Hepatitis B virus X protein promotes tumor glycolysis by downregulating lncRNA OIP5-AS1/HKDC1 in HCC.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide, with Hepatitis B virus (HBV) infection being the leading cause. This study aims to investigate the role of HBV in HCC pathogenesis involving glucose metabolism. Long non-coding RNA (lncRNA) OIP5-AS1 was significantly downregulated in HBV-positive HCC patients, and its low expression indicated a poor prognosis. This lncRNA was primarily localized in the cytoplasm, acting as a tumor suppressor. HBV protein X (HBx) repressed OIP5-AS1 expression by inhibiting a ligand-activated transcriptional factor peroxisome proliferator-activated receptor α (PPARα). Furthermore, mechanistic studies revealed that OIP5-AS1 inhibited tumor growth by suppressing Hexokinase domain component 1 (HKDC1)-mediated glycolysis. The expression of HKDC1 could be enhanced by transcriptional factor sterol regulatory element-binding protein 1 (SREBP1). OIP5-AS1 facilitated the ubiquitination and degradation of SREBP1 to suppress HKDC1 transcription, which inhibited glycolysis. The results suggest that lncRNA OIP5-AS1 plays an anti-oncogenic role in HBV-positive HCC via the HBx/OIP5-AS1/HKDC1 axis, providing a promising diagnostic marker and therapeutic target for HBV-positive HCC patients.

Important oncogenic and immunogenic roles of SPP1 and CSF1 in hepatocellular carcinoma.

The treatment and prognosis of liver cancer remain the focus of medical research. Studies have shown that SPP1 and CSF1 play important roles in cell proliferation, invasion, and metastasis. Therefore, this study analyzed the oncogenic and immunologic roles of SPP1 and CSF1 in hepatocellular carcinoma (HCC). We found that the expression levels of SPP1 and CSF1 in HCC were markedly increased and positively correlated. High SPP1 expression was significantly associated with poor OS, DSS, PFS, and RFS. It was not affected by gender, alcohol use, HBV, or race, whereas CSF1 was affected by these factors. Higher expression levels of SPP1 and CSF1 indicated higher levels of immune cell infiltration and a higher immune score with the R software package ESTIMATE. Further analysis revealed that many genes work co-expressed between SPP1 and CSF1 with the LinkedOmics database, which were mainly involved in signal transduction, the integral components of the membrane, protein binding, and osteoclast differentiation. In addition, we screened ten hub genes using cytoHubba, among which the expression of four genes was significantly associated with the prognosis of HCC patients. Finally, we demonstrated the oncogenic and immunologic roles of SPP1 and CSF1 using the vitro experiments. Reducing the expression of either SPP1 or CSF1 could significantly reduce the proliferation of HCC cells and the expression of CSF1, SPP1, and the other four hub genes. This study suggested that SPP1 and CSF1 interact with each other and have the potential to be therapeutic and prognostic targets for HCC.

MetalProGNet: a structure-based deep graph model for metalloprotein-ligand interaction predictions.

Metalloproteins play indispensable roles in various biological processes ranging from reaction catalysis to free radical scavenging, and they are also pertinent to numerous pathologies including cancer, HIV infection, neurodegeneration, and inflammation. Discovery of high-affinity ligands for metalloproteins powers the treatment of these pathologies. Extensive efforts have been made to develop in silico approaches, such as molecular docking and machine learning (ML)-based models, for fast identification of ligands binding to heterogeneous proteins, but few of them have exclusively concentrated on metalloproteins. In this study, we first compiled the largest metalloprotein-ligand complex dataset containing 3079 high-quality structures, and systematically evaluated the scoring and docking powers of three competitive docking tools (i.e., PLANTS, AutoDock Vina and Glide SP) for metalloproteins. Then, a structure-based deep graph model called MetalProGNet was developed to predict metalloprotein-ligand interactions. In the model, the coordination interactions between metal ions and protein atoms and the interactions between metal ions and ligand atoms were explicitly modelled through graph convolution. The binding features were then predicted by the informative molecular binding vector learned from a noncovalent atom-atom interaction network. The evaluation on the internal metalloprotein test set, the independent ChEMBL dataset towards 22 different metalloproteins and the virtual screening dataset indicated that MetalProGNet outperformed various baselines. Finally, a noncovalent atom-atom interaction masking technique was employed to interpret MetalProGNet, and the learned knowledge accords with our understanding of physics.

