95 research outputs found

    Psign to Symbol: an Archaeology of Art

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    The following thesis is a goal divided into three parts: the bibliographic/ etymologic/ and all that is absent

    Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

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    Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

    Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

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    Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

    Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

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    This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

    Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

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    Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

    Inter-layer Adhesion Performance of Steel Packaging Materials for Food Cans Under Retort Conditions

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    The steel packaging industry faces the dual challenge imposed by legislation to eradicate the use of Chrome(VI) from sub- strate manufacture and the removal of Bisphenol-A (BPA) from the organic lacquer at the point of food contact. The paper reports on an experimental investigation into the quality of adhesion between the coatings and substrates as a result of the retort process, typically the harshest conditions to which the materials will be exposed. In terms of adhesion, the novel Chrome(III) substrates show promise when compared with current Chrome(VI) substrate. There is a significant reduction in the adhesion of the polyester-based Bisphenol-A non-intent lacquers compared to the incumbent epoxy-phenolic lacquers. Adhesion performance is lower with an increase in retort temperature and time of exposure. The adhesion further reduces in mild acidic and saline conditions. The reduction in adhesion post-retort is attributed to the sensitivity of the polyester-based BPANI lacquers to water vapour absorption. The process reversible nature of the adhesion loss indicates that, at short time- scales, the adhesion loss is a result of polyester hydrolysis. Acidic and saline solutions also lead to a reduction in adhesion as a result of metal surface corrosion. The paper has impact on producers, fillers and consumers of steel packaging foodstuff

    Bisphenol A and its analogues: A comprehensive review to identify and prioritize effect biomarkers for human biomonitoring

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    Human biomonitoring (HBM) studies have demonstrated widespread and daily exposure to bisphenol A (BPA). Moreover, BPA structural analogues (e.g. BPS, BPF, BPAF), used as BPA replacements, are being increasingly detected in human biological matrices. BPA and some of its analogues are classified as endocrine disruptors suspected of contributing to adverse health outcomes such as altered reproduction and neurodevelopment, obesity, and metabolic disorders among other developmental and chronic impairments. One of the aims of the H2020 European Human Biomonitoring Initiative (HBM4EU) is the implementation of effect biomarkers at large scales in future HBM studies in a systematic and standardized way, in order to complement exposure data with mechanistically-based biomarkers of early adverse effects. This review aimed to identify and prioritize existing biomarkers of effect for BPA, as well as to provide relevant mechanistic and adverse outcome pathway (AOP) information in order to cover knowledge gaps and better interpret effect biomarker data. A comprehensive literature search was performed in PubMed to identify all the epidemiologic studies published in the last 10 years addressing the potential relationship between bisphenols exposure and alterations in biological parameters. A total of 5716 references were screened, out of which, 119 full-text articles were analyzed and tabulated in detail. This work provides first an overview of all epigenetics, gene transcription, oxidative stress, reproductive, glucocorticoid and thyroid hormones, metabolic and allergy/immune biomarkers previously studied. Then, promising effect biomarkers related to altered neurodevelopmental and reproductive outcomes including brainderived neurotrophic factor (BDNF), kisspeptin (KiSS), and gene expression of nuclear receptors are prioritized, providing mechanistic insights based on in vitro, animal studies and AOP information. Finally, the potential of omics technologies for biomarker discovery and its implications for risk assessment are discussed. To the best of our knowledge, this is the first effort to comprehensively identify bisphenol-related biomarkers of effect for HBM purposes.European Union Commission H2020-EJP-HBM4EU 733032HBM4EU Initiativ

    Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

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    Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

    AI is a viable alternative to high throughput screening: a 318-target study

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    : High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery
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