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Hammelman, Jennifer, and David K. Gifford. "Discovering differential genome sequence activity with interpretable and efficient deep learning." PLOS Computational Biology 17, no. 8 (August 9, 2021): e1009282. http://dx.doi.org/10.1371/journal.pcbi.1009282.
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Discovering sequence features that differentially direct cells to alternate fates is key to understanding both cellular development and the consequences of disease related mutations. We introduce Expected Pattern Effect and Differential Expected Pattern Effect, two black-box methods that can interpret genome regulatory sequences for cell type-specific or condition specific patterns. We show that these methods identify relevant transcription factor motifs and spacings that are predictive of cell state-specific chromatin accessibility. Finally, we integrate these methods into framework that is readily accessible to non-experts and available for download as a binary or installed via PyPI or bioconda at https://cgs.csail.mit.edu/deepaccess-package/.
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Chabbert, Christophe D., Lars M. Steinmetz, and Bernd Klaus. "DChIPRep, an R/Bioconductor package for differential enrichment analysis in chromatin studies." PeerJ 4 (April 26, 2016): e1981. http://dx.doi.org/10.7717/peerj.1981.
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The genome-wide study of epigenetic states requires the integrative analysis of histone modification ChIP-seq data. Here, we introduce an easy-to-use analytic framework to compare profiles of enrichment in histone modifications around classes of genomic elements, e.g. transcription start sites (TSS). Our framework is available via the user-friendly R/Bioconductor packageDChIPRep.DChIPRepuses biological replicate information as well as chromatin Input data to allow for a rigorous assessment of differential enrichment.DChIPRepis available for download through the Bioconductor project athttp://bioconductor.org/packages/DChIPRep.Contact.DChIPRep@gmail.com.
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Deller, J. R., Hayder Radha, and J. Justin McCormick. "Exploiting Identifiability and Intergene Correlation for Improved Detection of Differential Expression." ISRN Bioinformatics 2013 (June 3, 2013): 1–15. http://dx.doi.org/10.1155/2013/404717.
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Accurate differential analysis of microarray data strongly depends on effective treatment of intergene correlation. Such dependence is ordinarily accounted for in terms of its effect on significance cutoffs. In this paper, it is shown that correlation can, in fact, be exploited to share information across tests and reorder expression differentials for increased statistical power, regardless of the threshold. Significantly improved differential analysis is the result of two simple measures: (i) adjusting test statistics to exploit information from identifiable genes (the large subset of genes represented on a microarray that can be classified a priori as nondifferential with very high confidence], but (ii) doing so in a way that accounts for linear dependencies among identifiable and nonidentifiable genes. A method is developed that builds upon the widely used two-sample t-statistic approach and uses analysis in Hilbert space to decompose the nonidentified gene vector into two components that are correlated and uncorrelated with the identified set. In the application to data derived from a widely studied prostate cancer database, the proposed method outperforms some of the most highly regarded approaches published to date. Algorithms in MATLAB and in R are available for public download.
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Ma, Guowei, Mingyan Liu, Ke Du, Xin Zhong, Shiqiang Gong, Linchi Jiao, and Minjie Wei. "Differential Expression of mRNAs in the Brain Tissues of Patients with Alzheimer’s Disease Based on GEO Expression Profile and Its Clinical Significance." BioMed Research International 2019 (February 26, 2019): 1–9. http://dx.doi.org/10.1155/2019/8179145.
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Background. Early diagnosis of Alzheimer’s disease (AD) is an urgent point for AD prevention and treatment. The biomarkers of AD still remain indefinite. Based on the bioinformatics analysis of mRNA differential expressions in the brain tissues and the peripheral blood samples of Alzheimer’s disease (AD) patients, we investigated the target mRNAs that could be used as an AD biomarker and developed a new effective, practical clinical examination program. Methods. We compared the AD peripheral blood mononuclear cells (PBMCs) expression dataset (GEO accession GSE4226 and GSE18309) with AD brain tissue expression datasets (GEO accessions GSE1297 and GSE5281) from GEO in the present study. The GEO gene database was used to download the appropriate gene expression profiles to analyze the differential mRNA expressions between brain tissue and blood of AD patients and normal elderly. The Venn diagram was used to screen out the differential expression of mRNAs between the brain tissue and blood. The protein-protein interaction network map (PPI) was used to view the correlation between the possible genes. GO (gene ontology) and KEGG (Kyoto Gene and Genomic Encyclopedia) were used for gene enrichment analysis to determine the major affected genes and the function or pathway. Results. Bioinformatics analysis revealed that there were differentially expressed genes in peripheral blood and hippocampus of AD patients. There were 4958 differential mRNAs in GSE18309, 577 differential mRNAs in GSE4226 in AD PBMCs sample, 7464 differential mRNAs in GSE5281, and 317 differential mRNAs in GSE129 in AD brain tissues, when comparing between AD patients and healthy elderly. Two mRNAs of RAB7A and ITGB1 coexpressed in hippocampus and peripheral blood were screened. Furthermore, functions of differential genes were enriched by the PPI network map, GO, and KEGG analysis, and finally the chemotaxis, adhesion, and inflammatory reactions were found out, respectively. Conclusions. ITGB1 and RAB7A mRNA expressions were both changed in hippocampus and PBMCs, highly suggested being used as an AD biomarker with AD. Also, according to the results of this analysis, it is indicated that we can test the blood routine of the elderly for 2-3 years at a frequency of 6 months or one year. When a patient continuously detects the inflammatory manifestations, it is indicated as a potentially high-risk AD patient for AD prevention.
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Tran, Stephen S., Qing Zhou, and Xinshu Xiao. "Statistical inference of differential RNA-editing sites from RNA-sequencing data by hierarchical modeling." Bioinformatics 36, no. 9 (January 31, 2020): 2796–804. http://dx.doi.org/10.1093/bioinformatics/btaa066.
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Abstract Motivation RNA-sequencing (RNA-seq) enables global identification of RNA-editing sites in biological systems and disease. A salient step in many studies is to identify editing sites that statistically associate with treatment (e.g. case versus control) or covary with biological factors, such as age. However, RNA-seq has technical features that incumbent tests (e.g. t-test and linear regression) do not consider, which can lead to false positives and false negatives. Results In this study, we demonstrate the limitations of currently used tests and introduce the method, RNA-editing tests (REDITs), a suite of tests that employ beta-binomial models to identify differential RNA editing. The tests in REDITs have higher sensitivity than other tests, while also maintaining the type I error (false positive) rate at the nominal level. Applied to the GTEx dataset, we unveil RNA-editing changes associated with age and gender, and differential recoding profiles between brain regions. Availability and implementation REDITs are implemented as functions in R and freely available for download at https://github.com/gxiaolab/REDITs. The repository also provides a code example for leveraging parallelization using multiple cores.
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Ovchinnikov, Alexey, Isabel Pérez Verona, Gleb Pogudin, and Mirco Tribastone. "CLUE: exact maximal reduction of kinetic models by constrained lumping of differential equations." Bioinformatics 37, no. 12 (February 3, 2021): 1732–38. http://dx.doi.org/10.1093/bioinformatics/btab010.
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Abstract Motivation Detailed mechanistic models of biological processes can pose significant challenges for analysis and parameter estimations due to the large number of equations used to track the dynamics of all distinct configurations in which each involved biochemical species can be found. Model reduction can help tame such complexity by providing a lower-dimensional model in which each macro-variable can be directly related to the original variables. Results We present CLUE, an algorithm for exact model reduction of systems of polynomial differential equations by constrained linear lumping. It computes the smallest dimensional reduction as a linear mapping of the state space such that the reduced model preserves the dynamics of user-specified linear combinations of the original variables. Even though CLUE works with non-linear differential equations, it is based on linear algebra tools, which makes it applicable to high-dimensional models. Using case studies from the literature, we show how CLUE can substantially lower model dimensionality and help extract biologically intelligible insights from the reduction. Availability and implementation An implementation of the algorithm and relevant resources to replicate the experiments herein reported are freely available for download at https://github.com/pogudingleb/CLUE. Supplementary information Supplementary data are available at Bioinformatics online.
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Du, Yajing, Sujuan Yuan, Xibing Zhuang, Qi Zhang, and Tiankui Qiao. "Multiomics Differences in Lung Squamous Cell Carcinoma Patients with High Radiosensitivity Index Compared with Those with Low Radiosensitivity Index." Disease Markers 2021 (August 30, 2021): 1–11. http://dx.doi.org/10.1155/2021/3766659.
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Objectives. Radiosensitivity Index (RSI) can predict intrinsic radiotherapy sensitivity. We analyzed multiomics characteristics in lung squamous cell carcinoma between high and low RSI groups, which may help understand the underlying molecular mechanism of radiosensitivity and guide optional treatment for patients in the future. Methods. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) data were used to download clinical data, mRNA, microRNA, and lncRNA expression. Differential analyses, including mRNA, miRNA, lncRNA, and G.O. and KEGG, and GSVA analyses, were performed with R. Gene set enrichment analysis was done by GSEA. miRNA-differentially expressed gene network and ceRNA network were analyzed and graphed by the Cytoscape software. Results. In TCGA data, 542 patients were obtained, including 171 in the low RSI group (LRSI) and 371 in the high RSI group (HRSI). In RNAseq, 558 significantly differentially expressed genes (DEGs) were obtained. KRT6A was the most significantly upregulated gene and IDO1 was the most significantly downregulated gene. In miRNAseq, miR-1269a was the most significantly upregulated. In lncRNAseq, LINC01871 was the most upregulated. A 66-pair interaction between differentially expressed genes and miRNAs and an 11-pair interaction between differential lncRNAs and miRNAs consisted of a ceRNA network, of which miR-184 and miR-490-3p were located in the center. In the GEO data, there were 40 DEGs. A total of 17 genes were founded in both databases, such as ADAM23, AHNAK2, BST2, COL11A1, CXCL13, FBN2, IFI27, IFI44L, MAGEA6, and PTGR1. GSVA analysis revealed 31 significant pathways. GSEA found 87 gene sets enriched in HRSI and 91 gene sets in LRSI. G.O. and KEGG of RNA expression levels revealed that these genes were most enriched in T cell activation and cytokine−cytokine receptor interaction. Conclusions. Patients with lung squamous cell carcinoma have different multiomics characteristics between two groups. These differences may have an essential significance with radiotherapy effect.
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BREITLING, RAINER, and PAWEL HERZYK. "RANK-BASED METHODS AS A NON-PARAMETRIC ALTERNATIVE OF THE T-STATISTIC FOR THE ANALYSIS OF BIOLOGICAL MICROARRAY DATA." Journal of Bioinformatics and Computational Biology 03, no. 05 (October 2005): 1171–89. http://dx.doi.org/10.1142/s0219720005001442.
