Integrative cBioPortal Analysis Revealed Molecular Mechanisms That Regulate EGFR-PI3K-AKT-mTOR Pathway in Diffuse Gliomas of the Brain

Diffuse gliomas are a heterogeneous group of tumors with aggressive biological behavior and a lack of effective treatment methods. Despite new molecular findings, the differences between pathohistological types still require better understanding. In this in silico analysis, we investigated AKT1, AKT2, AKT3, CHUK, GSK3β, EGFR, PTEN, and PIK3AP1 as participants of EGFR-PI3K-AKT-mTOR signaling using data from the publicly available cBioPortal platform. Integrative large-scale analyses investigated changes in copy number aberrations (CNA), methylation, mRNA transcription and protein expression within 751 samples of diffuse astrocytomas, anaplastic astrocytomas and glioblastomas. The study showed a significant percentage of CNA in PTEN (76%), PIK3AP1 and CHUK (75% each), EGFR (74%), AKT2 (39%), AKT1 (32%), AKT3 (19%) and GSK3β (18%) in the total sample. Comprehensive statistical analyses show how genomics and epigenomics affect the expression of examined genes differently across various pathohistological types and grades, suggesting that genes AKT3, CHUK and PTEN behave like tumor suppressors, while AKT1, AKT2, EGFR, and PIK3AP1 show oncogenic behavior and are involved in enhanced activity of the EGFR-PI3K-AKT-mTOR signaling pathway. Our findings contribute to the knowledge of the molecular differences between pathohistological types and ultimately offer the possibility of new treatment targets and personalized therapies in patients with diffuse gliomas.

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Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases

10.1101/2021.11.11.21266048 ◽

2021 ◽

Author(s):

Thomas Linden ◽

Frank Hanses ◽

Daniel Domingo-Fernandez ◽

Lauren Nicole DeLong ◽

Alpha Tom Kodamullil ◽

...

Keyword(s):

Machine Learning ◽

Drug Targets ◽

Active Sites ◽

Molecular Mechanisms ◽

Treatment Options ◽

Tyrosine Kinase 2 ◽

Machine Learning Model ◽

Anti Cancer ◽

New Treatment ◽

Using Data

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center Lean European Open Survey on SARS-CoV-2-infected patients (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimers Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.

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Effects and Mechanisms of Anlotinib and Dihydroartemisinin Combination Therapy in Ameliorating Malignant Biological Behavior of Gastric Cancer Cells

Current Pharmaceutical Biotechnology ◽

10.2174/1389201021666200623132803 ◽

2020 ◽

Vol 21 ◽

Author(s):

Qiong Luo ◽

Suyun Zhang ◽

Donghuan Zhang ◽

Rui Feng ◽

Nan Li ◽

...

Keyword(s):

Gastric Cancer ◽

Cancer Cells ◽

Molecular Mechanisms ◽

Chorioallantoic Membrane ◽

Cell Counting ◽

Biological Behavior ◽

Gastric Cancer Cells ◽

Invasion And Migration ◽

Apoptosis Related Protein ◽

And Migration

Background: Gastric cancer(GC) is currently one of the major malignancies that threatens human lives and health. Anlotinib is a novel small-molecule that inhibits angiogenesis to exert anti-tumor effects. However, the function in gastric cancer is incompletely understood. Objective: The aim of the present study was to investigate the anti-tumor effects and molecular mechanisms of anlotinib combined with dihydroartemisinin (DHA) in SGC7901 gastric cancer cells. Method: Different concentrations of anlotinib and DHA were used to treat SGC7901 gastric cancer cells, after which cell proliferation was measured. Drug interactions of anlotinib and DHA were analyzed by the Chou-Talalay method with CompuSyn software. proliferation, apoptosis, invasion, migration, and angiogenesis were measured using the cell counting kit-8 (CCK8) assay, flow cytometry, Transwell invasion assays, scratch assays, and chicken chorioallantoic membrane (CAM) assays. proliferation-associated protein (Ki67), apoptosis-related protein (Bcl-2), and vascular endothelial growth factor A (VEGF-A) were quantified by Western bloting. Results: The combination of 2.5 μmol/L of anlotinib and 5 of μmol/L DHA was highly synergistic in inhibiting cell growth, significantly increased the apoptosis rate and suppressed obviously the invasion and migration capability and angiogenesis of gastric cancer cells. In addition, the expression levels of Ki67, Bcl-2, and VEGF-A, as well as angiogenesis, were significantly decreased in the Combination of drugs compared with in control and either drug alone. Conclusion: The combination of anlotinib and DHA showed synergistic antitumor activity, suggesting their potential in treating patients with gastric cancer.

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Student Perceptions of Community: A Different Perspective

NASPA Journal ◽

10.2202/0027-6014.1064 ◽

1998 ◽

Vol 35 (4) ◽

Author(s):

Jackie Clark ◽

Joan Hirt

Keyword(s):

Student Perceptions ◽

Large Scale ◽

Research University ◽

Small Scale ◽

Social Environments ◽

Common Assumption ◽

Small Communities ◽

Public Research University ◽

The Common ◽

Using Data

The creation of small communities has been proposed as a way of enhancing the educational experience of students at large institutions. Using data from a survey of students living in large and small residences at a public research university, this study does not support the common assumption that small-scale social environments are more conducive to positive community life than large-scale social environments.