A High-Salt Diet Exacerbates Liver Fibrosis through Enterococcus-Dependent Macrophage Activation.

People consume more salt than the recommended levels due to poor dietary practices. The effects of long-term consumption of high-salt diets (HSD) on liver fibrosis are unclear. This study aimed to explore the impact of HSD on liver fibrosis. In this study, a carbon tetrachloride (CCL4)-induced liver fibrosis mouse model was used to evaluate fibrotic changes in the livers of mice fed a normal diet (ND) and an HSD. The HSD exacerbated liver injury and fibrosis. Moreover, the protein expression levels of transforming growth factor ß (TGF-ß), tumor necrosis factor alpha (TNF-α), and monocyte chemoattractant protein 1 (MCP-1) were significantly higher in the HSD group than in the normal group. The proportion of macrophages and activation significantly increased in the livers of HSD-fed mice. Meanwhile, the number of macrophages significantly increased in the small intestinal lamina propria of HSD-fed mice. The levels of profibrotic factors also increased in the small intestine of HSD-fed mice. Additionally, HSD increased the profibrotic chemokines and monocyte chemoattractant levels in the portal vein blood. Further characterization suggested that the HSD decreased the expression of tight junction proteins (ZO-1 and CLDN1), enhancing the translocation of bacteria. Enterococcus promoted liver injury and inflammation. In vitro experiments demonstrated that Enterococcus induced macrophage activation through the NF-κB pathway, thus promoting the expression of fibrosis-related genes, leading to liver fibrogenesis. Similarly, Enterococcus disrupted the gut microbiome in vivo and significantly increased the fibrotic markers, TGF-ß, and alpha smooth muscle actin (α-SMA) expression in the liver. IMPORTANCE This study further confirms that Enterococcus induce liver fibrosis in mice. These results indicate that an HSD can exacerbate liver fibrosis by altering the gut microbiota composition, thus impairing intestinal barrier function. Therefore, this may serve as a new target for liver fibrosis therapy and gut microbiota management.

Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network.

Covalent ligands have attracted increasing attention due to their unique advantages, such as long residence time, high selectivity, and strong binding affinity. They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed. However, our limited knowledge of covalent binding sites has hindered the discovery of novel ligands. Therefore, developing in silico methods to identify covalent binding sites is highly desirable. Here, we propose DeepCoSI, the first structure-based deep graph learning model to identify ligandable covalent sites in the protein. By integrating the characterization of the binding pocket and the interactions between each cysteine and the surrounding environment, DeepCoSI achieves state-of-the-art predictive performances. The validation on two external test sets which mimic the real application scenarios shows that DeepCoSI has strong ability to distinguish ligandable sites from the others. Finally, we profiled the entire set of protein structures in the RCSB Protein Data Bank (PDB) with DeepCoSI to evaluate the ligandability of each cysteine for covalent ligand design, and made the predicted data publicly available on website.

Construction and validation of a prognostic risk model for breast cancer based on protein expression.

Breast cancer (BRCA) is the primary cause of mortality among females globally. The combination of advanced genomic analysis with proteomics characterization to construct a protein prognostic model will help to screen effective biomarkers and find new therapeutic directions. This study obtained proteomics data from The Cancer Proteome Atlas (TCPA) dataset and clinical data from The Cancer Genome Atlas (TCGA) dataset. Kaplan-Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G). Based on risk curves, survival curves, receiver operating characteristic curves, and independent prognostic analysis, the protein prognostic model could be viewed as an independent factor to accurately predict the survival time of BRCA patients. We further validated that this prognostic model had good predictive performance in the GSE88770 dataset. The expression of 6 proteins was significantly associated with the overall survival of BRCA patients. The 6 proteins and encoding genes were differentially expressed in normal and primary tumor tissues and in different BRCA stages. In addition, we verified the expression of 3 differential proteins by immunohistochemistry and found that CDH3 and EIF4G1 were significantly higher in breast cancer tissues. Functional enrichment analysis indicated that the 6 genes were mainly related to the HIF-1 signaling pathway and the PI3K-AKT signaling pathway. This study suggested that the prognosis-related proteins might serve as new biomarkers for BRCA diagnosis, and that the risk model could be used to predict the prognosis of BRCA patients.