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We have recently introduced a rank-based test statistic, RankProducts (RP), as a new non-parametric method for detecting differentially expressed genes in microarray experiments. It has been shown to generate surprisingly good results with biological datasets. The basis for this performance and the limits of the method are, however, little understood. Here we explore the performance of such rank-based approaches under a variety of conditions using simulated microarray data, and compare it with classical Wilcoxon rank sums and t-statistics, which form the basis of most alternative differential gene expression detection techniques.We show that for realistic simulated microarray datasets, RP is more powerful and accurate for sorting genes by differential expression than t-statistics or Wilcoxon rank sums — in particular for replicate numbers below 10, which are most commonly used in biological experiments.Its relative performance is particularly strong when the data are contaminated by non-normal random noise or when the samples are very inhomogenous, e.g. because they come from different time points or contain a mixture of affected and unaffected cells.However, RP assumes equal measurement variance for all genes and tends to give overly optimistic p-values when this assumption is violated. It is therefore essential that proper variance stabilizing normalization is performed on the data before calculating the RP values. Where this is impossible, another rank-based variant of RP (average ranks) provides a useful alternative with very similar overall performance.The Perl scripts implementing the simulation and evaluation are available upon request. Implementations of the RP method are available for download from the authors website ().
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Frodden, Ernesto, and Diego Hidalgo. "Surface charges toolkit for gravity." International Journal of Modern Physics D 29, no. 06 (April 2020): 2050040. http://dx.doi.org/10.1142/s0218271820500406.
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These notes provide a detailed catalog of surface charge formulas for different classes of gravity theories. The present catalog reviews and extends the existing literature on the topic. Part of the focus is on reviewing the method to compute quasi-local surface charges for gauge theories in order to clarify conceptual issues and their range of applicability. Many surface charge formulas for gravity theories are expressed in metric, tetrads-connection, Chern–Simons connection, and even BF variables. For most of them, the language of differential forms is exploited and contrasted with the more popular metric components language. The gravity theory is coupled with matter fields as scalar, Maxwell, Skyrme, Yang–Mills, and spinors. Furthermore, three examples with ready-to-download notebook codes, show the method in full action. Several new results are highlighted through the notes.
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Li, Jun-Chen, Qiu-Han Chen, Rui Jian, Jiang-Rong Zhou, Yun Xu, Fang Lu, Jun-Qiao Li, and Hao Zhang. "The Partial Role of KLF4 and KLF5 in Gastrointestinal Tumors." Gastroenterology Research and Practice 2021 (July 27, 2021): 1–13. http://dx.doi.org/10.1155/2021/2425356.
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Background. KLF4 and KLF5 are members of the KLF transcription factor family, which play an important role in many gastrointestinal tumors. To gain a deeper insight into its function and role, bioinformatics was used to analyze the function and role of KLF4 and KLF5 in gastrointestinal tumors. Methods. Data were collected from several online databases. Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN database analysis, Kaplan-Meier Plotter analysis, LOGpc system, the Pathology Atlas, and the STRING website were used to analyze the data. We download relevant data from TCGA and then perform GO enrichment and KEGG enrichment analysis. The effects of KLF5 on gastric cancer cell proliferation were measured by CCK-8 assay. The effect of KLF5 on the expression of CyclinD1 and MMP9 was detected by Western blot. Results. KLF4 and KLF5 were differentially expressed in normal and tumor tissues of the gastrointestinal tract, and their differential expression is related to several genes or pathways. KEGG analysis showed that KLF5 was coexpressed with endocytosis-related genes. KLF5 promotes the proliferation of gastric cancer cells and the expression of metastasis-related molecules. Conclusion. KLF4 and KLF5 are of great significance for developing gastrointestinal tumors and can be used as therapeutic targets.
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Farrell, Fionnuala, and Michael Carr. "The effect of using a project-based learning (PBL) approach to improve engineering students’ understanding of statistics." Teaching Mathematics and its Applications: An International Journal of the IMA 38, no. 3 (August 26, 2019): 135–45. http://dx.doi.org/10.1093/teamat/hrz005.
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Abstract Over the last number of years we have gradually been introducing a project based learning approach to the teaching of engineering mathematics in Dublin Institute of Technology. Several projects are now in existence for the teaching of both second-order differential equations and first order differential equations. We intend to incrementally extend this approach across more of the engineering mathematics curriculum. As part of this ongoing process, practical real-world projects in statistics were incorporated into a second year ordinary degree mathematics module. This paper provides an overview of these projects and their implementation. As a means to measure the success of this initiative, we used the SALG instrument to gain feedback from the students. The SALG online tool - Student Assessment of their Learning Gains - https://salgsite.net/; is a free course-evaluation tool that enables third-level educators to gather feedback specifically focused on what the students gained through the learning exercise they experience. It can be used to measure students’ learning gains. Pre-developed surveys are available which can be modified and are stored in a repository for ease of access. Results are anonymous and there is the ability to download comments and basic statistical analysis of responses. Feedback from the survey points to a large increase in understanding of the material coupled with an increase in confidence. In addition we outline some of the limitations of our initial implementation of this approach and what we hope to improve on for the next academic year.
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Stępień, Paweł, Małgorzata Serowik, Jacek A. Koziel, and Andrzej Białowiec. "Waste to Carbon Energy Demand Model and Data Based on the TGA and DSC Analysis of Individual MSW Components." Data 4, no. 2 (April 19, 2019): 53. http://dx.doi.org/10.3390/data4020053.
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The pioneering developed simplified mathematical model can be used to determine the energy consumption of the torrefaction process. Specifically, the energy balance model was developed for torrefaction of municipal solid waste (MSW; a combustible fraction of common municipal waste). Municipalities are adopting waste separation and need tools for energy recovery options. This type of model is needed for initial decision-making, evaluation of cost estimates, life cycle analysis (LCA), and for optimizing the torrefaction of MSW. The MSW inputs are inherently variable and are site-, location-, and country-dependent. Thus, in this model, MSW inputs consist of eight types of common municipal waste components: chicken meat, diapers, gauze, eggs packaging, paper receipts, cotton, genuine leather, and polypropylene. The model uses simple experimental input consisting of thermogravimetric (TGA) and differential scanning calorimetry (DSC) analyses for each type of individual MSW material. The model was created in a Microsoft Office Excel spreadsheet and is available for download and use for site-specific waste mixes and properties. The model allows estimating the energy demand of the process depending on the percentage composition of the MSW and the final torrefaction temperature. The model enables initial optimization of the torrefaction process regarding its energy demand by changing the proportion of MSW mix and the final temperature.
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McTee, Haley M., Deborah Mood, Tammy Fredrickson, Amy Thrasher, and Angela Yarnell Bonino. "Using Visual Supports to Facilitate Audiological Testing for Children With Autism Spectrum Disorder." American Journal of Audiology 28, no. 4 (December 16, 2019): 823–33. http://dx.doi.org/10.1044/2019_aja-19-0047.
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Purpose One in 59 children is diagnosed with autism spectrum disorder (ASD). Due to overlapping symptoms between hearing loss and ASD, children who are suspected of having ASD require an audiological evaluation to determine their hearing status for the purpose of differential diagnosis. The purpose of this article is twofold: (a) to increase audiologists' knowledge of ASD by discussing the challenges associated with testing and interpreting clinical data for children with ASD or suspected ASD and (b) to provide visual supports that can be used to facilitate audiological assessment. Method Eight children (ages 4–12 years) were recruited as video model participants. Videos were filmed using scripts that used concise and concrete language while portraying common clinical procedures. Using the video models, corresponding visual schedules were also created. Conclusion Although obtaining reliable hearing data from children with ASD is challenging, incorporating visual supports may facilitate testing. Video models and visual schedules have been created and made freely available for download online under a Creative Commons License (Creative Commons–Attribution-NonCommercial-ShareAlike 4.0 International License). Incorporating visual supports during clinical testing has the potential to reduce the child's and family's stress, as well as to increase the probability of obtaining a reliable and comprehensive audiological evaluation. Future research is warranted to determine the effectiveness and feasibility of implementing these tools in audiology clinics. Supplemental Material https://doi.org/10.23641/asha.10086434
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Du, Guobo, Jing Zhou, Long Cheng, Xiaojie Ma, Yan Gui, and Bangxian Tan. "High Expression of miR-206 Predicts Adverse Outcomes: A Potential Therapeutic Target for Esophageal Cancer." Combinatorial Chemistry & High Throughput Screening 22, no. 9 (January 1, 2020): 599–611. http://dx.doi.org/10.2174/1386207322666191018145825.
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Background: MicroRNA-206 (miR-206) inhibits cell proliferation, invasion and migration in a variety of tumors, but the prognostic value of its Esophageal Cancer (EC) remains unclear. Objective: To study the role of miR-206 in EC. Methods: The datasets of RNA-Seq, miRNA-Seq, methylation, copy number variation (CNV), and clinical follow-up information were download from The Cancer Genome Atlas (TCGA). After integration and standardization, the prognostic value and potential function of miR-206 were analyzed. The important roles of miR-206 expression in EC genetic and epigenetic mechanisms were analyzed by RNA-Seq, miRNA-Seq, and methylation data. The potential mechanism of CNV in different miR-206 expression groups was analyzed using GISTIC. Results: High expression of miR-206 was associated with poor outcome of EC (OS: p=0.005, AUC=0.69, N=178). Transforming growth factor β (TGF-β) signaling pathway, Wnt signaling pathway, mitogen-activated protein kinases (MAPK) signaling pathway, mammalian target of rapamycin (mTOR) signaling pathway were inhibited in high expression group. the aberrant methylation sites in the high and low expression groups were mainly distributed in the promoter region containing CpG islands, and there were different copy number patterns in the H and L samples, and the genes in the differential copy number were mainly enriched in cancer-related pathways, such as thyroid cancer, central carbon metabolism. Conclusion: This study explored the unique genomic and epigenetic landscape associated with the expression of miR-206, provided evidence of mir-206 as a prognostic biomarker or a potential therapeutic target for EC patients.
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Bhardwaj, Ashutosh. "Assessment of Vertical Accuracy for TanDEM-X 90 m DEMs in Plain, Moderate, and Rugged Terrain." Proceedings 24, no. 1 (June 5, 2019): 8. http://dx.doi.org/10.3390/iecg2019-06208.
