{"title":"结合生物信息学和机器学习方法揭示了与预后相关的ceRNA网络，并提出ABCA8、CAT和CXCL12是骨肉瘤的独立保护因子。","authors":"Jiaqi Fan, Jianhong Liao, Yuwen Huang","doi":"10.17219/acem/172663","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Aberrant circular RNA (circRNA) acts as an oncogene or suppressor during neoplasm initiation and development. However, the functions of most circRNAs in osteosarcoma (OS) remain unclear.</p><p><strong>Objectives: </strong>We aimed to investigate the expression, molecular functions and mechanisms underlying circRNAs in OS.</p><p><strong>Material and methods: </strong>Network interaction, pathway enrichment and regression analyses were performed to determine differentially expressed (DE) circRNAs, microRNAs (miRNAs) and messenger RNAs (mRNAs). We constructed competitive endogenous RcodeNA (ceRNA) networks and integrated patient clinical data to analyze the relationship between the networks and prognosis. The circRNA, miRNA and mRNA data were retrieved from Gene Expression Omnibus (GEO) microarray datasets. A circRNA-miRNA-mRNA interaction network was established and visualized using miRNet. Protein interactions were investigated using STRING and Cytoscape, and hub genes were identified using the MCODE plug-in. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway analyses were performed to determine the DEmRNAs. LIMMA and RobustRankAggreg were used to screen for DERNAs. Node genes in the interaction network were analyzed using least absolute shrinkage and selection operator (LASSO) and Cox regression to obtain OS-related ceRNA networks.</p><p><strong>Results: </strong>We identified 9 DEcircRNAs, 243 DEmiRNAs and 211 DEmRNAs. We found that a ceRNA subnetwork, based on 1 circRNA, 1 miRNA and 8 mRNAs, was closely associated with OS prognosis. Integrating the proportional hazards model and survival analysis revealed 3 independent protective factors: adenosine triphosphate (ATP)-binding cassette sub-family A member 8 (ABCA8), catalase (CAT) and C-X-C motif chemokine ligand 12 (CXCL12).</p><p><strong>Conclusions: </strong>Our study provides novel insights into circRNA-related ceRNA networks and identifies potential prognostic biomarkers of OS.</p>","PeriodicalId":7306,"journal":{"name":"Advances in Clinical and Experimental Medicine","volume":" ","pages":"857-868"},"PeriodicalIF":2.1000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Combined bioinformatics and machine learning methodologies reveal prognosis-related ceRNA network and propose ABCA8, CAT, and CXCL12 as independent protective factors against osteosarcoma.\",\"authors\":\"Jiaqi Fan, Jianhong Liao, Yuwen Huang\",\"doi\":\"10.17219/acem/172663\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Aberrant circular RNA (circRNA) acts as an oncogene or suppressor during neoplasm initiation and development. However, the functions of most circRNAs in osteosarcoma (OS) remain unclear.</p><p><strong>Objectives: </strong>We aimed to investigate the expression, molecular functions and mechanisms underlying circRNAs in OS.</p><p><strong>Material and methods: </strong>Network interaction, pathway enrichment and regression analyses were performed to determine differentially expressed (DE) circRNAs, microRNAs (miRNAs) and messenger RNAs (mRNAs). We constructed competitive endogenous RcodeNA (ceRNA) networks and integrated patient clinical data to analyze the relationship between the networks and prognosis. The circRNA, miRNA and mRNA data were retrieved from Gene Expression Omnibus (GEO) microarray datasets. A circRNA-miRNA-mRNA interaction network was established and visualized using miRNet. Protein interactions were investigated using STRING and Cytoscape, and hub genes were identified using the MCODE plug-in. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway analyses were performed to determine the DEmRNAs. LIMMA and RobustRankAggreg were used to screen for DERNAs. Node genes in the interaction network were analyzed using least absolute shrinkage and selection operator (LASSO) and Cox regression to obtain OS-related ceRNA networks.</p><p><strong>Results: </strong>We identified 9 DEcircRNAs, 243 DEmiRNAs and 211 DEmRNAs. We found that a ceRNA subnetwork, based on 1 circRNA, 1 miRNA and 8 mRNAs, was closely associated with OS prognosis. Integrating the proportional hazards model and survival analysis revealed 3 independent protective factors: adenosine triphosphate (ATP)-binding cassette sub-family A member 8 (ABCA8), catalase (CAT) and C-X-C motif chemokine ligand 12 (CXCL12).</p><p><strong>Conclusions: </strong>Our study provides novel insights into circRNA-related ceRNA networks and identifies potential prognostic biomarkers of OS.</p>\",\"PeriodicalId\":7306,\"journal\":{\"name\":\"Advances in Clinical and Experimental Medicine\",\"volume\":\" \",\"pages\":\"857-868\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Clinical and Experimental Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.17219/acem/172663\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Clinical and Experimental Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.17219/acem/172663","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
Combined bioinformatics and machine learning methodologies reveal prognosis-related ceRNA network and propose ABCA8, CAT, and CXCL12 as independent protective factors against osteosarcoma.