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EGCG binds intrinsically disordered N-terminal domain of p53 and disrupts p53-MDM2 interaction

Nature Communications ◽

10.1038/s41467-021-21258-5 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Jing Zhao ◽

Alan Blayney ◽

Xiaorong Liu ◽

Lauren Gandy ◽

Weihua Jin ◽

...

Keyword(s):

Large Scale ◽

Molecular Mechanisms ◽

Epigallocatechin Gallate ◽

Cancer Drug ◽

Dynamic Interactions ◽

Cancer Drug Discovery ◽

Intrinsically Disordered ◽

Terminal Domain ◽

Cancerous Cells

AbstractEpigallocatechin gallate (EGCG) from green tea can induce apoptosis in cancerous cells, but the underlying molecular mechanisms remain poorly understood. Using SPR and NMR, here we report a direct, μM interaction between EGCG and the tumor suppressor p53 (KD = 1.6 ± 1.4 μM), with the disordered N-terminal domain (NTD) identified as the major binding site (KD = 4 ± 2 μM). Large scale atomistic simulations (>100 μs), SAXS and AUC demonstrate that EGCG-NTD interaction is dynamic and EGCG causes the emergence of a subpopulation of compact bound conformations. The EGCG-p53 interaction disrupts p53 interaction with its regulatory E3 ligase MDM2 and inhibits ubiquitination of p53 by MDM2 in an in vitro ubiquitination assay, likely stabilizing p53 for anti-tumor activity. Our work provides insights into the mechanisms for EGCG’s anticancer activity and identifies p53 NTD as a target for cancer drug discovery through dynamic interactions with small molecules.

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Modeling Fused Filament Fabrication using Artificial Neural Networks

Production Engineering ◽

10.1007/s11740-021-01020-y ◽

2021 ◽

Author(s):

Paul Oehlmann ◽

Paul Osswald ◽

Juan Camilo Blanco ◽

Martin Friedrich ◽

Dominik Rietzel ◽

...

Keyword(s):

Real Time ◽

Large Scale ◽

Cost Effective ◽

Print Quality ◽

Ann Model ◽

Production Environment ◽

Fused Filament Fabrication ◽

Artificial Neural ◽

Using Data ◽

Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

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Nebula: ultra-efficient mapping-free structural variant genotyper

Nucleic Acids Research ◽

10.1093/nar/gkab025 ◽

2021 ◽

Author(s):

Parsoa Khorsand ◽

Fereydoun Hormozdiari

Keyword(s):

Large Scale ◽

Structural Variants ◽

Sequencing Technologies ◽

Generic Framework ◽

Common Genetic Variants ◽

Order Of Magnitude ◽

Complex Events ◽

Comparable Accuracy ◽

Using Data ◽

Computational Resources

Abstract Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

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EPCO-29. EPIGENOMICS OF THE GLIOMA LONGITUDINAL ANALYSIS (GLASS) CONSORTIUM

Neuro-Oncology ◽

10.1093/neuonc/noaa215.308 ◽

2020 ◽

Vol 22 (Supplement_2) ◽

pp. ii75-ii75

Author(s):

Thais Sabedot ◽

Michael Wells ◽

Indrani Datta ◽

Tathiane Malta ◽

Ana Valeria Castro ◽

...

Keyword(s):

Dna Methylation ◽

Longitudinal Analysis ◽

Copy Number ◽

Large Scale ◽

Molecular Classification ◽

Tumor Burden ◽

Copy Number Alterations ◽

Unsupervised Analysis ◽

Diffuse Gliomas ◽

New Biomarkers

Abstract Adult diffuse gliomas are central nervous system (CNS) tumors that arise from the malignant transformation of glial cells. Nearly all gliomas will recur despite standard treatment however, current histopathological grading fails to predict which of them will relapse and/or progress. The Glioma Longitudinal AnalySiS (GLASS) consortium is a large-scale collaboration that aims to investigate the molecular profiling of matched primary and recurrent glioma samples from multiple institutions in order to better understand the dynamic evolution of these tumors. At this time, the cohort comprises 946 samples across 11 institutions and among those, 864 have DNA methylation data available. The current molecular classification based on 7 subtypes published by TCGA in 2016 was applied to the dataset. Among the IDH wildtype tumors, 33% (16/49) of the patients showed a change of subtype upon recurrence, whereas most of them (9/16) were Classic-like at the primary stage but changed to either Mesenchymal-like or PA-like at the recurrent level. Among the IDH mutant tumors, 15% (22/142) showed a change of subtype at recurrent stage, in which 16 out of 22 progressed from G-CIMP-high to G-CIMP-low. Although some tumors progressed to a different subtype upon recurrence, an unsupervised analysis showed that the samples tend to cluster by patient instead of by subtype. By estimating the copy number alterations of these tumors using DNA methylation, the overall copy number profile of the recurrent samples remains similar to their primary counterpart. From this initial analysis using epigenomic data, we were able to characterize some aspects of glioma evolution and how the DNA methylation is associated with the progression of these tumors to different subtypes. These findings corroborate the importance of epigenetics in gliomas and can potentially lead to the identification of new biomarkers that can reflect tumor burden and predict its development.

