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      Upregulation of peroxisome proliferator-activated receptor-α and the lipid metabolism pathway promotes carcinogenesis of ampullary cancer

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          Abstract

          Ampullary cancer is a rare periampullary cancer currently with no targeted therapeutic agent. It is important to develop a deeper understanding of the carcinogenesis of ampullary cancer. We attempted to explore the characteristics of ampullary cancer in our dataset and a public database, followed by a search for potential drugs. We used a bioinformatics pipeline to analyze complementary (c)DNA microarray data of ampullary cancer and surrounding normal duodenal tissues from five patients. A public database from the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) was applied for external validation. Bioinformatics tools used included the Gene Set Enrichment Analysis (GSEA), Database for Annotation, Visualization and Integrated Discovery (DAVID), MetaCore, Kyoto Encyclopedia of Genes and Genomes (KEGG), Hallmark, BioCarta, Reactome, and Connectivity Map (CMap). In total, 9097 genes were upregulated in the five ampullary cancer samples compared to normal duodenal tissues. From the MetaCore analysis, genes of peroxisome proliferator-activated receptor alpha ( PPARA) and retinoid X receptor ( RXR)-regulated lipid metabolism were overexpressed in ampullary cancer tissues. Further a GSEA of the KEGG, Hallmark, Reactome, and Gene Ontology databases revealed that PPARA and lipid metabolism-related genes were enriched in our specimens of ampullary cancer and in the NCBI GSE39409 database. Expressions of PPARA messenger (m)RNA and the PPAR-α protein were higher in clinical samples and cell lines of ampullary cancer. US Food and Drug Administration (FDA)-approved drugs, including alvespimycin, trichostatin A (a histone deacetylase inhibitor), and cytochalasin B, may have novel therapeutic effects in ampullary cancer patients as predicted by the CMap analysis. Trichostatin A was the most potent agent for ampullary cancer with a half maximal inhibitory concentration of < 0.3 μM. According to our results, upregulation of PPARA and lipid metabolism-related genes are potential pathways in the carcinogenesis and development of ampullary cancer. Results from the CMap analysis suggested potential drugs for patients with ampullary cancer.

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          Most cited references55

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

            DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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              The Molecular Signatures Database (MSigDB) hallmark gene set collection.

              The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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                Author and article information

                Journal
                Int J Med Sci
                Int J Med Sci
                ijms
                International Journal of Medical Sciences
                Ivyspring International Publisher (Sydney )
                1449-1907
                2021
                1 January 2021
                : 18
                : 1
                : 256-269
                Affiliations
                [1 ]PhD Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan.
                [2 ]Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan.
                [3 ]Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70403, Taiwan.
                [4 ]Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan.
                [5 ]Senior Citizen Service Management, Chia-Nan University of Pharmacy and Science, Tainan 71710, Taiwan.
                [6 ]NTT Institute of Hi-Technology, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam.
                [7 ]Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
                [8 ]Department of Biochemistry and Molecular Biology, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan.
                [9 ]Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 70101, Taiwan.
                [10 ]Center for Infectious Diseases and Signaling Research, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan.
                Author notes
                ✉ Corresponding author: Hui-Ping Hsu, 138 Sheng-Li Rd. Tainan 70403, Taiwan. E-mail: hphsu@ 123456mail.ncku.edu.tw ; Tel.: +886-6-2353535 ext. 5272; Fax: +886-6-2766676.

                Competing Interests: The authors have declared that no competing interest exists.

                Article
                ijmsv18p0256
                10.7150/ijms.48123
                7738964
                33390794
                b47457c7-015c-4b04-beeb-f7a67ae73c98
                © The author(s)

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.

                History
                : 13 May 2020
                : 28 October 2020
                Categories
                Research Paper

                Medicine
                ampullary cancer,lipid metabolism,carcinogenesis,ppara gene,bioinformatics
                Medicine
                ampullary cancer, lipid metabolism, carcinogenesis, ppara gene, bioinformatics

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