Dataset Information

Identification of foam cell biomarkers by microarray analysis.

ABSTRACT: Background:Lipid infiltration and inflammatory response run through the occurrence of atherosclerosis. Differentiation into macrophages and foam cell formation are the key steps of AS. Aim of this study was that the differential gene expression between foam cells and macrophages was analyzed to search the key links of foam cell generation, so as to explore the pathogenesis of atherosclerosis and provide targets for the early screening and prevention of coronary artery disease (CAD).

Methods:The gene expression profiles of GSE9874 were downloaded from Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9874) on GPL96 [HG-U133A] Affymetrix Human Genome U133. A total of 22,383 genes were analyzed for differentially expression genes (DEGs) by Bayes package. GO enrichment analysis and KEGG pathway analysis for DEGs were performed using KOBAS 3.0 software (Peking University, Beijing, China). STRING software (STRING 10.0; European Molecular Biology Laboratory, Heidelberg, Germany) was used to analyze the protein-protein interaction (PPI) of DEGs.

Results:A total of 167 DEGs between macrophages and foam cells were identified. Compared with macrophages, 102 genes were significantly upregulated and 65 genes were significantly downregulated (P??1) in foam cells. DEGs were mainly enrich in 'sterol biosynthetic and metabolic process', 'cholesterol metabolic and biosynthetic process' by GO enrichment analysis. The results of KEGG pathway analysis showed all differential genes are involved in biological processes through 143 KEGG pathways. A PPI network of the DEGs was constructed and 10 outstanding genes of the PPI network was identified by using Cytoscape, which include HMGCR, SREBF2, LDLR, HMGCS1, FDFT1, LPL, DHCR24, SQLE, ABCA1 and FDPS.

Conclusion:Lipid metabolism related genes and molecular pathways were the key to the transformation of macrophages into foam cells. Therefore, lipid metabolism disorder is the key to turn macrophages into foam cells, which plays a major role in CAD.

SUBMITTER: Song Z

PROVIDER: S-EPMC7201525 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Identification of foam cell biomarkers by microarray analysis.

Song Zikai Z Lv Shijie S Wu Haidi H Qin Ling L Cao Hongyan H Zhang Bo B Ren Shuping S

BMC cardiovascular disorders 20200506 1

<h4>Background</h4>Lipid infiltration and inflammatory response run through the occurrence of atherosclerosis. Differentiation into macrophages and foam cell formation are the key steps of AS. Aim of this study was that the differential gene expression between foam cells and macrophages was analyzed to search the key links of foam cell generation, so as to explore the pathogenesis of atherosclerosis and provide targets for the early screening and prevention of coronary artery disease (CAD).<h4>M ...[more]

PMID: 32375652

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Project description:BackgroundAtherosclerosis 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.MethodsAn 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.ResultsWe 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.ConclusionsOur 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.

Project description:Background: Non-small-cell lung cancer (NSCLC) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC. Methods: The microarray datasets of GSE18842, GSE30219, GSE31210, GSE32863 and GSE40791 from Gene Expression Omnibus database were downloaded. The differential expressed genes (DEGs) between NSCLC and normal samples were identified by limma package. The construction of protein-protein interaction (PPI) network, module analysis and enrichment analysis were performed using bioinformatics tools. The expression and prognostic values of hub genes were validated by GEPIA database and real-time quantitative PCR. Based on these DEGs, the candidate small molecules for NSCLC were identified by the CMap database. Results: A total of 408 overlapping DEGs including 109 up-regulated and 296 down-regulated genes were identified; 300 nodes and 1283 interactions were obtained from the PPI network. The most significant biological process and pathway enrichment of DEGs were response to wounding and cell adhesion molecules, respectively. Six DEGs (PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5) which significantly up-regulated in NSCLC tissues, were selected as hub genes according to the results of module analysis. The GEPIA database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. Additionally, CMap predicted the 20 most significant small molecules as potential therapeutic drugs for NSCLC. DL-thiorphan was the most promising small molecule to reverse the NSCLC gene expression. Conclusions: Based on the gene expression profiles of 696 NSCLC samples and 237 normal samples, we first revealed that PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5 could act as the promising novel diagnostic and therapeutic targets for NSCLC. Our work will contribute to clarifying the molecular mechanisms of NSCLC initiation and progression.

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Identification of foam cell biomarkers by microarray analysis.

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Identification of foam cell biomarkers by microarray analysis.

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