote cancer progression. This article explores the related factors and the effects of type M2 macrophages. METHOD: We obtained bladder cancer (BC) sequencing data from TCGA and GSE31189. We used the CIBERSORT algorithm calculate M2 macrophage proportions among 22 type immune cells. The Estimate package was used to measure BC purity. M2 macrophage-related genes were selected using WGCNA. Receiver operating characteristic curves and Kaplan-Meier analyses were performed to determine the risk score, conducted for M2 macrophage-related factors. The Pearson test was used to determine the correlation among M2 macrophage-related genes, clinical phenotype, immune phenotype and tumor mutation burden (TMB). The TIMER database was used to calculate correlations among M2 macrophages and other cancers. RESULTS: Expression of four M2 macrophages co-expressed genes (CD163, CD209, CSF1, MMD) positively correlated with infiltration of M2 macrophages, which were enriched in the negative regulation of immune system process and the positive regulation of tumor necrosis factor production. M2 macrophage-related factors are robust biomarkers for predicting the BC and immune phenotypes. The Cox regression model built on these four co-expression factors showed a close correlation with outcome (AUC = 0.64). The four co-expression factors negatively correlated outcome and TMB. CONCLUSION: Four co-expressed genes promote high levels of infiltration of type M2 macrophages in the negative regulation of immune system processes and the positive regulation of tumor necrosis factor production processes. These co-expressed genes and the biological process they involve might suggest new strategies for regulation of chemotaxis in M2 macrophages.

Macrophage M2 Co-expression Factors Correlate With the Immune Microenvironment and Predict Outcome of Renal Clear Cell Carcinoma.

Purpose: In the tumor microenvironment, the functional differences among various tumor-associated macrophages (TAM) are not completely clear. Tumor-associated macrophages are thought to promote the progression of cancer. This article focuses on exploring M2 macrophage-related factors and behaviors of renal clear cell carcinoma. Method: We obtained renal clear cell carcinoma data from TCGA-KIRC-FPKM, GSE8050, GSE12606, GSE14762, and GSE3689. We used the "Cibersort" algorithm to calculate type M2 macrophage proportions among 22 types of immune cells. M2 macrophage-related co-expression module genes were selected using weighted gene co-expression network analysis (WGCNA). A renal clear cell carcinoma prognosis risk score was built based on M2 macrophage-related factors. The ROC curve and Kaplan-Meier analysis were performed to evacuate the risk score in various subgroups. The Pearson test was used to calculate correlations among M2 macrophage-related genes, clinical phenotype, immune phenotype, and tumor mutation burden (TMB). We measured differences in co-expression of genes at the protein level in clear renal cell carcinoma tissues. Results: There were six M2 macrophage co-expressed genes (F13A1, FUCA1, SDCBP, VSIG4, HLA-E, TAP2) related to infiltration of M2 macrophages; these were enriched in neutrophil activation and involved in immune responses, antigen processing, and presentation of exogenous peptide antigen via MHC class I. M2-related factor frequencies were robust biomarkers for predicting the renal clear cell carcinoma patient clinical phenotype and immune microenvironment. The Cox regression model, built based on M2 macrophage-related factors, showed a close prognostic correlation (AUC = 0.78). The M2 macrophage-related prognosis model also performed well in various subgroups. Using western blotting, we found that VSIG4 protein expression levels were higher in clear renal cell carcinoma tissues than in normal tissues. Conclusion: These co-expressed genes were most related to the M2 macrophage phenotype. They correlated with the immune microenvironment and predicted outcomes of renal clear cell carcinoma. These co-expressed genes and the biological processes associated with them might provide the basis for new strategies to intervene via chemotaxis of M2 macrophages.

Coexpressed Genes That Promote the Infiltration of M2 Macrophages in Melanoma Can Evaluate the Prognosis and Immunotherapy Outcome.