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Synthetic Aperture Radar (SAR) interferometry technique generates digital elevation models (DEMs) and is used by various agencies widely. The recently released TanDEM-X DEM by DLR at 90 m spatial resolution is available for free download to users. This paper examines the accuracy of TanDEM-X DEM at different experimental sites with different topographic characteristics. Three sites were chosen, namely Kendrapara (Odisha), Jaipur (Rajasthan), and Dehradun (Uttarakhand) with plain, moderate, and highly undulating terrain conditions. The root mean square error (RMSE) were calculated using ground control points (GCPs) collected by differential GPS method for experimental sites at Dehradun, Jaipur, and Kendrapara. The accuracy of TanDEM-X 90 m datasets is compared with other openly accessible optically-derived DEMs (ASTER GDEM V2, CartoDEM V3 R1, AW3D30) and InSAR-derived DEMs (SRTM, ALOS PALSAR RTC HR). The RMSEs reveal that at Jaipur site with moderate terrain with urban and agriculture as major land use land cover (LULC) classes, the results of TanDEM-X 90 m DEM have higher accuracy than ALOS PALSAR RTC HR DEM. However, it is observed that in a predominantly plain region with agriculture practice (Kendrapara site, Odisha) and rugged region (Dehradun site, Uttarakhand) with mixed land use land cover (LULC) (e.g., forest, urban, streams, and agriculture) the results of ALOS PALSAR RTC HR data have higher accuracy than TanDEM-X 90 m DEM. Further, the study indicates that for a relatively plain site at Kendrapara (Orissa), CartoDEM V3 R1 DEM has the best performance with an RMSE of 1.96 m, which is the lowest among all DEMs utilized in the study.
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Obermayer, Benedikt, Manuel Holtgrewe, Mikko Nieminen, Clemens Messerschmidt, and Dieter Beule. "SCelVis: exploratory single cell data analysis on the desktop and in the cloud." PeerJ 8 (February 19, 2020): e8607. http://dx.doi.org/10.7717/peerj.8607.
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Background Single cell omics technologies present unique opportunities for biomedical and life sciences from lab to clinic, but the high dimensional nature of such data poses challenges for computational analysis and interpretation. Furthermore, FAIR data management as well as data privacy and security become crucial when working with clinical data, especially in cross-institutional and translational settings. Existing solutions are either bound to the desktop of one researcher or come with dependencies on vendor-specific technology for cloud storage or user authentication. Results To facilitate analysis and interpretation of single-cell data by users without bioinformatics expertise, we present SCelVis, a flexible, interactive and user-friendly app for web-based visualization of pre-processed single-cell data. Users can survey multiple interactive visualizations of their single cell expression data and cell annotation, define cell groups by filtering or manual selection and perform differential gene expression, and download raw or processed data for further offline analysis. SCelVis can be run both on the desktop and cloud systems, accepts input from local and various remote sources using standard and open protocols, and allows for hosting data in the cloud and locally. We test and validate our visualization using publicly available scRNA-seq data. Methods SCelVis is implemented in Python using Dash by Plotly. It is available as a standalone application as a Python package, via Conda/Bioconda and as a Docker image. All components are available as open source under the permissive MIT license and are based on open standards and interfaces, enabling further development and integration with third party pipelines and analysis components. The GitHub repository is https://github.com/bihealth/scelvis.
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Figueredo, Grazziela P., Tanvi V. Joshi, James M. Osborne, Helen M. Byrne, and Markus R. Owen. "On-lattice agent-based simulation of populations of cells within the open-source Chaste framework." Interface Focus 3, no. 2 (April 6, 2013): 20120081. http://dx.doi.org/10.1098/rsfs.2012.0081.
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Over the years, agent-based models have been developed that combine cell division and reinforced random walks of cells on a regular lattice, reaction–diffusion equations for nutrients and growth factors; and ordinary differential equations for the subcellular networks regulating the cell cycle. When linked to a vascular layer, this multiple scale model framework has been applied to tumour growth and therapy. Here, we report on the creation of an agent-based multi-scale environment amalgamating the characteristics of these models within a Virtual Physiological Human (VPH) Exemplar Project. This project enables reuse, integration, expansion and sharing of the model and relevant data. The agent-based and reaction–diffusion parts of the multi-scale model have been implemented and are available for download as part of the latest public release of Chaste (Cancer, Heart and Soft Tissue Environment; http://www.cs.ox.ac.uk/chaste/), part of the VPH Toolkit (http://toolkit.vph-noe.eu/). The environment functionalities are verified against the original models, in addition to extra validation of all aspects of the code. In this work, we present the details of the implementation of the agent-based environment, including the system description, the conceptual model, the development of the simulation model and the processes of verification and validation of the simulation results. We explore the potential use of the environment by presenting exemplar applications of the ‘what if’ scenarios that can easily be studied in the environment. These examples relate to tumour growth, cellular competition for resources and tumour responses to hypoxia (low oxygen levels). We conclude our work by summarizing the future steps for the expansion of the current system.
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Torma, Péter, and Chin Wu. "Temperature and Circulation Dynamics in a Small and Shallow Lake: Effects of Weak Stratification and Littoral Submerged Macrophytes." Water 11, no. 1 (January 12, 2019): 128. http://dx.doi.org/10.3390/w11010128.
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In this paper, the effects of littoral submerged macrophytes on weak stratification conditions in a small and shallow lake are investigated. Diverse submerged macrophytes occupying a large portion of the littoral zone act as resistance to water motions and affect lake hydrodynamics. Strong solar radiation and mild wind forcing typically occurring during the summer season result in weak stratification characterized by a diurnal cycle with a temperature differential of 1–3 °C. Temperature and circulation dynamics of a small and shallow lake are depicted by extensive field measurements and a three-dimensional non-hydrostatic model with a generic length scale (GLS) approach for the turbulence closure and drag forces induced by macrophytes. Results show that the effects of macrophytes on velocity profiles are apparent. In the pelagic area, the circulation patterns with and without macrophytes are similar. The velocity profile is generally characterized by a two-layer structure with the maximum velocity at both the water surface and the mid-depth. In contrast, inside the littoral zone, the mean flow is retarded by macrophytes and the velocity profile is changed to only one maximum velocity at the surface with a steeper decrease until 2.0 m depth and another slight decrease to the lake bottom. From the whole lake perspective, littoral macrophytes dampen the horizontal water temperature difference between the upwind side and download side of the lake. Macrophytes promote a stronger temperature stratification by retarding mean flows and reducing vertical mixing. Overall, this study shows that the temperature structures and circulation patterns under weak stratification conditions in a small and shallow lake are strongly affected by littoral vegetation.
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Sun, Heng, Bowen Sui, Yu Li, Jun Yan, Mingming Cao, Lijia Zhang, and Songjiang Liu. "Analysis of the Significance of Immune Cell Infiltration and Prognosis of Non-Small-Cell Lung Cancer by Bioinformatics." Journal of Healthcare Engineering 2021 (September 22, 2021): 1–8. http://dx.doi.org/10.1155/2021/3284186.
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Objective. To perform gene set enrichment analysis (GSEA) and analysis of immune cell infiltration on non-small-cell lung cancer (NSCLC) expression profiling microarray data based on bioinformatics, construct TICS scoring model to distinguish prognosis time, screen key genes and cancer-related pathways for NSCLC treatment, explore differential genes in NSCLC patients, predict potential therapeutic targets for NSCLC, and provide new directions for the treatment of NSCLC. Methods. Transcriptome data of 81 NSCLC patients and the GEO database were used to download matching clinical data (access number: GSE120622). Form the expression of non-small cell lung cancer (NSCLC). TICS values were calculated and grouped according to TICS values, and we used mRNA expression profile data to perform GSEA in non-small-cell lung cancer patients. Biological process (GO) analysis and DAVID and KOBAS were used to undertake pathway enrichment (KEGG) analysis of differential genes. Use protein interaction (PPI) to analyze the database STRING, and construct a PPI network model of target interaction. Results. We obtained 6 significantly related immune cells including activated B cells through the above analysis (Figure 1(b), p < 0.001 ). Based on the TICS values of significantly correlated immune cells, 41 high-risk and 40 low-risk samples were obtained. TICS values and immune score values were subjected to Pearson correlation coefficient calculation, and TICS and IMS values were found to be significantly correlated (Cor = 0.7952). Based on non-small-cell lung cancer mRNA expression profile data, a substantial change in mRNA was found between both the high TICS group as well as the low TICS group (FDR 0.01, FC > 2). The researchers discovered 730 mRNAs that were considerably upregulated in the high TICS group and 121 mRNAs that were considerably downregulated in the low TICS group. High confidence edges (combined score >0.7) were selected using STRING data; then, 191 mRNAs were matched to the reciprocal edges; finally, an undirected network including 164 points and 777 edges was constructed. Important members of cellular chemokine-mediated signaling pathways, such as CCL19, affect patient survival time. Conclusion. (1) The longevity of patients with non-small-cell lung cancer was substantially connected with the presence of immature B cells, activated B cells, MDSC, effector memory CD4 T cells, eosinophils, and regulatory T cells. (2) Immune-related genes such as CX3CR1, CXCR4, CXCR5, and CCR7, which are associated with the survival of NSCLC, affect the prognosis of NSCLC patients by regulating the immune process.
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García Heras, Víctor Alberto. "El pan y el oro. Consumo diferenciado en la ciudad de Cuenca durante la Guerra de Sucesión española = Brad and gold. Differential human consumption in the city of Cuenca during de War of Spanish Succession." Estudios Humanísticos. Historia, no. 15 (June 6, 2017): 29. http://dx.doi.org/10.18002/ehh.v0i15.5040.