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The Effect of Alcohol on Telomere Length: A Systematic Review of Epidemiological Evidence and a Pilot Study during Pregnancy

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18095038 ◽

2021 ◽

Vol 18 (9) ◽

pp. 5038

Author(s):

Andrea Maugeri ◽

Martina Barchitta ◽

Roberta Magnano San Lio ◽

Maria Clara La Rosa ◽

Claudia La Mastra ◽

...

Keyword(s):

Systematic Review ◽

Pilot Study ◽

Alcohol Consumption ◽

Telomere Length ◽

Molecular Mechanisms ◽

Epidemiological Studies ◽

Potential Association ◽

Significant Difference ◽

Periconceptional Period ◽

Using Data

Several studies—albeit with still inconclusive and limited findings—began to focus on the effect of drinking alcohol on telomere length (TL). Here, we present results from a systematic review of these epidemiological studies to investigate the potential association between alcohol consumption, alcohol-related disorders, and TL. The analysis of fourteen studies—selected from PubMed, Medline, and Web of Science databases—showed that people with alcohol-related disorders exhibited shorter TL, but also that alcohol consumption per se did not appear to affect TL in the absence of alcohol abuse or dependence. Our work also revealed a lack of studies in the periconceptional period, raising the need for evaluating this potential relationship during pregnancy. To fill this gap, we conducted a pilot study using data and samples form the Mamma & Bambino cohort. We compared five non-smoking but drinking women with ten non-smoking and non-drinking women, matched for maternal age, gestational age at recruitment, pregestational body mass index, and fetal sex. Interestingly, we detected a significant difference when analyzing relative TL of leukocyte DNA of cord blood samples from newborns. In particular, newborns from drinking women exhibited shorter relative TL than those born from non-drinking women (p = 0.024). Although these findings appeared promising, further research should be encouraged to test any dose–response relationship, to adjust for the effect of other exposures, and to understand the molecular mechanisms involved.

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Assisting scalable diagnosis automatically via CT images in the combat against COVID-19

Scientific Reports ◽

10.1038/s41598-021-83424-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Bohan Liu ◽

Pan Liu ◽

Lutao Dai ◽

Yanlin Yang ◽

Peng Xie ◽

...

Keyword(s):

Large Scale ◽

Area Under The Curve ◽

Independent Set ◽

Reference Method ◽

Ct Images ◽

Loss Of Life ◽

Case Identification ◽

Reverse Transcription Pcr ◽

Using Data

AbstractThe pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

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Functions and clinical significance of KLRG1 in the development of lung adenocarcinoma and immunotherapy

BMC Cancer ◽

10.1186/s12885-021-08510-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xiaodong Yang ◽

Yuexin Zheng ◽

Zhihai Han ◽

Xiliang Zhang

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Lung Adenocarcinoma ◽

Molecular Mechanisms ◽

Killer Cell ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Normal Lung ◽

Using Data ◽

Low Expression

Abstract Background As a marker of differentiation, Killer cell lectin like receptor G1 (KLRG1) plays an inhibitory role in human NK cells and T cells. However, its clinical role remains inexplicit. This work intended to investigate the predictive ability of KLRG1 on the efficacy of immune-checkpoint inhibitor in the treatment of lung adenocarcinoma (LUAD), as well as contribute to the possible molecular mechanisms of KLRG1 on LUAD development. Methods Using data from the Gene Expression Omnibus, the Cancer Genome Atlas and the Genotype-Tissue Expression, we compared the expression of KLRG1 and its related genes Bruton tyrosine kinase (BTK), C-C motif chemokine receptor 2 (CCR2), Scm polycomb group protein like 4 (SCML4) in LUAD and normal lung tissues. We also established stable LUAD cell lines with KLRG1 gene knockdown and investigated the effect of KLRG1 knockdown on tumor cell proliferation. We further studied the prognostic value of the four factors in terms of overall survival (OS) in LUAD. Using data from the Gene Expression Omnibus, we further investigated the expression of KLRG1 in the patients with different responses after immunotherapy. Results The expression of KLRG1, BTK, CCR2 and SCML4 was significantly downregulated in LUAD tissues compared to normal controls. Knockdown of KLRG1 promoted the proliferation of A549 and H1299 tumor cells. And low expression of these four factors was associated with unfavorable overall survival in patients with LUAD. Furthermore, low expression of KLRG1 also correlated with poor responses to immunotherapy in LUAD patients. Conclusion Based on these findings, we inferred that KLRG1 had significant correlation with immunotherapy response. Meanwhile, KLRG1, BTK, CCR2 and SCML4 might serve as valuable prognostic biomarkers in LUAD.

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