Secreted phosphoprotein 1 as a potential prognostic and immunotherapy biomarker in multiple human cancers.

Secreted phosphoprotein 1 (SPP1) is involved in immune regulation, cell survival, and tumor progression. Studies have demonstrated that SPP1 plays an important role in certain individual tumors. However, the expression profile and oncogenic features of SPP1 in diverse cancers are remaining unknown. Therefore, we performed a comprehensive analysis using The Cancer Genome Atlas (TCGA) database. Raw data of 33 cancer types were download from the University of California Santa Cruz (UCSC) Xena website. The expression of SPP1 and its relationship with tumor prognosis, immune invasion, tumor microenvironment, and immunotherapy were analyzed using the R language. The function analysis was conducted using Gene Set Enrichment Analysis (GSEA). The oncogenic features of SPP1 was validated by wound-healing assay and EdU staining assay. SPP1 highly expressed in most cancers. The expression of SPP1 was significant related to prognosis, tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoint genes, suggested that SPP1 plays an essential role in the tumor immune microenvironment and immune cell infiltration. The immune/stromal scores correlated positively with the SPP1 expression, and the relationship was affected by tumor heterogeneity and immunotherapy. In addition, SPP1 could predict the response of tumor immunotherapy. Functional analysis revealed the SPP1-associated terms and pathways. Finally, SPP1 significantly elevated cell proliferation and migration in A549, Huh7, HT-29, A2780 tumor cell lines. In conclusion, this study indicated that SPP1 involved in tumorigenesis, tumor progression, and regulated tumor immune microenvironment, revealing SPP1 might be a potential target for evaluating prognosis and immunotherapy in multiple cancers.

Interleukin-33 as an early predictor of cetuximab treatment efficacy in patients with colorectal cancer.

BACKGROUND: Cetuximab is used for colorectal cancer (CRC) treatment. However, the early biomarker of treatment efficacy of cetuximab has not been identified. METHODS: After 1 year of cetuximab treatment, patients were divided into an effective group and an ineffective group. The interleukin-33 (IL-33) level and the distribution of lymphatic cells in patients were investigated by analyzing the peripheral blood mononuclear cells via flow cytometry analysis and ELISA. The correlation between IL-33 immunomodulatory effect and cetuximab treatment efficacy was determined through experiments in vivo and in vitro. RESULTS: The IL-33 level in the peripheral blood was increased at 4 weeks after cetuximab administration of effective group, meanwhile, the osteopontin (OPN) was reduced. Whereas neither IL-33 level nor OPN level of ineffective patients changed. In the effective group, the number of natural killer (NK) and CD8+ T cells were increased. Moreover, CD137 and CD107a expression on NK cells were higher in the effective group compared to the ineffective group. In vitro cetuximab treatment also increased the number of NK and CD8+ T cells as well as CD137 and CD107a expression upon IL-33 stimulation. Moreover, the secretion of OPN was inhibited by IL-33 administration in cetuximab-treated PBMCs from the effective group patients. IL-33 upregulated the cytotoxicity of NK cells and inhibited tumor cells growth in the effective cetuximab treatment mice. CONCLUSION: Effective cetuximab treatment induced a change of IL-33 and OPN at the early stage and triggered the NK cells antitumor activity. Consequently, significantly increased IL-33 level and decreased OPN level in the peripheral blood at the early treatment are proposed as potential predictors of cetuximab treatment efficacy.

Revised road traffic safety law and years of life lost due to traffic deaths in China, 2002-2019.