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<p>El presente artículo tiene por objetivo poner de manifiesto las prácticas de consumo en el interior de Castilla durante un periodo bélico y convulso como la guerra de Sucesión. Para llevarlo a cabo hemos trabajado la diversa documentación<br />municipal contenida en el Archivo Histórico Municipal de Cuenca, los protocolos notariales depositados en el Archivo Histórico Provincial de Cuenca y documentación<br />proveniente del Archivo Histórico Nacional. El trabajo con toda la documentación ha consistido en la descarga y comparación sistemática de las informaciones referentes a<br />prácticas de consumo, abastecimiento de la población, precios, carestías, etc., y por lo que respecta a la documentación notarial se han trabajado los testamentos e inventarios de bienes que podían aportar información sobre las prácticas de consumo de las élites. Hemos podido constatar que la falta de abastecimiento de algunos productos, sobre todo del trigo, supone un importante factor de inestabilidad y descontento social. De la misma<br />forma y de manera paralela, las élites locales llevaban a cabo prácticas de consumo que las diferenciaba tanto económica como simbólicamente del resto de la población</p><p><strong>Abstract</strong></p><p>The objective of this article is to reveal the practices of human consumption in the interior of Castile during a war and convulsive period as the War of Spanish Succession. In order to carry it out, we have worked on various local documents from the Archivo Municipal de Cuenca, Notary Public Documents filed in the Archivo Histórico Provincial de Cuenca and documentation from the Archivo Histórico<br />Nacional. All this documentation has allowed a systematic download and comparison of information related to practices of human consumption, population supply, prices,<br />shortages, etc. Regarding Notary Public Documentation, we have worked on wills and goods stocks to obtain information on human consumption practices of elites. We have<br />been able to confirm that lack of supply of some products, especially wheat, was an important factor of instability and social unrest. In the same way and in parallel, local<br />elites carried out human consumption practices well differentiated economically and symbolically from the rest of the population</p>
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Ivanov, Nikolay A., Nadia Dahmane, Jeffrey P. Greenfield, and Christopher E. Mason. "4565 Sex-Specific Differences in the Genomic Landscape of Pediatric and Adult Glioblastoma." Journal of Clinical and Translational Science 4, s1 (June 2020): 112–13. http://dx.doi.org/10.1017/cts.2020.343.
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OBJECTIVES/GOALS: It has been previously shown that pediatric high-grade glioma (pHGG) survival is different between sexes. We set out to find out whether there are sex-specific differences in the genomic landscapes of pHGG that may underlie this sex disparity. METHODS/STUDY POPULATION: We downloaded Illumina 450k DNAm data from ArrayExpress and GeneExpressionOmnibus. The minfi package was used to process raw DNAm data. Sex chromosomes and CpGs that are common SNPs were removed. Surrogate variables (SVs) were estimated via the sva Bioconductor package. Differentially methylated CpGs were identified by fitting a multiple linear regression model for the DNAm level at each CpG, with independent variables being sex (a binary variable) and the estimated SVs. RNAseq data was downloaded from Cavatica, and differential gene expression analysis was carried out via the DESeq2 package. RESULTS/ANTICIPATED RESULTS: In the pediatric glioblastoma (GBM) DNAm data [58 female & 91 male IDH wt samples; ages 0.1–21 yrs;], we found 7,371 differentially methylated cytosines (DMCs) at FDR≤0.05. Of the DMCs, 289 had DNAm differences between male and female samples ≥10%. The majority of probes (68%) were in CpG islands, shelves, or shores. We also found 4 differentially methylated regions (DMRs) between sexes (FWER≤0.1). In the adult GBM DNAm samples [32 F & 32 M IDH wt samples; ages 22–75 yrs], we found only 117 DMCs at FDR≤0.05, and no DMRs. In the RNAseq dataset [68 F & 54 M pHGG samples, ages 0.08–30.6 yrs], we found 383 differentially expressed genes (at FDR≤0.05), and 16 of them (4%) overlapped a DMC. DISCUSSION/SIGNIFICANCE OF IMPACT: Our findings demonstrate that pHGG exhibits sex-specific methylome differences. Interestingly, this difference is greater in the pediatric population as compared to adults. The pHGG transcriptome also differs by sex, which may be related to differential DNAm in a minority of cases.
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Liang, Zhen, Shuhua Zhao, Danni Liu, Tianhao Mu, and Jinfeng Chen. "Analysis of genetic mutations associated with immune-activated microenvironment in lung adenocarcinoma." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e20520-e20520. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e20520.
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e20520 Background: Tumor microenvironment plays an important role in suppressing or enhancing immune response. However, the relationship between genetic mutation and prognosis of lung adenocarcinoma (LUAD) patients, and the molecular mechanism of immune microenvironment are not completely clear. Methods: Expression matrix and somatic mutations of TCGA LUAD (n = 509) were downloaded from UCSC Xena. Immune-related gene list was downloaded from ImmPort. We applied R packages ESTIMATE and CIBERSORT to calculate the proportion of tumor-infiltrating immune cell by mRNA data. Samples were divided into ImmuneScore-High or ImmuneScore-Low depending on upper or lower quartiles of ESTIMATE immune infiltration score. Differential expression analysis between ImmuneScore-High and ImmuneScore-Low was performed with R package DESeq2. Results: Samples were divided into ImmuneScore-High (n = 127) and ImmuneScore-Low (n = 128). The immune cell components of these two groups presented different patterns. 10 most frequently mutated immune-related genes were significantly different between two groups (Chi-Squared test, p value < 0.05). 4,139 differentially expressed genes were obtained (|log2FC| > 1, FDR < 0.05). IL-7R, PIK3R5, TRBC2 and VCAM1 were identified with differential mutation frequency and presented differential expression between two groups. Kaplan-Meier plot in UALCAN cancer database showed IL-7R had a significant influence on the prognosis of patients (log rank test, p value = 0.045). Conclusions: ImmuneScore-High and ImmunScore-Low groups presented different genetic mutation frequency and gene expression. Moreover, mutation and different expression of IL-7R may be used as a potential biomarker in the treatment of patients with lung adenocarcinoma.
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Gao, Wei, Jianwei Yang, Changhua Zhuo, Sha Huang, Jinyuan Lin, Guangfeng Wu, and Min Zhou. "A Pipeline to Call Multilevel Expression Changes between Cancer and Normal Tissues and Its Applications in Repurposing Drugs Effective for Gastric Cancer." BioMed Research International 2020 (August 6, 2020): 1–12. http://dx.doi.org/10.1155/2020/3451610.
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Differential gene analyses on gastric cancer usually focus on expression change of single genes between tumor and adjacent normal tissues. However, besides changes on single genes, there are also coexpression and expression network module changes during the development of gastric cancer. In this study, we proposed a pipeline to investigate various levels of changes between gastric cancer and adjacent normal tissues, which were used to repurpose potential drugs for treating gastric cancer. Specifically, we performed a series of analyses on 242 gastric cancer samples (33-normal, 209-cancer) downloaded from the cancer genome atlas (TCGA), including data quality control, differential gene analysis, gene coexpression network analysis, module function enrichment analysis, differential coexpression analysis, differential pathway analysis, and screening of potential therapeutic drugs. In the end, we discovered some genes and pathways that are significantly different between cancer and adjacent normal tissues (such as the interleukin-4 and interleukin-13 signaling pathway) and screened perturbed genes by 2703 drugs that have a high overlap with the identified differentially expressed genes. Our pipeline might be useful for understanding cancer pathogenesis as well as gastric cancer treatment.
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Zhang, Delong, Huanggui Zhou, Jun Liu, and Jie Mao. "Long Noncoding RNA ASB16-AS1 Promotes Proliferation, Migration, and Invasion in Glioma Cells." BioMed Research International 2019 (March 4, 2019): 1–10. http://dx.doi.org/10.1155/2019/5437531.
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Glioma is a lethal, malignant intracranial tumor that becomes progressively common. It has been shown that long noncoding RNAs (lncRNAs) serve important roles in numerous diseases such as gliomas. lncRNAs can regulate the expression of targeted genes through various mechanisms. To identify a novel lncRNA that may be critical in glioma, the present study downloaded the RNA expression profiles of 171 glioma tissues and 5 normal tissues from The Cancer Genome Atlas (TCGA) database using the TCGAbiolinks package in R. Then, lncRNAs in the downloaded TCGA data were identified using the HUGO Gene Nomenclature Committee (HGNC). Based on the fragments per kilobase million value, differential expression analysis was conducted using the limma package in R. In addition, receiver operating characteristic (ROC) analysis was performed, and the area under the curve (AUC) was evaluated using the ROCR package in R. A total of 178 lncRNAs corresponding to differentially expressed genes with an AUC >0.85 were selected. Upon identifying the differential lncRNAs, ceRNA networks were constructed with these differential lncRNAs using the starbase database. From these networks, the top 10% hub genes were selected. In addition, the present study randomly selected 4 lncRNAs for quantitative polymerase chain reaction validation in tissue samples. The results revealed that lncRNA ASB16-AS1 exhibited significantly differential expression in tissue samples and was significantly associated with tumor staging and grading. Furthermore, the proliferation, invasion, and migration of U87MG and U251 glioblastoma stem-like cells (U87GS, U251GS) were significantly inhibited upon inhibition of ASB16-AS1, and the expression of key proteins in the EMT signaling pathway was affected by knocking down ASB16-AS1. Overall, the present study revealed that lncRNA ASB16-AS1 improves the proliferation, migration, and invasion of glioma cells.
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Liu, Xinhong, Feng Chen, Fang Tan, Fang Li, Ruokun Yi, Dingyi Yang, and Xin Zhao. "Construction of a Potential Breast Cancer-Related miRNA-mRNA Regulatory Network." BioMed Research International 2020 (November 4, 2020): 1–18. http://dx.doi.org/10.1155/2020/6149174.
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Background. Breast cancer is a malignant tumor that occurs in the epithelial tissue of the breast gland and has become the most common malignancy in women. The regulation of the expression of related genes by microRNA (miRNA) plays an important role in breast cancer. We constructed a comprehensive breast cancer-miRNA-gene interaction map. Methods. Three miRNA microarray datasets (GSE26659, GSE45666, and GSE58210) were obtained from the GEO database. Then, the R software “LIMMA” package was used to identify differential expression analysis. Potential transcription factors and target genes of screened differentially expressed miRNAs (DE-miRNAs) were predicted. The BRCA GE-mRNA datasets (GSE109169 and GSE139038) were downloaded from the GEO database for identifying differentially expressed genes (DE-genes). Next, GO annotation and KEGG pathway enrichment analysis were conducted. A PPI network was then established, and hub genes were identified via Cytoscape software. The expression and prognostic roles of hub genes were further evaluated. Results. We found 6 upregulated differentially expressed- (DE-) miRNAs and 18 downregulated DE-miRNAs by analyzing 3 Gene Expression Omnibus databases, and we predicted the upstream transcription factors and downstream target genes for these DE-miRNAs. Then, we used the GEO database to perform differential analysis on breast cancer mRNA and obtained differentially expressed mRNA. We found 10 hub genes of upregulated DE-miRNAs and 10 hub genes of downregulated DE-miRNAs through interaction analysis. Conclusions. In this study, we have performed an integrated bioinformatics analysis to construct a more comprehensive BRCA-miRNA-gene network and provide new targets and research directions for the treatment and prognosis of BRCA.
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Zhang, Hao, Lin Sun, and Xiao Hu. "Macrophages M1-Related Prognostic Signature in Hepatocellular Carcinoma." Journal of Oncology 2021 (September 23, 2021): 1–10. http://dx.doi.org/10.1155/2021/6347592.
