{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T11:25:21Z","timestamp":1788780321927,"version":"build-2803163510"},"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T00:00:00Z","timestamp":1712793600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Objective<\/jats:title>\n<jats:p>To construct a competitive endogenous RNA (ceRNA) regulatory network in blood exosomes of patients with ovarian cancer (OC) using bioinformatics and explore its pathogenesis.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>The exoRbase2.0 database was used to download blood exosome gene sequencing data from patients OC and normal controls and the expression profiles of exosomal mRNA, long non-coding RNA (lncRNA), and circular RNA (circRNA) were detected independently using R language for differential expression analysis. TargetScan and miRanda databases were combined for the prediction and differential expression of mRNA-binding microRNAs (miRNA). The miRcode and starBase databases were used to predict miRNAs that bind to differentially expressed lncRNAs and circRNAs repectively. The relevant mRNA, circRNA, lncRNA and their corresponding miRNA prediction data were imported into Cytoscape software for visualization of the ceRNA network. The R language and KEGG Orthology-based Annotation System (KOBAS) were used to execute and illustrate the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Hub genes were identified using The CytoHubba plugin.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>Thirty-one differentially expressed mRNAs, 17 differentially expressed lncRNAs, and 24 differentially expressed circRNAs were screened. Cytoscape software was used to construct the ceRNA network with nine mRNA nodes, two lncRNA nodes, eight circRNA nodes, and 51 miRNA nodes. Both GO and KEGG were focused on the Spliceosome pathway, indicating that spliceosomes are closely linked with the development of OC, while <jats:italic>heterogenous nuclear ribonucleoprotein K<\/jats:italic> and <jats:italic>RNA binding motif protein X-linked<\/jats:italic> genes were the top 10 score Hub genes screened by Cytoscape software, including two lncRNAs, four mRNAs, and four circRNAs. In patients with OC, the expression of <jats:italic>eukaryotic translation initiation factor 4 gamma 2<\/jats:italic> (EIF4G2), <jats:italic>SERPINE 1 mRNA binding protein 1<\/jats:italic> (SERBP1), <jats:italic>ribosomal protein L15<\/jats:italic> (RPL15) and <jats:italic>human leukocyte antigen complex P5<\/jats:italic> (HCP5) was significantly higher whereas that of <jats:italic>testis expressed transcript<\/jats:italic>, <jats:italic>Y-linked 15<\/jats:italic> and <jats:italic>DEAD-box helicase 3 Y-linked<\/jats:italic> genes was lower compared to normal controls Immunocorrelation scores revealed that SERBP1 was significantly and negatively correlated with endothelial cells and CD4+ T cells and positively correlated with natural killer (NK) cells and macrophages, respectively; RPL15 was significantly positively correlated with macrophages and endothelial cells and negatively correlated with CD8+ T cells and uncharacterized cells, respectively. <jats:italic>EIF4G2<\/jats:italic> was significantly and negatively correlated with endothelial cells and CD4+ T cells, and positively correlated with uncharacterized cells, respectively. Based on the survival data and the significant correlation characteristics derived from the multifactorial Cox analysis (<jats:italic>P &lt; 0<\/jats:italic>.<jats:italic>05<\/jats:italic>), the survival prediction curves demonstrated that the prognostic factors associated with 3-year survival in patients with OC were The prognostic factors associated with survival were Macrophage, RPL15.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>This study successfully constructs a ceRNA regulatory network in blood exosomes of OV patients, which provides the specific targets for diagnosis and treatment of OC.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0291149","type":"journal-article","created":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T20:18:15Z","timestamp":1712866695000},"page":"e0291149","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["Construction and analysis of competitive endogenous RNA networks and prognostic models associated with ovarian cancer based on the exoRBase database"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6851-4241","authenticated-orcid":true,"given":"Zanhao","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chongyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianing","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2769-1422","authenticated-orcid":true,"given":"Cong","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,4,11]]},"reference":[{"issue":"6","key":"pone.0291149.ref001","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.3390\/cells9061402","article-title":"Targeting Cancer Stem Cells to Overcome Therapy Resistance in Ovarian Cancer.","volume":"9","author":"S Mu\u00f1oz-Galv\u00e1n","year":"2020","journal-title":"Cells"},{"issue":"6","key":"pone.0291149.ref002","first-page":"561","article-title":"Ovarian Cancer: An Overview.","volume":"91","author":"SM Penny","year":"2020","journal-title":"Radiol Technol"},{"issue":"6","key":"pone.0291149.ref003","first-page":"394","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [published correction appears in CA Cancer J Clin.","volume":"68","author":"F Bray","year":"2018","journal-title":"2020 Jul;70(4):313]. 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