Legal intervention is a powerful tool to reduce road traffic injuries (RTIs). China amended the Road Traffic Safety Law in 2011, but the impact of amended law on traffic crash deaths is still unknown. In this study, we conducted an interrupted time series analysis and examined years of life lost (YLLs) per 100,000 population as the assessment indicator to evaluate the association of road traffic safety law and traffic crash mortality. Annual YLLs data due to traffic deaths from 2002 to 2019 in China were obtained from the Global Burden of Disease (GBD) 2019. After implementation of the revised law, the average level of total YLLs per 100,000 population due to traffic deaths decreased from 1133.14 to 848.87, and the slope of annual YLLs per 100,000 population decreased by 30.11 (95% CI: 22.46, 37.75), indicating a steeper downward trend. The revised traffic law was associated with YLLs reduction due to traffic deaths for males, females, all age groups, pedestrians, motor vehicle users, and other road users, as well as traffic deaths attributed to alcohol use and tobacco use. These findings suggested that the revised Road Traffic Safety Law improved road safety by decreasing YLLs due to traffic deaths in China. However, the burden of RTIs is still heavy and efforts to further improve traffic laws and the adoption of other interventions are urgently needed.

RBM15-mediated N6-methyladenosine modification affects COVID-19 severity by regulating the expression of multitarget genes.

Severe coronavirus disease 2019 (COVID-19) is characterized by symptoms of lymphopenia and multiorgan damage, but the underlying mechanisms remain unclear. To explore the function of N6-methyladenosine (m6A) modifications in COVID-19, we performed microarray analyses to comprehensively characterize the m6A epitranscriptome. The results revealed distinct global m6A profiles in severe and mild COVID-19 patients. Programmed cell death and inflammatory response were the major biological processes modulated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Further, RBM15, a major m6A methyltransferase, was significantly elevated and positively correlated with disease severity. Silencing RBM15 drastically reduced lymphocyte death in vitro. Knockdown of RBM15 remarkably suppressed the expression levels of multitarget genes related to programmed cell death and inflammatory response. This study shows that SARS-CoV-2 infection alters the m6A epitranscriptome of lymphocytes, particularly in the case of severe patients. RBM15 regulated host immune response to SARS-CoV-2 by elevating m6A modifications of multitarget genes. These findings indicate that RBM15 can serve as a target for the treatment of COVID-19.

VAD-MM/GBSA: A Variable Atomic Dielectric MM/GBSA Model for Improved Accuracy in Protein-Ligand Binding Free Energy Calculations.

The molecular mechanics/generalized Born surface area (MM/GBSA) has been widely used in end-point binding free energy prediction in structure-based drug design (SBDD). However, in practice, it is usually being treated as a disputed method mostly because of its system dependence. Here, combining with machine-learning optimization, we developed a novel version of MM/GBSA, named variable atomic dielectric MM/GBSA (VAD-MM/GBSA), by assigning variable dielectric constants directly to the protein/ligand atoms. The new strategy exhibits markedly improved accuracy in binding affinity calculations for various protein-ligand systems and is promising to be used in the postprocessing of structure-based virtual screening. Moreover, VAD-MM/GBSA outperformed prime MM/GBSA in Schrödinger software and showed remarkable predictive performance for specific protein targets, such as POL polyprotein, human immunodeficiency virus type 1 (HIV-1) protease, etc. Our study showed that the VAD-MM/GBSA method with little extra computational overhead provides a potential replacement of the MM/GBSA in AMBER software. An online web server of VAD-MMGBSA has been developed and is now available at http://cadd.zju.edu.cn/vdgb.

Hepatitis B virus P protein initiates glycolytic bypass in HBV-related hepatocellular carcinoma via a FOXO3/miRNA-30b-5p/MINPP1 axis.