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A large number of studies have found that macrophages M1 play an important role in the occurrence and development of tumors. The aim of our study is to explore the causes of differential infiltration of macrophages M1 in hepatocellular carcinoma from the perspective of transcriptome and establish a prognostic model of hepatocellular carcinoma. We downloaded gene expression and clinical data from the public database, estimated the content of macrophages M1 in different samples with R software, and found the different genes between high- and low-infiltration groups. Using differentially expressed genes, we constructed a model composed of 7 genes. The risk score of the model has a good ability to predict the prognosis, has a positive correlation with immune checkpoints, and is closely related to other immune cells and immune function. Our model shows good prognostic function and has wide application value.
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Zheng, Qi, Xiaoyong Wei, Jun Rao, and Cuncai Zhou. "Identification of key miRNAs in the progression of hepatocellular carcinoma using an integrated bioinformatics approach." PeerJ 8 (May 6, 2020): e9000. http://dx.doi.org/10.7717/peerj.9000.
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Backgroud It has been shown that aberrant expression of microRNAs (miRNAs) and transcriptional factors (TFs) is tightly associated with the development of HCC. Therefore, in order to further understand the pathogenesis of HCC, it is necessary to systematically study the relationship between the expression of miRNAs, TF and genes. In this study, we aim to identify the potential transcriptomic markers of HCC through analyzing common microarray datasets, and further establish the differential co-expression network of miRNAs–TF–mRNA to screen for key miRNAs as candidate diagnostic markers for HCC. Method We first downloaded the mRNA and miRNA expression profiles of liver cancer from the GEO database. After pretreatment, we used a linear model to screen for differentially expressed genes (DEGs) and miRNAs. Further, we used weighed gene co-expression network analysis (WGCNA) to construct the differential gene co-expression network for these DEGs. Next, we identified mRNA modules significantly related to tumorigenesis in this network, and evaluated the relationship between mRNAs and TFs by TFBtools. Finally, the key miRNA was screened out in the mRNA–TF–miRNA ternary network constructed based on the target TF of differentially expressed miRNAs, and was further verified with external data set. Results A total of 465 DEGs and 215 differentially expressed miRNAs were identified through differential genes expression analysis, and WGCNA was used to establish a co-expression network of DEGs. One module that closely related to tumorigenesis was obtained, including 33 genes. Next, a ternary network was constructed by selecting 256 pairs of mRNA–TF pairs and 100 pairs of miRNA–TF pairs. Network mining revealed that there were significant interactions between 18 mRNAs and 25 miRNAs. Finally, we used another independent data set to verify that miRNA hsa-mir-106b and hsa-mir-195 are good classifiers of HCC and might play key roles in the progression of HCC. Conclusion Our data indicated that two miRNAs—hsa-mir-106b and hsa-mir-195—are identified as good classifiers of HCC.
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Zhang, Dong, Liying Guan, and Xiaoming Li. "Bioinformatics analysis identifies potential diagnostic signatures for coronary artery disease." Journal of International Medical Research 48, no. 12 (December 2020): 030006052097985. http://dx.doi.org/10.1177/0300060520979856.
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Background Coronary artery disease (CAD) is the leading cause of mortality worldwide. We aimed to screen out potential gene signatures and construct a diagnostic model for CAD. Method We downloaded two mRNA profiles, GSE66360 and GSE60993, and performed analyses of differential expression, gene ontology terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The STRING database was used to identify protein–protein interactions (PPI). PPI network visualization and screening out of key genes were performed using Cytoscape software. Finally, a diagnostic model was constructed. Results A total of 2127 differentially expressed genes (DEGs) were identified in GSE66360, and 527 DEGs in GSE60993. Of the 153 DEGs from both datasets that showed differential expression between CAD patients and controls, 471 biological process terms, 35 cellular component terms, 17 molecular function terms, and 49 KEGG pathways were significantly enriched. The top 20 key genes in the PPI network were identified, and a diagnostic model constructed from five optimal genes that could efficiently separate CAD patients from controls. Conclusion We identified several potential biomarkers for CAD and built a logistic regression model that will provide a valuable reference for future clinical diagnoses and guide therapeutic strategies.
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Ding, Hao, Xiao-Xing Xiong, Guan-Lan Fan, Yue-Xiong Yi, Yu-Rou Chen, Jing-Tao Wang, and Wei Zhang. "The New Biomarker for Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) Based on Public Database Mining." BioMed Research International 2020 (April 13, 2020): 1–9. http://dx.doi.org/10.1155/2020/5478574.
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To reconstruct the ceRNA biological network of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) and to select an appropriate mRNA as a biomarker that could be used for CESC early diagnosis and prognosis evaluation. We downloaded CESC data from the TCGA public database, and statistical analysis was conducted with the R software to find out differential expressed genes encoding for lncRNAs, miRNAs, and mRNAs. The differentially expressed mRNAs (DEmRNAs) screened in the ceRNA network were analyzed for survival to find the mRNAs with significantly linked to the survival prognosis. These mRNAs were searched in the Pathological Atlas to identify the final appropriate mRNAs. Differential expression analysis revealed 773 lncRNAs, 94 miRNAs, and 2466 mRNAs. Survival analysis of DEmRNAs in the ceRNA network indicated that ADGRF4, ANXA8L1, HCAR3, IRF6, and PDE2A (P<0.05) were negatively correlated with survival time. Verification of these six DEmRNAs in the Pathology Atlas indicated that PDE2A was a possible biomarker for CESC patients. PDE2A might be a biomarker for early diagnosis and prognosis evaluation of CESC patients, but due to the lack of available data, further studies may be needed for confirmation.
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Huang, Jin, Xuejing Liang, and Zhenyu Cai. "A Potential ceRNA Network for Neurological Damage in Preterm Infants." BioMed Research International 2021 (August 21, 2021): 1–15. http://dx.doi.org/10.1155/2021/2628824.
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Objective. This study is aimed at identifying key genes involved in neurological damage in preterm infants and at determining their potential circRNA-miRNA-mRNA regulatory mechanisms. Methods. Differentially expressed miRNAs, mRNAs, and circRNAs were downloaded from the GEO database. GO and KEGG enrichment analyses were used to determine possible relevant functions of differentially expressed mRNAs. The TTRUST database was used to predict differential TF-mRNA regulatory relationships. Then, CircMIR, miRDB, TargetScan and miRTarBase were then used to map circRNA/miRNA-TF/mRNA interaction networks. Finally, GSEA enrichment analysis was performed on the core transcription factors. Results. A total of 640 mRNAs, 139 circRNAs, and 206 differentially expressed miRNAs associated with neurological injury in preterm infants were obtained. Based on the findings of Cytoscape and PPI network analysis, the hsa_circ_0008439-hsa-mir-3665-STAT3-MMP3 regulatory axis was established. GSEA analysis revealed that suppressed expression levels of STAT3 were associated with upregulated oxidative phosphorylation pathways in the neurological injury group of preterm infants. Conclusions. The circRNA-miRNA-TF-mRNA regulatory network of neurological injury in preterm infants can be used to elucidate on the pathogenesis of brain injury and help us with the early detection of brain injury in preterm infants.
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Wang, Qingwei, and Weiping Zheng. "Upregulation of CDC7 Associated with Cervical Cancer Incidence and Development." BioMed Research International 2021 (March 3, 2021): 1–9. http://dx.doi.org/10.1155/2021/6663367.
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Background. Cervical cancer is a common malignant tumor of women. Using integrated bioinformatics, this study identified key disease-causing genes in cervical cancer that may provide effective biomarkers or therapeutic targets for early diagnosis and treatment. Results. We used high-throughput sequencing data from the Gene Expression Omnibus (GEO) to identify new cervical cancer biomarkers. The GSE63678 dataset was downloaded. The data was analyzed via bioinformatics methods, and 61 differentially expressed genes were obtained. These differential genes were analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analyses. GO analysis demonstrated that the basic biological functions of differential genes were mostly regulating cell division, mitotic nuclear division, and immune response. Analysis of the KEGG pathway showed the primary involved in the cell cycle, p53 signaling pathway, and cytokine-cytokine receptor interactions. Using TCGA database to query differential expression of differential genes in cervical cancer, the CDC7 gene was found to be highly expressed. In silico analysis of protein interactions using the STRING database revealed that CDC7 interacts with many proteins. These findings were then validated in vitro with immunohistochemistry and qRt-PCR to confirm that CDC7 is highly expressed in cervical cancer tissues. Cell function tests demonstrated that inhibition of CDC7 expression could inhibit the proliferation and migration of cervical cancer HeLa and SiHa cells and promote apoptosis. Conclusion. With comprehensive bioinformatics combined with clinical and cellular function analysis, CDC7 is important to the development of cervical cancer. Targeting of this biomarker may improve the early diagnosis and treatment of cervical cancer.
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Mello, Ana Carolina, Martiela Freitas, Laura Coutinho, Tiago Falcon, and Ursula Matte. "Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma." BioMed Research International 2020 (January 10, 2020): 1–12. http://dx.doi.org/10.1155/2020/3968279.
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Uterine corpus endometrial carcinoma (UCEC) is the second most common type of gynecological tumor. Several research studies have recently shown the potential of different ncRNAs as biomarkers for prognostics and diagnosis in different types of cancers, including UCEC. Thus, we hypothesized that long noncoding RNAs (lncRNAs) could serve as efficient factors to discriminate solid primary (TP) and normal adjacent (NT) tissues in UCEC with high accuracy. We performed an in silico differential expression analysis comparing TP and NT from a set of samples downloaded from the Cancer Genome Atlas (TCGA) database, targeting highly differentially expressed lncRNAs that could potentially serve as gene expression markers. All analyses were performed in R software. The receiver operator characteristics (ROC) analyses and both supervised and unsupervised machine learning indicated a set of 14 lncRNAs that may serve as biomarkers for UCEC. Functions and putative pathways were assessed through a coexpression network and target enrichment analysis.
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Wang, Shuaiqun, Xiaoling Xu, and Wei Kong. "Identification of Hub Genes Associated with Lung Adenocarcinoma Based on Bioinformatics Analysis." Computational and Mathematical Methods in Medicine 2021 (April 16, 2021): 1–12. http://dx.doi.org/10.1155/2021/5550407.
