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      Identification of candidate biomarkers and mechanisms in foam cell formation from heterogeneous cellular origins via integrated transcriptome analysis

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          Abstract

          Background

          Atherosclerosis is an underlying cause of cardiovascular disease which is a leading cause of death worldwide. Foam cells play a crucial role in atherosclerotic lesion development, and macrophages and vascular smooth muscle cells (VSMCs) appear to contribute to the formation of the majority of atheromatous foam cells via oxidized low-density lipoprotein (ox-LDL) uptake.

          Methods

          An integrated, microarray-based analysis using GSE54666 and GSE68021, which contain samples of human macrophages and VSMCs incubated with ox-LDL, was conducted. The differentially expressed genes (DEGs) in each dataset were investigated via the linear models for microarray data ( limma) v. 3.40.6 software package in R v. 4.1.2 (The R Foundation for Statistical Computing). Gene ontology (GO) and pathway enrichment were performed via the ClueGO v. 2.5.8 and CluePedia v. 1.5.8 databases and the Database of Annotation, Visualization and Integrated (DAVID; https://david.ncifcrf.gov). The convergent DEGs in the two cell types were obtained, and the protein interactions and network of transcriptional factors were analyzed using the Search Tool for the Retrieval of Interacting Genes (STRING) v. 11.5 and the Transcriptional Regulatory Relationships Unraveled by Sentence-based Text-mining (TRRUST) v. 2 databases. The selected DEGs were further validated using external data from GSE9874, and a machine learning algorithm of the least absolute shrinkage and selection operator (LASSO) regression and receiver operating characteristic (ROC) analysis were applied to explore the candidate biomarkers.

          Results

          We discovered the significant DEGs and pathways that were shared or unique among the 2 cell types, coupling with enriched lipid metabolism in macrophages, and upregulated defense response in VSMCs. Moreover, we identified BTG2, ABCA1, and SLC7A11 as potential biomarkers and molecular targets for atherogenesis.

          Conclusions

          Our study provides a comprehensive summary of the landscape of the transcriptional regulations in macrophages and VSMCs under ox-LDL treatment from a bioinformatics perspective, which may contribute to a better understanding of the pathophysiological mechanisms of foam cell formation.

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          Most cited references62

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          Ferroptosis: an iron-dependent form of nonapoptotic cell death.

          Nonapoptotic forms of cell death may facilitate the selective elimination of some tumor cells or be activated in specific pathological states. The oncogenic RAS-selective lethal small molecule erastin triggers a unique iron-dependent form of nonapoptotic cell death that we term ferroptosis. Ferroptosis is dependent upon intracellular iron, but not other metals, and is morphologically, biochemically, and genetically distinct from apoptosis, necrosis, and autophagy. We identify the small molecule ferrostatin-1 as a potent inhibitor of ferroptosis in cancer cells and glutamate-induced cell death in organotypic rat brain slices, suggesting similarities between these two processes. Indeed, erastin, like glutamate, inhibits cystine uptake by the cystine/glutamate antiporter (system x(c)(-)), creating a void in the antioxidant defenses of the cell and ultimately leading to iron-dependent, oxidative death. Thus, activation of ferroptosis results in the nonapoptotic destruction of certain cancer cells, whereas inhibition of this process may protect organisms from neurodegeneration. Copyright © 2012 Elsevier Inc. All rights reserved.
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            pROC: an open-source package for R and S+ to analyze and compare ROC curves

            Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.
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              NCBI GEO: archive for functional genomics data sets—update

              The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.
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                Author and article information

                Journal
                Ann Transl Med
                Ann Transl Med
                ATM
                Annals of Translational Medicine
                AME Publishing Company
                2305-5839
                2305-5847
                24 February 2023
                15 March 2023
                : 11
                : 5
                : 189
                Affiliations
                [1 ]deptState Key Laboratory of Cardiovascular Diseases , Fuwai Hospital & National Center for Cardiovascular Diseases , Beijing, China;
                [2 ]deptChinese Academy of Medical Sciences , Peking Union Medical College , Beijing, China
                Author notes

                Contributions: (I) Conception and design: Both authors; (II) Administrative support: Y Yang; (III) Provision of study materials or patients: Both authors; (IV) Collection and assembly of data: J Xu; (V) Data analysis and interpretation: J Xu; (VI) Manuscript writing: Both authors; (VII) Final approval of manuscript: Both authors.

                Correspondence to: Yuejin Yang, MD, PhD. Department of Cardiology, Fuwai Hospital, 167 Beilishi Road, Beijing 100037, China. Email: yangyjfw@ 123456126.com .
                Article
                atm-11-05-189
                10.21037/atm-22-3761
                10061454
                37007574
                aba61c29-3553-475d-9d16-bef48bb90edd
                2023 Annals of Translational Medicine. All rights reserved.

                Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0.

                History
                : 27 July 2022
                : 05 December 2022
                Categories
                Original Article

                atherosclerosis,foam cell,macrophage,vascular smooth muscle cells,transcriptome

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