BACKGROUND: Hepatitis B virus (HBV) infection is a crucial risk factor for hepatocellular carcinoma (HCC). However, its underlying mechanism remains understudied. METHODS: Microarray analysis was conducted to compare the genes and miRNAs in liver tissue from HBV-positive and HBV-negative HCC patients. Biological functions of these biomarkers in HBV-related HCC were validated via in vitro and in vivo experiments. Furthermore, we investigated the effect of HBV on the proliferation and migration of tumor cells in HBV-positive HCC tissue. Bioinformatics analysis was then performed to validate the clinical value of the biomarkers in a large HCC cohort. RESULTS: We found that a gene, MINPP1 from the glycolytic bypass metabolic pathway, has an important biological function in the development of HBV-positive HCC. MINPP1 is down-regulated in HBV-positive HCC and could inhibit the proliferation and migration of the tumor cells. Meanwhile, miRNA-30b-5p was found to be a stimulator for the proliferation of tumor cell through glycolytic bypass in HBV-positive HCC. More importantly, miRNA-30b-5p could significantly downregulate MINPP1 expression. Metabolic experiments showed that the miRNA-30b-5p/MINPP1 axis is able to accelerate the conversion of glucose to lactate and 2,3-bisphosphoglycerate (2,3-BPG). In the HBV-negative HCC cells, miRNA-30b-5p/MINPP1 could not regulate the glycolytic bypass to promote the tumorigenesis. However, once HBV was introduced into these cells, miRNA-30b-5p/MINPP1 significantly enhanced the proliferation, migration of tumor cells, and promoted the glycolytic bypass. We further revealed that HBV infection promoted the expression of miRNA-30b-5p through the interaction of HBV protein P (HBp) with FOXO3. Bioinformatics analysis on a large cohort dataset showed that high expression of MINPP1 was associated with favorable survival of HBV-positive HCC patients, which could lead to a slower progress of this disease. CONCLUSION: Our study found that the HBp/FOXO3/miRNA-30b-5p/MINPP1 axis contributes to the development of HBV-positive HCC cells through the glycolytic bypass. We also presented miRNA-30b-5p/MINPP1 as a novel biomarker for HBV-positive HCC early diagnosis and a potential pharmaceutical target for antitumor therapy.

Comprehensive Study of Tumor Immune Microenvironment and Relevant Genes in Hepatocellular Carcinoma Identifies Potential Prognostic Significance.

Background: The tumor immune microenvironment (TIME) is an external immune system that regulates tumorigenesis. However, cellular interactions involving the TIME in hepatocellular carcinoma (HCC) are poorly characterized. Methods: In this study, we used multidimensional bioinformatic methods to comprehensively analyze cellular TIME characteristics in 735 HCC patients. Additionally, we explored associations involving TIME molecular subtypes and gene types and clinicopathological features to construct a prognostic signature. Results: Based on their characteristics, we classified TIME and gene signatures into three phenotypes (TIME T1-3) and two gene clusters (Gene G1-2), respectively. Further analysis revealed that Gene G1 was associated with immune activation and surveillance and included CD8+ T cells, natural killer cell activation, and activated CD4+ memory T cells. In contrast, Gene G2 was characterized by increased M0 macrophage and regulatory T cell levels. After calculation of principal component algorithms, a TIME score (TS) model, including 78 differentially expressed genes, was constructed based on TIME phenotypes and gene clusters. Furthermore, we observed that the Gene G2 cluster was characterized by high TS, and Gene G1 was characterized by low TS, which correlated with poor and favorable prognosis of HCC, respectively. Correlation analysis showed that TS had a positive association with several clinicopathologic signatures [such as grade, stage, tumor (T), and node (N)] and known somatic gene mutations (such as TP53 and CTNNB1). The prognostic value of the TS model was verified using external data sets. Conclusion: We constructed a TS model based on differentially expressed genes and involving immune phenotypes and demonstrated that the TS model is an effective prognostic biomarker and predictor for HCC patients.