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Lung adenocarcinoma (LUAD) is one of the malignant lung tumors. However, its pathology has not been fully understood. The purpose of this study is to identify the hub genes associated with LUAD by bioinformatics methods. Three gene expression datasets including GSE116959, GSE74706, and GSE85841 downloaded from the Gene Expression Omnibus (GEO) database were used in this study. The differentially expressed genes (DEGs) related to LUAD were screened by using the limma package. Gene Ontology (GO) and KEGG analysis of DEGs were carried out through the DAVID website. The protein-protein interaction (PPI) of differentially expressed genes was drawn by the STRING website, and the results were imported into Cytoscape for visualization. Then, the PPI network was analyzed by using MCODE, and the modules with a score greater than 5 were found by using cytoHubba. Finally, the GEPIA database and UALCAN database were used to verify and analyze the survival of hub genes. We identified 67 upregulated genes and 277 downregulated genes from three LUAD datasets. The results of GO analysis showed that the downregulated genes were significantly enriched in matrix adhesion and angiogenesis and upregulated differential genes were significantly enriched in cell adhesion and vascular development. KEGG pathway analysis showed that the differential genes of LUAD were significantly enriched in viral carcinogenesis and adhesion spots. The PPI network of differentially expressed genes consists of 269 nodes and 625 interactions. In addition, three modules with scores greater than 5 and seven hub genes, namely, MCM4, BIRC5, CDC20, CDC25C, FOXM1, GTSE1, and RFC4, playing an important role in the PPI network were screened out. In this study, we obtained the hub genes and pathways related to LUAD, revealing the molecular mechanism and pathogenesis of LUAD, which is helpful for the early detection of LUAD and provides a new idea for the treatment of LUAD.
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Zhu, Xiaotun, Xiao Liu, Ying Liu, Wansheng Chang, Yanfeng Song, and Shulai Zhu. "Uncovering the Potential Differentially Expressed miRNAs and mRNAs in Ischemic Stroke Based on Integrated Analysis in the Gene Expression Omnibus Database." European Neurology 83, no. 4 (2020): 404–14. http://dx.doi.org/10.1159/000507364.
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Introduction: Ischemic stroke is the third leading cause of death. There is no known treatment or cure for the disease. Moreover, the pathological mechanism of ischemic stroke remains unclear. Objective: We aimed to identify potential microRNAs (miRNAs) and mRNAs, contributing to understanding the pathology of ischemic stroke. Methods: First, the data of miRNA and mRNA were downloaded for differential expression analysis. Then, the regulatory network between miRNA and mRNAs was constructed. Third, top 100 differentially expressed mRNAs were used to construct a protein-protein interaction network followed by the function annotation of mRNAs. In addition, in vitro experiment was used to validate the expression of mRNAs. Last, receiver operating characteristic diagnostic analysis of differentially methylated genes was performed. Results: Totally, up to 26 differentially expressed miRNAs and 1,345 differentially expressed mRNAs were identified. Several regulatory interaction pairs between miRNA and mRNAs were identified, such as hsa-miR-206-HMGCR/PICALM, hsa-miR-4491-TMEM97, hsa-miR-3622b-5p/hsa-miR-548k-KLF12, and hsa-miR-302a-3p/hsa-miR-3145-3p-CTSS. MAPK signaling pathway (involved DUSP1) and the Notch signaling pathway (involved NUMB and CREBBP) were identified. The expression validation of KLF12, ARG1, ITGAM, SIRT4, SERPINH1, and DUSP1 was consistent with the bioinformatics analysis. Interestingly, hsa-miR-206, hsa-miR-4491, hsa-miR-3622b-5p, hsa-miR-548k, hsa-miR-302a-3p, hsa-miR-3145-3p, KLF12, and ID3 had the potential diagnostic value of ischemic stroke. Conclusions: The identified differentially expressed miRNAs and mRNAs may be associated with the development of ischemic stroke.
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Li, Yan-zhen, Hao-jie Xu, Jia-min Hu, Shi-zhu Lin, Na Zhang, Hong-da Cai, Kai Zeng, et al. "Bioinformatic Analysis of Gene Expression Profile in Plasma of Hypertensive Patients." American Journal of Hypertension 33, no. 6 (May 21, 2020): 581. http://dx.doi.org/10.1093/ajh/hpaa040.
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Abstract Background To analyze expression profiles of long noncoding RNA (lncRNA) and messenger RNA (mRNA) in patients with essential hypertension (EH) and normotensive adults. Methods The gene chip dataset GSE76845, which was generated from 5 plasma samples from patients with EH and 5 normotensives, was downloaded from the National Biotechnology Information Center Public Data Platform. Each sample (total RNA) was pooled from the total RNA of 3 age- and gender-matched subjects (EH patients or healthy controls). A ClusterProfiler package including gene set enrichment analysis (GSEA) was used to identify differentially expressed genes. The target microRNA and mRNA were predicted by microcode, microDB, microTarBase, and TargetScan databases. Finally, a competing endogenous RNAs (ceRNA) regulatory network was constructed. Results Compared with the healthy control adults, 191 differential lncRNAs (90 upregulated and 101 downregulated) and 1,187 differential mRNAs (533 upregulated and 654 downregulated) were identified in EH patients. GSEA analysis showed that 17 pathways, including ubiquinone and terpenoid-quinone biosynthesis, parathyroid hormone synthesis secretion and action, fatty acid metabolism, and steroid hormone biosynthesis are involved in hypertension. A ceRNA network consisting of 150 nodes and 488 interactive pairs was constructed. Conclusions lncRNA and mRNA profile analysis provides new insight into molecular mechanisms of EH pathogenesis and potential targets for therapeutic interventions.
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Jin, Jieqi, Mengkai Guang, Anthony Chukwunonso Ogbuehi, Simin Li, Kai Zhang, Yihong Ma, Aneesha Acharya, et al. "Shared Molecular Mechanisms between Alzheimer’s Disease and Periodontitis Revealed by Transcriptomic Analysis." BioMed Research International 2021 (April 1, 2021): 1–22. http://dx.doi.org/10.1155/2021/6633563.
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Objective. To investigate the genetic crosstalk mechanisms that link periodontitis and Alzheimer’s disease (AD). Background. Periodontitis, a common oral infectious disease, is associated with Alzheimer’s disease (AD) and considered a putative contributory factor to its progression. However, a comprehensive investigation of potential shared genetic mechanisms between these diseases has not yet been reported. Methods. Gene expression datasets related to periodontitis were downloaded from the Gene Expression Omnibus (GEO) database, and differential expression analysis was performed to identify differentially expressed genes (DEGs). Genes associated with AD were downloaded from the DisGeNET database. Overlapping genes among the DEGs in periodontitis and the AD-related genes were defined as crosstalk genes between periodontitis and AD. The Boruta algorithm was applied to perform feature selection from these crosstalk genes, and representative crosstalk genes were thus obtained. In addition, a support vector machine (SVM) model was constructed by using the scikit-learn algorithm in Python. Next, the crosstalk gene-TF network and crosstalk gene-DEP (differentially expressed pathway) network were each constructed. As a final step, shared genes among the crosstalk genes and periodontitis-related genes in DisGeNET were identified and denoted as the core crosstalk genes. Results. Four datasets (GSE23586, GSE16134, GSE10334, and GSE79705) pertaining to periodontitis were included in the analysis. A total of 48 representative crosstalk genes were identified by using the Boruta algorithm. Three TFs (FOS, MEF2C, and USF2) and several pathways (i.e., JAK-STAT, MAPK, NF-kappa B, and natural killer cell-mediated cytotoxicity) were identified as regulators of these crosstalk genes. Among these 48 crosstalk genes and the chronic periodontitis-related genes in DisGeNET, C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3 were shared and identified as the most pivotal candidate links between periodontitis and AD. Conclusions. Exploration of available transcriptomic datasets revealed C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3 as the top candidate molecular linkage genes between periodontitis and AD.
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Liu, Guohong, Yunbao Pan, Yueying Li, and Haibo Xu. "lncRNA and mRNA signature for prognosis prediction of glioblastoma." Future Oncology 16, no. 13 (May 2020): 837–48. http://dx.doi.org/10.2217/fon-2019-0538.
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Aims: We aimed to find out potential novel biomarkers for prognosis of glioblastoma (GBM). Materials & methods: We downloaded mRNA and lncRNA expression profiles of 169 GBM and five normal samples from The Cancer Genome Atlas and 129 normal brain samples from genotype-tissue expression. We use R language to perform the following analyses: differential RNA expression analysis of GBM samples using ‘edgeR’ package, survival analysis taking count of single or multiple gene expression level using ‘survival’ package, univariate and multivariate Cox regression analysis using Cox function plugged in ‘survival’ package. Gene ontology and Kyoto encyclopedia of genes and genomes pathway analysis were performed using FunRich tool online. Results and conclusion: We obtained differentially DEmRNAs and DElncRNAs in GBM samples. Most prognostically relevant mRNAs and lncRNAs were filtered out. ‘GPCR ligand binding’ and ‘Class A/1’ are found to be of great significance. In short, our study provides novel biomarkers for prognosis of GBM.
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Cui, Ding, Yang Liu, Junyan Ma, Kaiqing Lin, Kaihong Xu, and Jun Lin. "Identification of key genes and pathways in endometriosis by integrated expression profiles analysis." PeerJ 8 (December 7, 2020): e10171. http://dx.doi.org/10.7717/peerj.10171.
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The purpose of this study was to integrate the existing expression profile data on endometriosis (EM)-related tissues in order to identify the differentially expressed genes. In this study, three series of raw expression data were downloaded from GEO database. Differentially expressed genes (DEGs) in three tissue types were screened. GO, KEGG pathway enrichment analysis, core differential genes (CDGs) protein–protein interaction (PPI) network and weighted gene co-expression network analysis (WGCNA) were performed, finally, the dysregulation of Hippo pathway in ectopic endometrium (EC) was detected by Western blotting. A total of 1,811 DEGs between eutopic (EU) and normal endometrium (NE), 5,947 DEGs between EC and EU, and 3,192 DEGs between EC and NE datasets were identified. After screening, 394 CDGs were obtained, and 5 hub genes identified in the PPI network. CDGs enrichment and WGCNA network analysis revealed cell proliferation, differentiation, migration and other biological processes, Hippo and Wnt signaling pathways, and a variety of tumor-related pathways. Western blotting results showed that YAP/TAZ was upregulated, and MOB1, pMOB1, SAV1, LATS1 and LATS2 were downregulated in EC. Moreover, CDGs, especially the hub genes, are potential biomarkers and therapeutic targets. Finally, the Hippo pathway might play a key role in the development of endometriosis.
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Bharne, Dhammapal, Praveen Kant, and Vaibhav Vindal. "maGUI: A Graphical User Interface for Analysis and Annotation of DNA Microarray Data." Open Bioinformatics Journal 12, no. 1 (June 30, 2019): 40–44. http://dx.doi.org/10.2174/1875036201912010040.
