Bioinformatics Identification of Ferroptosis-Associated Biomarkers and Therapeutic Compounds in Psoriasis
Purpose. Psoriasis is closely linked to ferroptosis. This study aimed to identify potential ferroptosis-associated genes in psoriasis using bioinformatics. Methods. Data from the GSE30999 dataset was downloaded from the Gene Expression Omnibus (GEO), and the ferroptosis-associated genes were retriev...
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| Veröffentlicht in: | Journal of oncology 2022-10, Vol.2022, p.1-15 |
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| description | Purpose. Psoriasis is closely linked to ferroptosis. This study aimed to identify potential ferroptosis-associated genes in psoriasis using bioinformatics. Methods. Data from the GSE30999 dataset was downloaded from the Gene Expression Omnibus (GEO), and the ferroptosis-associated genes were retrieved from FerrDb. The differentially expressed ferroptosis-associated genes were identified using Venn diagrams. Subsequently, a network of protein-protein interactions (PPIs) between psoriasis targets and ferroptosis-associated genes was constructed based on the STRING database and analyzed by Cytoscape software. The Metascape portal conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Moreover, the expression of ferroptosis-related genes was verified in the GSE13355 dataset. Finally, the verified genes were used to predict the therapeutic drugs for psoriasis using the DGIdb/CMap database. SwissDock was used to examine ligand docking, and UCSF Chimera displayed the results visually. Results. Among 85 pairs of psoriasis lesion (LS) and no-lesion (NL) samples from patients, 19 ferroptosis-associated genes were found to be differentially expressed (3 upregulated genes and 16 downregulated genes). Based on the PPI results, these ferroptosis-associated genes interact with each other. The GO and KEGG enrichment analysis of differentially expressed ferroptosis-related genes indicated several enriched terms related to the oxidative stress response. The GSE13355 dataset verified the results of the bioinformatics analysis obtained from the GSE30999 dataset regarding SLC7A5, SLC7A11, and CHAC1. Psoriasis-related compounds corresponding to SLC7A5 and SLC7A11 were also identified, including Melphalan, Quisqualate, Riluzole, and Sulfasalazine. Conclusion. We identified 3 differentially expressed ferroptosis-related genes through bioinformatics analysis. SLC7A5, SLC7A11, and CHAC1 may affect the development of psoriasis by regulating ferroptosis. These results open new avenues in understanding the treatment of psoriasis. |
| doi_str_mv | 10.1155/2022/3818216 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9581596</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A723798794</galeid><sourcerecordid>A723798794</sourcerecordid><originalsourceid>FETCH-LOGICAL-c453t-572a4ef0c9a2b74306ff585a337c84731c5be4503da56a785ae3ec6ef8bd196f3</originalsourceid><addsrcrecordid>eNp9kU9v1DAQxSNEJUrLjQ9giQsShPq_nQvSdkWhUqVyKGfL64y7Lokd7ATEt8fRrlDh0JNtzW-e581rmtcEfyBEiAuKKb1gmmhK5LPmlEitWs0Ffv7o_qJ5WcoDxpLjTp424TKkEH3Ko52DK-i6hzgHH1x9poiSR1eQc5rmVEJpN6UkF-wMPap9o83fIRdkY4_u9pDtBEsVQds0TmmJfUEhoq8l5WBr83lz4u1Q4NXxPGu-XX26235pb24_X283N63jgs2tUNRy8Nh1lu4UZ1h6L7SwjCmnuWLEiR1UH6y3QlpVK8DASfB615NOenbWfDzoTstuhN5VP9kOZsqhzvvbJBvMv5UY9uY-_TSd0ER0sgq8PQrk9GOBMpsxFAfDYCOkpRiqqCac1oVW9M1_6ENacqz2VkrxjgjyiLq3A5h12_Vft4qajaJMdVp1vFLvD5TLqZQM_u_IBJs1XrPGa47xVvzdAd-H2Ntf4Wn6D7dkpi4</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2727491517</pqid></control><display><type>article</type><title>Bioinformatics Identification of Ferroptosis-Associated Biomarkers and Therapeutic Compounds in Psoriasis</title><source>Wiley Online Library Open Access</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Alma/SFX Local Collection</source><source>PubMed Central Open Access</source><creator>Mao, Jingyi ; Ma, Xin</creator><contributor>Zhang, Zhiqian ; Zhiqian Zhang</contributor><creatorcontrib>Mao, Jingyi ; Ma, Xin ; Zhang, Zhiqian ; Zhiqian Zhang</creatorcontrib><description>Purpose. Psoriasis is closely linked to ferroptosis. This study aimed to identify potential ferroptosis-associated genes in psoriasis using bioinformatics. Methods. Data from the GSE30999 dataset was downloaded from the Gene Expression Omnibus (GEO), and the ferroptosis-associated genes were retrieved from FerrDb. The differentially expressed ferroptosis-associated genes were identified using Venn diagrams. Subsequently, a network of protein-protein interactions (PPIs) between psoriasis targets and ferroptosis-associated genes was constructed based on the STRING database and analyzed by Cytoscape software. The Metascape portal conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Moreover, the expression of ferroptosis-related genes was verified in the GSE13355 dataset. Finally, the verified genes were used to predict the therapeutic drugs for psoriasis using the DGIdb/CMap database. SwissDock was used to examine ligand docking, and UCSF Chimera displayed the results visually. Results. Among 85 pairs of psoriasis lesion (LS) and no-lesion (NL) samples from patients, 19 ferroptosis-associated genes were found to be differentially expressed (3 upregulated genes and 16 downregulated genes). Based on the PPI results, these ferroptosis-associated genes interact with each other. The GO and KEGG enrichment analysis of differentially expressed ferroptosis-related genes indicated several enriched terms related to the oxidative stress response. The GSE13355 dataset verified the results of the bioinformatics analysis obtained from the GSE30999 dataset regarding SLC7A5, SLC7A11, and CHAC1. Psoriasis-related compounds corresponding to SLC7A5 and SLC7A11 were also identified, including Melphalan, Quisqualate, Riluzole, and Sulfasalazine. Conclusion. We identified 3 differentially expressed ferroptosis-related genes through bioinformatics analysis. SLC7A5, SLC7A11, and CHAC1 may affect the development of psoriasis by regulating ferroptosis. These results open new avenues in understanding the treatment of psoriasis.</description><identifier>ISSN: 1687-8450</identifier><identifier>EISSN: 1687-8450</identifier><identifier>DOI: 10.1155/2022/3818216</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Antibiotics ; Apoptosis ; Arthritis ; Bioinformatics ; Computational biology ; Correlation analysis ; Datasets ; Dermatologic agents ; Dermatology ; Disease ; Drug resistance ; Ferroptosis ; Formulae, receipts, prescriptions ; Gene expression ; Genes ; Genomes ; Health aspects ; Ligands ; Lipid peroxidation ; Protein-protein interactions ; Proteins ; Psoriasis ; Skin ; Software packages</subject><ispartof>Journal of oncology, 2022-10, Vol.2022, p.1-15</ispartof><rights>Copyright © 2022 Jingyi Mao and Xin Ma.</rights><rights>COPYRIGHT 2022 John Wiley & Sons, Inc.</rights><rights>Copyright © 2022 Jingyi Mao and Xin Ma. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Copyright © 2022 Jingyi Mao and Xin Ma. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c453t-572a4ef0c9a2b74306ff585a337c84731c5be4503da56a785ae3ec6ef8bd196f3</citedby><cites>FETCH-LOGICAL-c453t-572a4ef0c9a2b74306ff585a337c84731c5be4503da56a785ae3ec6ef8bd196f3</cites><orcidid>0000-0002-3020-881X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581596/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581596/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids></links><search><contributor>Zhang, Zhiqian</contributor><contributor>Zhiqian Zhang</contributor><creatorcontrib>Mao, Jingyi</creatorcontrib><creatorcontrib>Ma, Xin</creatorcontrib><title>Bioinformatics Identification of Ferroptosis-Associated Biomarkers and Therapeutic Compounds in Psoriasis</title><title>Journal of oncology</title><description>Purpose. Psoriasis is closely linked to ferroptosis. This study aimed to identify potential ferroptosis-associated genes in psoriasis using bioinformatics. Methods. Data from the