PURPOSE: To improve immunotherapy efficacy for melanoma, a coexpression network and key genes of M2 macrophages in melanoma were explored. A prognostic risk assessment model was established for M2-related coexpressed genes, and the role of M2 macrophages in the immune microenvironment of melanoma was elucidated. METHOD: We obtained mRNA data from melanoma and peritumor tissue samples from The Cancer Genome Atlas-skin cutaneous melanoma (TCGA-SKCM). Then, we used CIBERSORT to calculate the proportion of M2 macrophage cells. A coexpression module most related to M2 macrophages in TCGA-SKCM was determined by analyzing the weighted gene coexpression network, and a coexpression network was established. After survival analysis, factors with significant results were incorporated into a Cox regression analysis to establish a model. The model's essential genes were analyzed using functional enrichment, GSEA, and subgroup and total carcinoma. Finally, external datasets GSE65904 and GSE78220 were used to verify the prognostic risk model. RESULTS: The yellow-green module was the coexpression module most related to M2 macrophages in TCGA-SKCM; NOTCH3, DBN1, KDELC2, and STAB1 were identified as the essential genes that promoted the infiltration of M2 macrophages in melanoma. These genes are concentrated in antigen treatment and presentation, chemokine, cytokine, the T cell receptor pathway, and the IFN-γ pathway. These factors were analyzed for survival, and factors with significant results were included in a Cox regression analysis. According to the methods, a model related to M2-TAM coexpressed gene was established, and the formula was risk score = 0.25∗NOTCH3 + 0.008∗ DBN1 - 0.031∗KDELC2 - 0.032∗STAB1. The new model was used to perform subgroup evaluation and external queue validation. The results showed good prognostic ability. CONCLUSION: We proposed a Cox proportional hazards regression model associated with coexpression genes of melanoma M2 macrophages that may provide a measurement method for generating prognosis scores in patients with melanoma. Four genes coexpressed with M2 macrophages were associated with high levels of infiltration of M2 macrophages. Our findings may provide significant candidate biomarkers for the treatment and monitoring of melanoma.

Inhibitory Effects of Inonotus obliquus Polysaccharide on Inflammatory Response in Toxoplasma gondii-Infected RAW264.7 Macrophages.

Our previous reports have shown that Inonotus obliquus polysaccharide (IOP) has protective effects against Toxoplasma gondii (T. gondii) infection in vivo. The aim of the present research is to explore the in vitro anti-inflammatory effects of IOP and its mechanism in RAW264.7 macrophages infected by T. gondii. In this study, it is indicated that IOP decreased the excessive secretion of inflammatory cytokines tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), interleukin-1ß (IL-1ß), IL-4, and IL-6 in T. gondii-infected RAW264.7 macrophages. IOP effectively suppressed the mRNA expression of these cytokines and chemokines monocyte chemoattractant protein-1 (MCP-1) and macrophage inflammatory protein-1α (MIP-1α). Moreover, IOP inhibited the phosphorylation of inhibitor kappa B kinase α/ß (IKKα/ß), inhibitor κBα (IκBα), p65 in nuclear factor-kappa B (NF-κB) signaling pathway and p38, c-Jun N-terminal kinase (JNK), and extracellular signal-regulated kinase 1/2 (ERK1/2) in mitogen-activated protein kinases (MAPKs) signaling pathway. Meantime, IOP prevented NF-κB p65 and c-Jun translocation from the cytoplasm to the nucleus. Further, IOP downregulated the protein expression of toll-like receptor 2 (TLR2) and TLR4 in T. gondii-infected RAW264.7 macrophages. The above results suggest that IOP can inhibit the inflammatory response infected with T. gondii via regulating TLR2/TLR4-NF-κB/MAPKs pathways and exerting its anti-T. gondii role in vitro.

CD8+ T Cell Co-Expressed Genes Correlate With Clinical Phenotype and Microenvironments of Urothelial Cancer.