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Summary: maGUI is a graphical user interface designed to analyze microarray data produced from experiments performed on various platforms such as Affymetrix, Agilent, Illumina, and Nimblegen and so on, automatically. It follows an integrated workflow for pre-processing and analysis of the microarray data. The user may proceed from loading of microarray data to normalization, quality check, filtering, differential gene expression, principal component analysis, clustering and classification. It also provides miscellaneous applications such as gene set test and enrichment analysis for identifying gene symbols using Bioconductor packages. Further, the user can build a co-expression network for differentially expressed genes. Tables and figures generated during the analysis can be viewed and exported to local disks. The graphical user interface is very friendly especially for the biologists to perform the most microarray data analyses and annotations without much need of learning R command line programming. Availability and Implementation: maGUI is an R package which can be downloaded freely from Comprehensive R Archive Network resource. It can be installed in any R environment with version 3.0.2 or above.
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Wang, Zhaojun, Haifeng Li, Fajun Li, Xin Su, Junhang Zhang, and Nicola Silvestris. "Bioinformatics-Based Identification of a circRNA-miRNA-mRNA Axis in Esophageal Squamous Cell Carcinomas." Journal of Oncology 2020 (September 29, 2020): 1–9. http://dx.doi.org/10.1155/2020/8813800.
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Background. Esophageal squamous cell carcinoma (ESCC) has a poor prognosis due to the lack of early disease symptoms. Using bioinformatics tools, this study aimed to discover differentially expressed nonprotein-coding RNAs and genes with potential prognostic relevance in ESCC. Methods. Two microRNAs (miRNAs) and one circular RNA (circRNA) microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression of miRNAs (DEMs) and circRNAs (DECs) was, respectively, identified in ESCC tissue and compared to adjacent healthy tissue. Further analysis was performed using the miRNA microarray datasets, where miRTarBase was used to predict which messenger RNAs (mRNAs) was present. This was followed by protein-protein interaction (PPI) network, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) analyses. Moreover, cytoHubba and UALCAN were used to predict the important nodes and perform patient survival analysis, respectively. The miRNA-associated circRNAs were predicted using the ENCORI website. The interaction between DECs and the predicted circRNAs was also determined. A circRNA-miRNA-mRNA axis was constructed. Results. Associated with RAP1B and circ_0052867, two miRNAs (miR-133b and miR-139-5p) were identified as being differentially expressed and downregulated across the two datasets. Finally, the circ_0052867/miR-139-5p/RAP1B regulatory axis was established. Conclusion. This study provides support for the possible mechanisms of disease progression in ESCC.
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Wu, Xuhui, Gongzhi Wu, Huaizhong Zhang, Xuyang Peng, Bin Huang, Mingjiang Huang, Jianyang Ding, Chaofan Mao, and Congxiong Peng. "MiR-196b Promotes the Invasion and Migration of Lung Adenocarcinoma Cells by Targeting AQP4." Technology in Cancer Research & Treatment 20 (January 1, 2021): 153303382098586. http://dx.doi.org/10.1177/1533033820985868.
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Objective: We aimed to investigate the mechanism of the regulatory axis of miR-196b/AQP4 underlying the invasion and migration of lung adenocarcinoma (LUAD) cells. Methods: LUAD miRNA and mRNA expression profiles were downloaded from TCGA database and then differential analysis was used to identify the target miRNA. Target gene for the miRNA was obtained via prediction using 3 bioinformatics databases and intersection with the differentially expressed mRNAs searched from TCGA-LUAD. Then, qRT-PCR and western blot were used to validate the expression of miR-196b and AQP4. Dual-luciferase reporter assay was performed to confirm the targeting relationship between miR-196b and AQP4. Transwell assay was used to investigate the migration and invasion of LUAD cells. Results: MiR-196b was screened out by differential and survival analyses, and the downstream target gene AQP4 was identified. In LUAD, miR-196b was highly expressed while AQP4 was poorly expressed. Besides, overexpression of miR-196b promoted cell invasion and migration, while overexpression of AQP4 had negative effects. Moreover, the results of the dual-luciferase reporter assay suggested that AQP4 was a direct target of miR-196b. In addition, we also found that overexpressing AQP4 could suppress the promotive effect of miR-196b on cancer cell invasion and migration. Conclusion: MiR-196b promotes the invasion and migration of LUAD cells by down-regulating AQP4, which helps us find new molecular targeted therapies for LUAD.
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Chen, Gaoyang, Wenqing Yu, Zhaoyan Li, Qingyu Wang, Qiwei Yang, Zhenwu Du, Guizhen Zhang, and Yang Song. "Potential Regulatory Effects of miR-182-3p in Osteosarcoma via Targeting EBF2." BioMed Research International 2019 (March 12, 2019): 1–9. http://dx.doi.org/10.1155/2019/4897905.
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Osteosarcoma (OS) is one of the most common primary malignant bone tumors in adolescents with a high mortality rate. MicroRNA (miRNA) is a kind of noncoding RNAs and has been proved to participate in many physiological processes. Many miRNAs have been reported to act as function regulators in OS. In our study, the miRNA and gene expression profiles of OS were downloaded from GEO Datasets and the differential expression analysis was performed using GEO2R. 58 up- and 126 downregulated miRNAs were found. In the three OS gene profiles, 125 up- and 27 downregulated genes were found to be differentially expressed in at least two profiles. The miRNA-mRNA networks were constructed to predict the potential target genes of 10 most up- and downregulated miRNA. Venn analysis was used to detect the coexpressed differentially expressed genes (DEGs). EBF2, one of the upregulated DEGs, was also a potential target gene of miR-182-3p. Knockdown and overexpression of miR-182-3p resulted in overexpression and downexpression of EBF2 separately. Luciferase reporter gene experiment further verified the binding site of miR-182-3p and EBF2. CCK8 assay showed that miR-182-3p knockdown can further enhance the proliferation activity of OS cells, while overexpressing miR-182-3p can inhibit the proliferation activity of OS cells. Our research indicated that downexpression of miR-182-3p in OS cells results in overexpression of EBF2 and promotes the progression of OS.
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Zhou, Hui, Wang Min, and Zhihua Zhu. "Comprehensive Study of Different Expressed Genes and Their Functional Modules in Anesthesia for Off-Pump Coronary Artery Bypass Grafting." BioMed Research International 2020 (July 6, 2020): 1–8. http://dx.doi.org/10.1155/2020/8062902.
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Purpose. The effect of preoperative anesthesia on coronary artery bypass grafting without extracorporeal circulation is not apparent. We want to investigate the effects and molecular mechanisms of two anesthesia methods on the treatment of coronary artery bypass grafting (OPCABG) under extracorporeal circulation. Patients and Methods. The data of inhaled anesthesia and intravenous anesthesia before coronary artery bypass grafting were downloaded from the GEO database, and the differences were analyzed with the control group. The combination of multiple analytical methods can decipher the mechanism of anesthesia on surgery, including protein interaction network analysis, enrichment analysis, and regulatory subprediction. Results. This study obtained 6699 differential genes under two kinds of anesthesia before OPCABG. By constructing a protein interaction network of differentially expressed genes, we obtained 14 functional module networks. By predicting regulators of functional module genes, we revealed a series of ncRNAs (miR-129-5p, miR-340-5p, and miR-410-3p) and transcription factors (VHL and YBX1). Conclusion. Based on functional module network analysis, we identified the effects of preoperative inhalation anesthesia and intravenous anesthesia on OPCABG, which provides a valuable theoretical reference for subsequent clinical studies.
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Qiao, X., H. Wang, X. Wang, B. Zhao, and J. Liu. "Microarray technology reveals potentially novel genes and pathways involved in non-functioning pituitary adenomas." Balkan Journal of Medical Genetics 19, no. 2 (December 31, 2016): 5–16. http://dx.doi.org/10.1515/bjmg-2016-0030.
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AbstractMicroarray data of non-functioning pituitary adenomas (NFPAs) were analyzed to disclose novel genes and pathways involved in NFPA tumorigenesis. Raw microarray data were downloaded from Gene Expression Omnibus. Data pre-treatment and differential analysis were conducted using packages in R. Functional and pathway enrichment analyses were performed using package GOs-tats. A protein-protein interaction (PPI) network was constructed using server STRING and Cytoscape. Known genes involved in pituitary adenomas (PAs), were obtained from the Comparative Toxicogenomics Database. A total of 604 differentially expressed genes (DEGs) were identifed between NFPAs and controls, including 177 up- and 427 down-regulated genes. Jak-STAT and p53 signaling pathways were significantly enriched by DEGs. The PPI network of DEGs was constructed, containing 99 up- and 288 down-regulated known disease genes (e.g. EGFR and ESR1) as well as 16 up- and 17 down-regulated potential novel NFPAs-related genes (e.g. COL4A5, LHX3, MSN, and GHSR). Genes like COL4A5, LHX3, MSN, and GHSR and pathways such as p53 signaling and Jak-STAT signaling, might participate in NFPA development. Although further validations are required, these findings might provide guidance for future basic and therapy researches.
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Ruan, Banlai, Xianzhen Feng, Xueyi Chen, Zhiwei Dong, Qi Wang, Kai Xu, Jinping Tian, et al. "Identification of a Set of Genes Improving Survival Prediction in Kidney Renal Clear Cell Carcinoma through Integrative Reanalysis of Transcriptomic Data." Disease Markers 2020 (October 13, 2020): 1–20. http://dx.doi.org/10.1155/2020/8824717.
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Background. With an enormous amount of research concerning kidney cancer being conducted, various treatments have been applied to its cure. However, high recurrence and metastasis rates continue to pose a threat to the survival of patients with kidney renal clear cell carcinoma (KIRC). Methods. Data from The Cancer Genome Atlas were downloaded, and a series of analyses were performed, including differential analysis, Cox analysis, weighted gene coexpression network analysis, least absolute shrinkage and selection operator analysis, multivariate Cox analysis, survival analysis, and receiver operating characteristic curve and functional enrichment analysis. Results. A total of 5,777 differentially expressed genes were identified from the differential analysis. The Cox analysis showed 1,853 significant genes ( P < 0.01 ). Weighted gene coexpression network analysis revealed that 226 genes in the module were related to clinical parameters, including Tumor-Node-Metastasis (TNM) staging. Least absolute shrinkage and selection operator and multivariate Cox analyses suggested that four genes (CDKL2, LRFN1, STAT2, and SOWAHB) had a potential function in predicting the survival time of patients with KIRC. Survival analysis uncovered that a high risk of these four genes was associated with an unfavorable prognosis. Receiver operating characteristic curve analysis further confirmed the accuracy of the risk score model. The analysis of clinicopathological parameters of the four identified genes revealed that they were associated with the progression of KIRC. Conclusion. The gene expression model consisting of CDKL2, LRFN1, STAT2, and SOWAHB is a promising tool for predicting the prognosis of patients with KIRC. The results of this study may provide insights into the diagnosis and treatment of KIRC.