GSE30999 dataset was downloaded from the Gene Expression Omnibus (GEO), and the ferroptosis-associated genes were retrieved from FerrDb. The differentially expressed ferroptosis-associated genes were identified using Venn diagrams. Subsequently, a network of protein-protein interactions (PPIs) between psoriasis targets and ferroptosis-associated genes was constructed based on the STRING database and analyzed by Cytoscape software. The Metascape portal conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Moreover, the expression of ferroptosis-related genes was verified in the GSE13355 dataset. Finally, the verified genes were used to predict the therapeutic drugs for psoriasis using the DGIdb/CMap database. SwissDock was used to examine ligand docking, and UCSF Chimera displayed the results visually. Results. Among 85 pairs of psoriasis lesion (LS) and no-lesion (NL) samples from patients, 19 ferroptosis-associated genes were found to be differentially expressed (3 upregulated genes and 16 downregulated genes). Based on the PPI results, these ferroptosis-associated genes interact with each other. The GO and KEGG enrichment analysis of differentially expressed ferroptosis-related genes indicated several enriched terms related to the oxidative stress response. The GSE13355 dataset verified the results of the bioinformatics analysis obtained from the GSE30999 dataset regarding SLC7A5, SLC7A11, and CHAC1. Psoriasis-related compounds corresponding to SLC7A5 and SLC7A11 were also identified, including Melphalan, Quisqualate, Riluzole, and Sulfasalazine. Conclusion. We identified 3 differentially expressed ferroptosis-related genes through bioinformatics analysis. SLC7A5, SLC7A11, and CHAC1 may affect the development of psoriasis by regulating ferroptosis. These results open new avenues in understanding the treatment of psoriasis.</description><subject>Antibiotics</subject><subject>Apoptosis</subject><subject>Arthritis</subject><subject>Bioinformatics</subject><subject>Computational biology</subject><subject>Correlation analysis</subject><subject>Datasets</subject><subject>Dermatologic agents</subject><subject>Dermatology</subject><subject>Disease</subject><subject>Drug resistance</subject><subject>Ferroptosis</subject><subject>Formulae, receipts, prescriptions</subject><subject>Gene expression</subject><subject>Genes</subject><subject>Genomes</subject><subject>Health aspects</subject><subject>Ligands</subject><subject>Lipid peroxidation</subject><subject>Protein-protein interactions</subject><subject>Proteins</subject><subject>Psoriasis</subject><subject>Skin</subject><subject>Software packages</subject><issn>1687-8450</issn><issn>1687-8450</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kU9v1DAQxSNEJUrLjQ9giQsShPq_nQvSdkWhUqVyKGfL64y7Lokd7ATEt8fRrlDh0JNtzW-e581rmtcEfyBEiAuKKb1gmmhK5LPmlEitWs0Ffv7o_qJ5WcoDxpLjTp424TKkEH3Ko52DK-i6hzgHH1x9poiSR1eQc5rmVEJpN6UkF-wMPap9o83fIRdkY4_u9pDtBEsVQds0TmmJfUEhoq8l5WBr83lz4u1Q4NXxPGu-XX26235pb24_X283N63jgs2tUNRy8Nh1lu4UZ1h6L7SwjCmnuWLEiR1UH6y3QlpVK8DASfB615NOenbWfDzoTstuhN5VP9kOZsqhzvvbJBvMv5UY9uY-_TSd0ER0sgq8PQrk9GOBMpsxFAfDYCOkpRiqqCac1oVW9M1_6ENacqz2VkrxjgjyiLq3A5h12_Vft4qajaJMdVp1vFLvD5TLqZQM_u_IBJs1XrPGa47xVvzdAd-H2Ntf4Wn6D7dkpi4</recordid><startdate>20221012</startdate><enddate>20221012</enddate><creator>Mao, Jingyi</creator><creator>Ma, Xin</creator><general>Hindawi</general><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>NAPCQ</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3020-881X</orcidid></search><sort><creationdate>20221012</creationdate><title>Bioinformatics Identification of Ferroptosis-Associated Biomarkers and Therapeutic Compounds in Psoriasis</title><author>Mao, Jingyi ; Ma, Xin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c453t-572a4ef0c9a2b74306ff585a337c84731c5be4503da56a785ae3ec6ef8bd196f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Antibiotics</topic><topic>Apoptosis</topic><topic>Arthritis</topic><topic>Bioinformatics</topic><topic>Computational