Background: Aberrant circular RNA (circRNA) acts as an oncogene or suppressor during neoplasm initiation and development. However, the functions of most circRNAs in osteosarcoma (OS) remain unclear.
Objectives: We aimed to investigate the expression, molecular functions and mechanisms underlying circRNAs in OS.
Material and methods: Network interaction, pathway enrichment and regression analyses were performed to determine differentially expressed (DE) circRNAs, microRNAs (miRNAs) and messenger RNAs (mRNAs). We constructed competitive endogenous RcodeNA (ceRNA) networks and integrated patient clinical data to analyze the relationship between the networks and prognosis. The circRNA, miRNA and mRNA data were retrieved from Gene Expression Omnibus (GEO) microarray datasets. A circRNA-miRNA-mRNA interaction network was established and visualized using miRNet. Protein interactions were investigated using STRING and Cytoscape, and hub genes were identified using the MCODE plug-in. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway analyses were performed to determine the DEmRNAs. LIMMA and RobustRankAggreg were used to screen for DERNAs. Node genes in the interaction network were analyzed using least absolute shrinkage and selection operator (LASSO) and Cox regression to obtain OS-related ceRNA networks.
Results: We identified 9 DEcircRNAs, 243 DEmiRNAs and 211 DEmRNAs. We found that a ceRNA subnetwork, based on 1 circRNA, 1 miRNA and 8 mRNAs, was closely associated with OS prognosis. Integrating the proportional hazards model and survival analysis revealed 3 independent protective factors: adenosine triphosphate (ATP)-binding cassette sub-family A member 8 (ABCA8), catalase (CAT) and C-X-C motif chemokine ligand 12 (CXCL12).
Conclusions: Our study provides novel insights into circRNA-related ceRNA networks and identifies potential prognostic biomarkers of OS.
期刊介绍：
Advances in Clinical and Experimental Medicine has been published by the Wroclaw Medical University since 1992. Establishing the medical journal was the idea of Prof. Bogumił Halawa, Chair of the Department of Cardiology, and was fully supported by the Rector of Wroclaw Medical University, Prof. Zbigniew Knapik. Prof. Halawa was also the first editor-in-chief, between 1992-1997. The journal, then entitled "Postępy Medycyny Klinicznej i Doświadczalnej", appeared quarterly.
Prof. Leszek Paradowski was editor-in-chief from 1997-1999. In 1998 he initiated alterations in the profile and cover design of the journal which were accepted by the Editorial Board. The title was changed to Advances in Clinical and Experimental Medicine. Articles in English were welcomed. A number of outstanding representatives of medical science from Poland and abroad were invited to participate in the newly established International Editorial Staff.
Prof. Antonina Harłozińska-Szmyrka was editor-in-chief in years 2000-2005, in years 2006-2007 once again prof. Leszek Paradowski and prof. Maria Podolak-Dawidziak was editor-in-chief in years 2008-2016. Since 2017 the editor-in chief is prof. Maciej Bagłaj.
Since July 2005, original papers have been published only in English. Case reports are no longer accepted. The manuscripts are reviewed by two independent reviewers and a statistical reviewer, and English texts are proofread by a native speaker.
The journal has been indexed in several databases: Scopus, Ulrich’sTM International Periodicals Directory, Index Copernicus and since 2007 in Thomson Reuters databases: Science Citation Index Expanded i Journal Citation Reports/Science Edition.
In 2010 the journal obtained Impact Factor which is now 1.179 pts. Articles published in the journal are worth 15 points among Polish journals according to the Polish Committee for Scientific Research and 169.43 points according to the Index Copernicus.
Since November 7, 2012, Advances in Clinical and Experimental Medicine has been indexed and included in National Library of Medicine’s MEDLINE database. English abstracts printed in the journal are included and searchable using PubMed http://www.ncbi.nlm.nih.gov/pubmed.
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