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Nehme, Ali, Hassan Dakik, Frédéric Picou, Meyling Cheok, Claude Preudhomme, Hervé Dombret, Juliette Lambert, et al. "Horizontal meta-analysis identifies common deregulated genes across AML subgroups providing a robust prognostic signature." Blood Advances 4, no. 20 (October 27, 2020): 5322–35. http://dx.doi.org/10.1182/bloodadvances.2020002042.
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Abstract Advances in transcriptomics have improved our understanding of leukemic development and helped to enhance the stratification of patients. The tendency of transcriptomic studies to combine AML samples, regardless of cytogenetic abnormalities, could lead to bias in differential gene expression analysis because of the differential representation of AML subgroups. Hence, we performed a horizontal meta-analysis that integrated transcriptomic data on AML from multiple studies, to enrich the less frequent cytogenetic subgroups and to uncover common genes involved in the development of AML and response to therapy. A total of 28 Affymetrix microarray data sets containing 3940 AML samples were downloaded from the Gene Expression Omnibus database. After stringent quality control, transcriptomic data on 1534 samples from 11 data sets, covering 10 AML cytogenetically defined subgroups, were retained and merged with the data on 198 healthy bone marrow samples. Differentially expressed genes between each cytogenetic subgroup and normal samples were extracted, enabling the unbiased identification of 330 commonly deregulated genes (CODEGs), which showed enriched profiles of myeloid differentiation, leukemic stem cell status, and relapse. Most of these genes were downregulated, in accordance with DNA hypermethylation. CODEGs were then used to create a prognostic score based on the weighted sum of expression of 22 core genes (CODEG22). The score was validated with microarray data of 5 independent cohorts and by quantitative real time-polymerase chain reaction in a cohort of 142 samples. CODEG22-based stratification of patients, globally and into subpopulations of cytologically healthy and elderly individuals, may complement the European LeukemiaNet classification, for a more accurate prediction of AML outcomes.
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Liao, Chen-Lu, Xing-Yu Sun, Qi Zhou, Min Tian, Yang Cao, and Hong-Bin Lyu. "Identification and validation of tumor microenvironment-related lncRNA prognostic signature for uveal melanoma." International Journal of Ophthalmology 14, no. 8 (August 18, 2021): 1151–59. http://dx.doi.org/10.18240/ijo.2021.08.03.
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AIM: To investigate the role of tumor microenvironment (TME)-related long non-coding RNA (lncRNA) in uveal melanoma (UM), probable prognostic signature and potential small molecule drugs using bioinformatics analysis. METHODS: UM expression profile data were downloaded from the Cancer Genome Atlas (TCGA) and bioinformatics methods were used to find prognostic lncRNAs related to UM immune cell infiltration. The gene expression profile data of 80 TCGA specimens were analyzed using the single sample Gene Set Enrichment Analysis (ssGSEA) method, and the immune cell infiltration of a single specimen was evaluated. Finally, the specimens were divided into high and low infiltration groups. The differential expression between the two groups was analyzed using the R package ‘edgeR’. Univariate, multivariate and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analyses were performed to explore the prognostic value of TME-related lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses were also performed. The Connectivity Map (CMap) data set was used to screen molecular drugs that may treat UM. RESULTS: A total of 2393 differentially expressed genes were identified and met the criteria for the low and high immune cell infiltration groups. Univariate Cox analysis of lncRNA genes with differential expression identified 186 genes associated with prognosis. Eight prognostic markers of TME-included lncRNA genes were established as potentially independent prognostic elements. Among 269 differentially expressed lncRNAs, 69 were up-regulated and 200 were down-regulated. Univariate Cox regression analysis of the risk indicators and clinical characteristics of the 8 lncRNA gene constructs showed that age, TNM stage, tumor base diameter, and low and high risk indices had significant prognostic value. We screened the potential small-molecule drugs for UM, including W-13, AH-6809 and Imatinib. CONCLUSION: The prognostic markers identified in this study are reliable biomarkers of UM. This study expands our current understanding of the role of TME-related lncRNAs in UM genesis, which may lay the foundations for future treatment of this disease.
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Xu, Zihao, Zilong Wu, Jingtao Zhang, Ruihao Zhou, Jiane Wu, and Bentong Yu. "Development of Multiscale Transcriptional Regulatory Network in Esophageal Cancer Based on Integrated Analysis." BioMed Research International 2020 (August 12, 2020): 1–17. http://dx.doi.org/10.1155/2020/5603958.
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Objective. To explore multiscale integrated analysis methods in identifying key regulators of esophageal cancer (ESCA). Methods. We downloaded the ESCA dataset from The Cancer Genome Atlas (TCGA) database, which contained RNA-seq data, miRNA-seq data, methylation data, and clinical phenotype information. Then, we combined ESCA-related genes from the NCBI-GENE and OMIM databases and RNA-seq dataset from TCGA to analyze differentially expressed genes (DEGs). Meanwhile, differentially expressed miRNAs (DEmiRNAs) and genes with differential methylation levels were identified. The pivot–module pairs were established using the RAID v2.0 database and TRRUST v2 database. Next, the multifactor-regulated functional network was constructed based on the above information. Additionally, gene corresponding targeted drug information was obtained from the DrugBank database. Moreover, we further screened regulators by assessing their diagnostic value and prognostic value, especially the value of distinguishing patients at TNM I stage from normal patients. In addition, the external database from the Gene Expression Omnibus (GEO) database was used for validation. Lastly, gene set enrichment analysis (GSEA) was performed to explore the potential biological functions of key regulators. Results. Our study indicated that CXCL8, CYP2C8, and E2F1 had excellent diagnostic and prognostic values, which may be potential regulators of ESCA. At the same time, the good early diagnosis ability of the three regulators also provided new insights for the diagnosis and early treatment of ESCA patients. Conclusion. We develop a multiscale integrated analysis and suggest that CXCL8, CYP2C8, and E2F1 are promising regulators with good diagnostic and prognostic values in ESCA.
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Ivanov, Nikolay A., Thomas J. Fahey, Christopher E. Mason, and Irene M. Min. "74325 Vast sex-specific differences in transcriptional landscapes of pancreatic neuroendocrine tumors." Journal of Clinical and Translational Science 5, s1 (March 2021): 103. http://dx.doi.org/10.1017/cts.2021.664.
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ABSTRACT IMPACT: Here, we describe extensive sex-specific differences in the transcriptomes of pancreatic neuroendocrine tumors (PNETs). Given that the clinical course of PNETs differs by sex (female sex is associated with better survival), achieving a greater understanding of the specific molecular sexual dimorphisms is invaluable for advancing personalized treatment. OBJECTIVES/GOALS: Epidemiologic studies demonstrate that pancreatic neuroendocrine tumors (PNETs) exhibit sexual dimorphisms with regards to prognosis, disease recurrence, and complication rates. We sought to compare the transcription and DNA methylation landscapes of PNETs by sex, to elucidate molecular differences that may underlie this sex disparity. METHODS/STUDY POPULATION: RNAseq data was generated from PNETs surgically resected at our institution (9 Female; 12 Male patients). RNA was extracted with the RNeasy Mini Kit, stranded sequencing libraries were prepared with TruSeq, and paired end sequencing was done on the HiSeq 2500/4000 systems. Transcript-level quantification was performed with salmon, and DESeq2 was used for differential expression analysis. To account for significant variation due to covariates other than sex, surrogate variables were computed with the SVA package and adjusted for. The goseq package was used for gene set over representation analysis. Matched DNA methylation (DNAm) and RNAseq data was downloaded from GEO (16 F; 16 M). Raw DNAm data was processed with minfi. Differential methylation analysis was done with limma and bumphunter. Analysis was done in R. RESULTS/ANTICIPATED RESULTS: We found that 826 autosomal genes were differentially expressed (DE) by sex in PNETs (at FDR ≤0.1). Gene set over representation analysis performed on the DE genes revealed significant enrichment for several processes, including ‘ascorbate & aldarate metabolism’ and ‘positive regulation of ERK1 & ERK2 cascade’ (all FDR ≤0.1). When we compared DNAm profiles between sexes, we found 8 CpGs which were differentially methylated by sex (at FDR ≤0.1), 7 of which were proximal to genes. Methylation of one of the sex-associated CpGs, overlapping the gene TIMM8B, was found to be negatively correlated with gene expression (rho=-0.41; p-value=0.02). Interestingly, TIMM8B deletion has been previously reported in other non-pancreatic neuroendocrine tumors. There were no differentially methylated regions between sexes. DISCUSSION/SIGNIFICANCE OF FINDINGS: Our findings demonstrate that PNETs exhibit extensive sexual dimorphisms with regards to gene expression profiles but have largely congruent methylomes by sex. These molecular differences may contribute to the variability in clinical course between men and women, and their characterization is vital for the advancement of personalized medicine.
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Mohamed, Ahmed, and Michelle M. Hill. "LipidSuite: interactive web server for lipidomics differential and enrichment analysis." Nucleic Acids Research 49, W1 (May 5, 2021): W346—W351. http://dx.doi.org/10.1093/nar/gkab327.
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Abstract Advances in mass spectrometry enabled high throughput profiling of lipids but differential analysis and biological interpretation of lipidomics datasets remains challenging. To overcome this barrier, we present LipidSuite, an end-to-end differential lipidomics data analysis server. LipidSuite offers a step-by-step workflow for preprocessing, exploration, differential analysis and enrichment analysis of untargeted and targeted lipidomics. Three lipidomics data formats are accepted for upload: mwTab file from Metabolomics Workbench, Skyline CSV Export, and a numerical matrix. Experimental variables to be used in analysis are uploaded in a separate file. Conventional lipid names are automatically parsed to enable lipid class and chain length analyses. Users can interactively explore data, choose subsets based on sample types or lipid classes or characteristics, and conduct univariate, multivariate and unsupervised analyses. For complex experimental designs and clinical cohorts, LipidSuite offers confounding variables adjustment. Finally, data tables and plots can be both interactively viewed or downloaded for publication or reports. Overall, we anticipate this free, user-friendly webserver to facilitate differential lipidomics data analysis and re-analysis, and fully harness biological interpretation from lipidomics datasets. LipidSuite is freely available at http://suite.lipidr.org.