biology</topic><topic>Correlation analysis</topic><topic>Datasets</topic><topic>Dermatologic agents</topic><topic>Dermatology</topic><topic>Disease</topic><topic>Drug resistance</topic><topic>Ferroptosis</topic><topic>Formulae, receipts, prescriptions</topic><topic>Gene expression</topic><topic>Genes</topic><topic>Genomes</topic><topic>Health aspects</topic><topic>Ligands</topic><topic>Lipid peroxidation</topic><topic>Protein-protein interactions</topic><topic>Proteins</topic><topic>Psoriasis</topic><topic>Skin</topic><topic>Software packages</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mao, Jingyi</creatorcontrib><creatorcontrib>Ma, Xin</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Nursing & Allied Health Premium</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mao, Jingyi</au><au>Ma, Xin</au><au>Zhang, Zhiqian</au><au>Zhiqian Zhang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bioinformatics Identification of Ferroptosis-Associated Biomarkers and Therapeutic Compounds in Psoriasis</atitle><jtitle>Journal of oncology</jtitle><date>2022-10-12</date><risdate>2022</risdate><volume>2022</volume><spage>1</spage><epage>15</epage><pages>1-15</pages><issn>1687-8450</issn><eissn>1687-8450</eissn><abstract>Purpose. Psoriasis is closely linked to ferroptosis. This study aimed to identify potential ferroptosis-associated genes in psoriasis using bioinformatics. Methods. Data from the GSE30999 dataset was downloaded from the Gene Expression Omnibus (GEO), and the ferroptosis-associated genes were retrieved from FerrDb. The differentially expressed ferroptosis-associated genes were identified using Venn diagrams. Subsequently, a network of protein-protein interactions (PPIs) between psoriasis targets and ferroptosis-associated genes was constructed based on the STRING database and analyzed by Cytoscape software. The Metascape portal conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Moreover, the expression of ferroptosis-related genes was verified in the GSE13355 dataset. Finally, the verified genes were used to predict the therapeutic drugs for psoriasis using the DGIdb/CMap database. SwissDock was used to examine ligand docking, and UCSF Chimera displayed the results visually. Results. Among 85 pairs of psoriasis lesion (LS) and no-lesion (NL) samples from patients, 19 ferroptosis-associated genes were found to be differentially expressed (3 upregulated genes and 16 downregulated genes). Based on the PPI results, these ferroptosis-associated genes interact with each other. The GO and KEGG enrichment analysis of differentially expressed ferroptosis-related genes indicated several enriched terms related to the oxidative stress response. The GSE13355 dataset verified the results of the bioinformatics analysis obtained from the GSE30999 dataset regarding SLC7A5, SLC7A11, and CHAC1. Psoriasis-related compounds corresponding to SLC7A5 and SLC7A11 were also identified, including Melphalan, Quisqualate, Riluzole, and Sulfasalazine. Conclusion. We identified 3 differentially expressed ferroptosis-related genes through bioinformatics analysis. SLC7A5, SLC7A11, and CHAC1 may affect the development of psoriasis by regulating ferroptosis. These results open new avenues in understanding the treatment of psoriasis.</abstract><cop>New York</cop><pub>Hindawi</pub><doi>10.1155/2022/3818216</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-3020-881X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Antibiotics Apoptosis Arthritis Bioinformatics Computational biology Correlation analysis Datasets Dermatologic agents Dermatology Disease Drug resistance Ferroptosis Formulae, receipts, prescriptions Gene expression Genes Genomes Health aspects Ligands Lipid peroxidation Protein-protein interactions Proteins Psoriasis Skin Software packages |
| title | Bioinformatics Identification of Ferroptosis-Associated Biomarkers and Therapeutic Compounds in Psoriasis |
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