PURPOSE: To identify immune-related co-expressed genes that promote CD8+ T cell infiltration in bladder cancer, and to explore the interactions among relevant genes in the tumor microenvironment. METHOD: We obtained bladder cancer gene matrix and clinical information data from TCGA, GSE32894 and GSE48075. The "estimate" package was used to calculate tumor purity and immune score. The CIBERSORT algorithm was used to assess CD8+ T cell proportions. Weighted gene co-expression network analysis was used to identify the co-expression modules with CD8+ T cell proportions and bladder tumor purity. Subsequently, we performed correlation analysis among angiogenesis factors, angiogenesis inhibitors, immune inflammatory responses, and CD8+ T cell related genes in tumor microenvironment. RESULTS: A CD8+ T cell related co-expression network was identified. Eight co-expressed genes (PSMB8, PSMB9, PSMB10, PSME2, TAP1, IRF1, FBOX6, ETV7) were identified as CD8+ T cell-related genes that promoted infiltration of CD8+ T cells, and were enriched in the MHC class I tumor antigen presentation process. The proteins level encoded by these genes (PSMB10, PSMB9, PSMB8, TAP1, IRF1, and FBXO6) were lower in the high clinical grade patients, which suggested the clinical phenotype correlation both in mRNA and protein levels. These factors negatively correlated with angiogenesis factors and positively correlated with angiogenesis inhibitors. PD-1 and PD-L1 positively correlated with these genes which suggested PD-1 expression level positively correlated with the biological process composed by these co-expression genes. In the high expression group of these genes, inflammation and immune response were more intense, and the tumor purity was lower, suggesting that these genes were immune protective factors that improved the prognosis in patients with bladder cancer. CONCLUSION: These co-expressed genes promote high levels of infiltration of CD8+ T cells in an immunoproteasome process involved in MHC class I molecules. The mechanism might provide new pathways for treatment of patients who are insensitive to PD-1 immunotherapy due to low degrees of CD8+ T cell infiltration.

Three-gene risk model in papillary renal cell carcinoma: a robust likelihood-based survival analysis.

BACKGROUND: Papillary renal cell carcinoma (PRCC) accounts for 15% of all renal cell carcinomas. The molecular mechanisms of renal papillary cell carcinoma remain unclear, and treatments for advanced disease are limited. RESULT: We built the computing model as follows: Risk score = 1.806 * TPX2 - 0.355 * TXNRD2 - 0.805 * SLC6A20. The 3-year AUC of overall survival was 0.917 in the training set (147 PRCC samples) and 0.760 in the test set (142 PRCC samples). Based on the robust model, M2 macrophages showed positive correlation with risk score, while M1 macrophages were the opposite. PRCC patients with low risk score showed higher tumor mutation burden. TPX2 is a risk factor, and co-expression factors were enriched in cell proliferation and cancer-related pathways. Finally, the proliferation and invasion of PRCC cell line were decreased in the TPX2 reduced group, and the differential expression was identified. TPX2 is a potential risk biomarker which involved in cell proliferation in PRCC. CONCLUSION: We conducted a study to develop a three gene model for predicting prognosis in patients with papillary renal cell carcinoma. Our findings may provide candidate biomarkers for prognosis that have important implications for understanding the therapeutic targets of papillary renal cell carcinoma. METHOD: Gene expression matrix and clinical data were obtained from TCGA (The Cancer Genome Atlas), GSE26574, GSE2048, and GSE7023. Prognostic factors were identified using "survival" and "rbsurv" packages, and a risk score was constructed using Multivariate Cox regression analysis. The co-expression networks of the factors in model were constructed using the "WGCNA" package. The co-expression genes of factors were enriched and displayed the biological process. Based on this robust risk model, immune cells infiltration proportions and tumor mutation burdens were compared between risk groups. Subsequently, using the PRCC cell line, the role of TPX2 was determined by Cell proliferation assay, 5-Ethynyl-20-deoxyuridine assay and Transwell assay.

Identification of a Robust Five-Gene Risk Model in Prostate Cancer: A Robust Likelihood-Based Survival Analysis.