In-depth characterization of the biomarkers based on tumor-infiltrated immune cells reveals implications for diagnosis and prognosis in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is an immune-related tumor, that the type and number of tumor-infiltrated immune cells can serve as biomarkers for the clinical application. In this study, we constructed the immune model for diagnostic and prognostic prediction of HCC based on the systematic bioinformatics analyses on the component of immune cells from large samples transcriptome. CIBERSORT analysis found that the component of immune cells between 513 HCC and 473 adjacent normal tissues was different. M0 macrophages and regulatory T cells were mainly enriched in tumor tissues, whereas the CD8+ T cell and activated CD4+ memory T cells were the most in normal tissues. Using random forest and LASSO analyses, eleven immune cell types were mined out to construct the immune diagnostic model (IDG), which showed high efficiency in distinguishing cancer from normal tissues both in testing and validation groups. In addition, the immune prognostic model (IPG) consisting of five types of immune cells was constructed using the LASSO-Cox algorithm. It showed that HCC patients of the high-risk group had a significantly shorter survival time than those of low-risk group in testing, validation, and entire cohorts. Besides, Nomogram plots and decision curve analyses revealed that the IPG was positively associated with the HCC clinical classification of the Barcelona Clinic Liver Cancer (BCLC) stage, and showing more accuracy of prediction than independent BCLC stage. Related analyses found that IDG positively correlated with epithelial-mesenchymal transition (EMT) and cytotoxic factor-related genes and negatively correlated with immune checkpoint regulators related genes. From the GSEA analysis of the biological function of genes related to IPG, it was found that the genes of the high-risk group were enriched in some tumorigenesis related pathways, such as DNA replication, cell cycle, and PPARA. Therefore, this study identified IDG and IPG as efficient biomarkers for the diagnosis and prognosis of HCC.

The Contradictory Role of Interleukin-33 in Immune Cells and Tumor Immunity.

Interleukin (IL)-33 is a member of the IL-1 superfamily and is a crucial cytokine playing the role of a dual-function molecule. IL-33 mediates its function by interacting with its receptor suppression of tumorigenicity 2 (ST2), which is constitutively expressed on T helper (Th)1 cells, Th2 cells, and other immune cells. Previously, we summarized findings on IL-33 and performed an intensive study of the correlation between IL-33 and tumor. IL-33 enables anti-tumor immune responses through Th1 cells and natural killer (NK) cells and plays a role in tumor immune escape in cancers via Th2 cells and regulatory T cells. Herein, we discuss the contradictory role of IL-33 in immune cells in different cancer, and our summaries may be helpful for better understanding of the development of research on IL-33 and tumor immunity.

Identification of prognostic biomarkers of hepatocellular carcinoma via long noncoding RNA expression and copy number alterations.

Aim: This study aimed to identify long noncoding RNAs (lncRNAs) with potential to be prognostic biomarkers of hepatocellular carcinoma (HCC) by analyzing copy number alterations (CNAs). Methods: RNA Sequencing data of 369 HCC patients was downloaded from The Cancer Genome Atlas database and analyzed with a series of systematic bioinformatics methods. Results: LncRNA-CNA association analysis revealed that many lncRNAs were located in sites frequently amplified or deleted. Three upregulated lncRNAs (LINC00689, SNHG20 and MAFG-AS1) with copy amplification and one downregulated lncRNA TMEM220-AS1 with copy deletion were associated with poor prognosis of HCC. Conclusion: This study reveals that differentially expressed lncRNAs correlate with CNAs in HCC. Moreover, the differentially expressed lncRNAs and their copy amplification/deletions could be promising prognostic biomarkers of HCC.

Sodium butyrate modulates gut microbiota and immune response in colorectal cancer liver metastatic mice.

Colorectal cancer (CRC) liver metastasis (CLM) is the leading death cause of CRC patients, but there is no satisfied approach to treat CLM. Gut microbiota plays a pivotal role in CRC initiation and development. Targeting dysbiosis of the gut microbiota might open up new opportunities for CLM treatment. Here, we investigated the efficacy of sodium butyrate (NaB), a major product of gut microbial fermentation, in modulating gut microbiota in CLM mice. NaB supplement decreased mouse colon cancer CT26 cell liver metastasis in intrasplenic tumor injection model of BALB/c mice. Using 16S rRNA gene sequencing, we found altered microbiota composition in CLM mice, characterized by increases of Firmicutes and Proteobacteria. NaB beneficially changed dysbiosis in CLM mice. Functional analysis of the KEGG pathways showed that NaB changed pathways related to immune system diseases and primary immunodeficiency in CLM mice. In addition, NaB decreased T regulatory cells and increased natural killer T cells and T helper 17 cells, accordingly decreased IL-10 and increased IL-17 secretion in CLM mice liver. In conclusion, NaB beneficially modulated gut microbiota and improved host immune response in CLM mice. These findings demonstrate the therapeutic potential of NaB in CLM treatment.

DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups.

BACKGROUND: Renal cell carcinoma (RCC) is the most common kidney cancer and includes several molecular and histological subtypes with different clinical characteristics. The combination of DNA methylation and gene expression data can improve the classification of tumor heterogeneity, by incorporating differences at the epigenetic level and clinical features. METHODS: In this study, we identified the prognostic methylation and constructed specific prognosis-subgroups based on the DNA methylation spectrum of RCC from the TCGA database. RESULTS: Significant differences in DNA methylation profiles among the seven subgroups were revealed by consistent clustering using 3389 CpGs that indicated that were significant differences in prognosis. The specific DNA methylation patterns reflected differentially in the clinical index, including TNM classification, pathological grade, clinical stage, and age. In addition, 437 CpGs corresponding to 477 genes of 151 samples were identified as specific hyper/hypomethylation sites for each specific subgroup. A total of 277 and 212 genes corresponding to DNA methylation at promoter sites were enriched in transcription factor of GKLF and RREB-1, respectively. Finally, Bayesian network classifier with specific methylation sites was constructed and was used to verify the test set of prognoses into DNA methylation subgroups, which was found to be consistent with the classification results of the train set. DNA methylation-based classification can be used to identify the distinct subtypes of renal cell carcinoma. CONCLUSIONS: This study shows that DNA methylation-based classification is highly relevant for future diagnosis and treatment of renal cell carcinoma as it identifies the prognostic value of each epigenetic subtype.

Microbubbles and Oil Droplets Stabilized by a Class II Hydrophobin in Marinelike Environments.

Hydrophobins are abundant amphipathic proteins produced by fungi. They have been interacting with oils in natural environments for millions of years; therefore, it is sensible to consider them as surfactants and dispersants for cleaning oil spills. To better understand the properties of these amphipathic proteins in seawater, a particular hydrophobin known as cerato-ulmin (CU; mass 7627 g/mol) was studied. CU is adept at forming strong membranes, as indicated by the capacity to stabilize gas-filled bubbles and oil-filled droplets with cylindrical and other nonspherical shapes. The limits of this unusual ability were tested using a wide variety of solvent conditions, including various salt solutions, alcohols, simple hydrocarbons (i.e., cyclohexane, dodecane), acids, and bases. CU concentrations ranged from 20 to 200 µg/mL. The bubbles and other structures made by CU in the presence of various gases span an enormous range of size, from nanometers to millimeters. After larger objects float to the surface, smaller structures remain, and these were found by light scattering to have a hydrodynamic diameter of ∼200 nm.

Formation and elasticity of membranes of the class II hydrophobin Cerato-ulmin at oil-water interfaces.

Protein surfactants show great potential to stabilize foams, bubbles, and emulsions. An important family of surface active proteins, the hydrophobins, is secreted by filamentous fungi. Two hydrophobin classes have been recognized, with Class II exhibiting slightly better solubility than Class I, although neither is very soluble in water. Hydrophobins are small proteins (8-14 kDa), but they are larger and more rigid than typical surfactants such as sodium dodecyl sulfate. This rigidity seems to be manifested in the strength of adsorbed hydrophobin layers on oil droplets or air bubbles. A particular Class II hydrophobin, Cerato-ulmin, was characterized at the oil-water interface (the oil was squalane). The results are compared to measurements at the air-water interface, newly extended to lower Cerato-ulmin concentrations. For both oil-water and air-water interfaces, static and dynamic properties were measured during the evolution of the membrane structure. The static measurements reveal that dilute Cerato-ulmin solution efficiently decreases the interfacial tension, whether at oil-water or air-water interfaces. The reduction in surface tension requires several hours. Interfacial mechanics were characterized too, and the dilatational modulus was found to reach large values at both types of interfaces: 339 ±â€¯19 mN/m at the squalane-water interface and at least 764 ±â€¯45 mN/m at the air-water interface. Both values well exceed those typical of small-molecule surfactants, but come closer to those expected of particulate-loaded interfaces. Circular dichroism provides some insight to adsorption-induced molecular rearrangements, which seem to be more prevalent at the oil-water interface than at the air-water interface.