Simin Li1, Xiujie Chen2, Xiangqiong Liu2, Yang Yu3, Hongying Pan4, Rainer Haak1, Jana Schmidt1, Dirk Ziebolz5, Gerhard Schmalz1. 1. Department of Cariology, Endodontology and Periodontology, University Leipzig, Liebigstr. 12, 04103 Leipzig, Germany. 2. College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China. 3. Department of Periodontology, The Stomatology Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China. 4. Department of Orthopedic surgery, Brigham and Women's Hospital, Harvard Medical School, Harvard University, Boston, USA. 5. Department of Cariology, Endodontology and Periodontology, University Leipzig, Liebigstr. 12, 04103 Leipzig, Germany. Electronic address: Dirk.Ziebolz@medizin.uni-leipzig.de.
Abstract
OBJECTIVES: This study aims to reveal regulatory network of lncRNAs-miRNAs-mRNAs in oral squamous cell carcinoma (OSCC) through gene expression data. MATERIAL AND METHODS: Differentially expressed lncRNAs, miRNAs and mRNAs (cut-off: False discovery rate (FDR)<0.05 and |fold change|>1.5) were unveiled by package edgeR of R. Cox regression analysis was performed to screen prognostic factors in OSCC related with overall survival (OS) and relapse-free survival (RFS). Protein-protein interaction (PPI) network was constructed for differentially expressed mRNAs using BioGRID, HPRD and DIP. Key hub genes were identified from top 100 differentially expressed mRNAs ranked by betweenness centrality using recursive feature elimination. LncRNA-miRNA and miRNA-mRNA regulatory network were constructed and combined into ceRNAs regulatory network. Gene ontology biological terms and Kyoto Encyclopedia of Genes and Genomes pathways were identified using Fisher's exact test. RESULTS: A total of 929 differentially expressed mRNAs, 23 differentially expressed lncRNAs and 29 differentially expressed miRNAs were identified. 59 mRNAs, 6 miRNAs (hsa-mir-133a-1, hsa-mir-1-2, hsa-mir-486, hsa-mir-135b, hsa-mir-196b, hsa-mir-193b) and 6 lncRNAs (C10orf91, C2orf48, SFTA1P, FLJ41941,PART1,TTTY14) were related with OS; and 52 mRNAs, 4 miRNAs (hsa-mir-133a-1, hsa-mir-135b, hsa-mir-196b, hsa-mir-193b) and 2 lncRNAs (PART1, TTTY14) were associated with RFS. A support vector machine (SVM) classifier containing 37 key hub genes was obtained. A ceRNA regulatory network containing 417 nodes and 696 edges was constructed. ECM-receptor interaction, cytokine-cytokine receptor interaction, focal adhesion, arachidonic acid metabolism, and p53 signaling pathway were significantly enriched in the network. CONCLUSION: These findings uncover the pathogenesis of OSCC and might provide potential therapeutic targets.
OBJECTIVES: This study aims to reveal regulatory network of lncRNAs-miRNAs-mRNAs in oral squamous cell carcinoma (OSCC) through gene expression data. MATERIAL AND METHODS: Differentially expressed lncRNAs, miRNAs and mRNAs (cut-off: False discovery rate (FDR)<0.05 and |fold change|>1.5) were unveiled by package edgeR of R. Cox regression analysis was performed to screen prognostic factors in OSCC related with overall survival (OS) and relapse-free survival (RFS). Protein-protein interaction (PPI) network was constructed for differentially expressed mRNAs using BioGRID, HPRD and DIP. Key hub genes were identified from top 100 differentially expressed mRNAs ranked by betweenness centrality using recursive feature elimination. LncRNA-miRNA and miRNA-mRNA regulatory network were constructed and combined into ceRNAs regulatory network. Gene ontology biological terms and Kyoto Encyclopedia of Genes and Genomes pathways were identified using Fisher's exact test. RESULTS: A total of 929 differentially expressed mRNAs, 23 differentially expressed lncRNAs and 29 differentially expressed miRNAs were identified. 59 mRNAs, 6 miRNAs (hsa-mir-133a-1, hsa-mir-1-2, hsa-mir-486, hsa-mir-135b, hsa-mir-196b, hsa-mir-193b) and 6 lncRNAs (C10orf91, C2orf48, SFTA1P, FLJ41941,PART1,TTTY14) were related with OS; and 52 mRNAs, 4 miRNAs (hsa-mir-133a-1, hsa-mir-135b, hsa-mir-196b, hsa-mir-193b) and 2 lncRNAs (PART1, TTTY14) were associated with RFS. A support vector machine (SVM) classifier containing 37 key hub genes was obtained. A ceRNA regulatory network containing 417 nodes and 696 edges was constructed. ECM-receptor interaction, cytokine-cytokine receptor interaction, focal adhesion, arachidonic acid metabolism, and p53 signaling pathway were significantly enriched in the network. CONCLUSION: These findings uncover the pathogenesis of OSCC and might provide potential therapeutic targets.
Authors: Camile B Lopes; Leandro L Magalhães; Carolina R Teófilo; Ana Paula N N Alves; Raquel C Montenegro; Massimo Negrini; Ândrea Ribeiro-Dos-Santos Journal: BMC Cancer Date: 2018-07-06 Impact factor: 4.430