AIM: In this paper, we aimed to develop and validate a risk prediction method using independent prognosis genes selected robustly in prostate cancer. METHOD: We considered 723 samples obtained from TCGA (the Cancer Genome Atlas), GSE46602, and GSE21032. Prostate cancer prognosis-related genes with P < 0.05 were selected using Univariable Cox regression analysis. We then built the lowest AIC (Akaike information criterion score) optimal gene model using the "Rbsurv" package in TCGA train set. The coefficients were obtained by Multivariable Cox regression analysis. We named the new prognosis method CMU5. The CMU5 risk score was verified in TCGA test set, GSE46602, and GSE21032. RESULTS: FAM72D, ARHGAP33, TACR2, PLEK2, and FA2H were identified as independent prognosis factors in prostate cancer patients. We built the computing model as follows: CMU5 risk score = 1.158∗FAM72D + 1.737∗ARHGAP33 - 0.737∗TACR2 - 0.651∗PLEK2 - 0.793∗FA2H. The AUC of DFS was 0.809 in the train set (274 samples), 0.710 in the test set (273 samples), and 0.768 in the complete set (547 samples). The benign prediction capacity of CMU5 was verified by GSE46602 (36 samples; AUC = 0.6039) and GSE21032 GPL5188 (140 samples; AUC = 0.7083). Using the cut-off point of 2.056, a significant difference was shown between high- and low-risk groups. CONCLUSION: A prognosis-related risk score formula named CMU5 was built and verified, providing reliable prediction of prostate cancer outcome. This signature might provide a basis for individualized treatment of prostate cancer.

Identification of core genes associated with prostate cancer progression and outcome via bioinformatics analysis in multiple databases.

ABSTRACT: The morbidity and mortality of prostate carcinoma has increased in recent years and has become the second most common ale malignant carcinoma worldwide. The interaction mechanisms between different genes and signaling pathways, however, are still unclear. METHODS: Variation analysis of GSE38241, GSE69223, GSE46602 and GSE104749 were realized by GEO2R in Gene Expression Omnibus database. Function enrichment was analyzed by DAVID.6.8. Furthermore, the PPI network and the significant module were analyzed by Cytoscape, STRING and MCODE.GO. Pathway analysis showed that the 20 candidate genes were closely related to mitosis, cell division, cell cycle phases and the p53 signaling pathway. A total of six independent prognostic factors were identified in GSE21032 and TCGA PRAD. Oncomine database and The Human Protein Atlas were applied to explicit that six core genes were over expression in prostate cancer compared to normal prostate tissue in the process of transcriptional and translational. Finally, gene set enrichment were performed to identified the related pathway of core genes involved in prostate cancer. RESULT: Hierarchical clustering analysis revealed that these 20 core genes were mostly related to carcinogenesis and development. CKS2, TK1, MKI67, TOP2A, CCNB1 and RRM2 directly related to the recurrence and prognosis of prostate cancer. This result was verified by TCGA database and GSE21032. CONCLUSION: These core genes play a crucial role in tumor carcinogenesis, development, recurrence, metastasis and progression. Identifying these genes could help us to understand the molecular mechanisms and provide potential biomarkers for the diagnosis and treatment of prostate cancer.

A vaccinia-based single vector construct multi-pathogen vaccine protects against both Zika and chikungunya viruses.

Zika and chikungunya viruses have caused major epidemics and are transmitted by Aedes aegypti and/or Aedes albopictus mosquitoes. The "Sementis Copenhagen Vector" (SCV) system is a recently developed vaccinia-based, multiplication-defective, vaccine vector technology that allows manufacture in modified CHO cells. Herein we describe a single-vector construct SCV vaccine that encodes the structural polyprotein cassettes of both Zika and chikungunya viruses from different loci. A single vaccination of mice induces neutralizing antibodies to both viruses in wild-type and IFNAR-/- mice and protects against (i) chikungunya virus viremia and arthritis in wild-type mice, (ii) Zika virus viremia and fetal/placental infection in female IFNAR-/- mice, and (iii) Zika virus viremia and testes infection and pathology in male IFNAR-/- mice. To our knowledge this represents the first single-vector construct, multi-pathogen vaccine encoding large polyproteins, and offers both simplified manufacturing and formulation, and reduced "shot burden" for these often co-circulating arboviruses.