Dataset Information

Pivotal regulatory network and genes in osteosarcoma.

ABSTRACT: INTRODUCTION:Understanding the transcriptional regulatory networks that map out the coordinated responses of transcription factors and target genes would represent a significant advance in the analysis of osteosarcoma, a common primary bone malignancy. The objective of our study was to interpret the mechanisms of osteosarcoma through the regulation network construction. MATERIAL AND METHODS:Using GSE14359 datasets downloaded from Gene Expression Omnibus data, we first screened the differentially expressed genes in osteosarcoma. We explored the regulation relationship between transcription factors and target genes using Cytoscape. The underlying molecular mechanisms of these crucial target genes were investigated by Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. RESULTS:A total of 1836 differentially expressed were identified and 98 regulatory relationships were constructed between 32 transcription factors and their 60 differentially expressed target genes. Furthermore, BCL2-like 1 (BCL2L1), tumor protein p53 (TP53), v-rel reticuloendotheliosis viral oncogene homolog A (avian) (RELA), interleukin 6 (IL6), retinoic acid receptor, alpha (RARA), nuclear factor I/C (CCAAT-binding transcription factor) (NFIC), and CCAAT/enhancer binding protein, beta (CEBPB) formed a small pivotal network, in which IL-6 could be regulated by TP53, NFIC, RARA, and CEBPB, but BCL2L1 may be only regulated by TP53 and RELA. These genes had been demonstrated to be involved in osteosarcoma progression via various biological processes and pathways, including regulation of cell apoptosis, proliferation, antigen processing and presentation pathway, and phosphatidylinositol signaling system. CONCLUSIONS:In general, we have obtained a regulatory network and several pathways that may play important roles in osteosarcoma, identified several pivotal genes in osteosarcoma, and predicted several potential key genes for osteosarcoma.

SUBMITTER: Luo Y

PROVIDER: S-EPMC3701964 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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Pivotal regulatory network and genes in osteosarcoma.

Luo Yi Y Deng Zhansheng Z Chen Jing J

Archives of medical science : AMS 20121008 3

<h4>Introduction</h4>Understanding the transcriptional regulatory networks that map out the coordinated responses of transcription factors and target genes would represent a significant advance in the analysis of osteosarcoma, a common primary bone malignancy. The objective of our study was to interpret the mechanisms of osteosarcoma through the regulation network construction.<h4>Material and methods</h4>Using GSE14359 datasets downloaded from Gene Expression Omnibus data, we first screened the ...[more]

PMID: 23847684

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Project description:Research on brain disorders with a strong genetic component and complex heritability, such as schizophrenia, has led to the development of brain transcriptomics. This field seeks to gain a deeper understanding of gene expression, a key factor in exploring further research issues. Our study focused on how genes are associated amongst each other. In this perspective, we have developed a novel data-driven strategy for characterizing genetic modules, i.e., clusters of strongly interacting genes. The aim was to uncover a pivotal community of genes linked to a target gene for schizophrenia. Our approach combined network topological properties with information theory to highlight the presence of a pivotal community, for a specific gene, and to simultaneously assess the information content of partitions with the Shannon's entropy based on betweenness. We analyzed the publicly available BrainCloud dataset containing post-mortem gene expression data and focused on the Dopamine D2 receptor, encoded by the DRD2 gene. We used four different community detection algorithms to evaluate the consistence of our approach. A pivotal DRD2 community emerged for all the procedures applied, with a considerable reduction in size, compared to the initial network. The stability of the results was confirmed by a Dice index ?80% within a range of tested parameters. The detected community was also the most informative, as it represented an optimization of the Shannon entropy. Lastly, we verified the strength of connection of the DRD2 community, which was stronger than any other randomly selected community and even more so than the Weighted Gene Co-expression Network Analysis module, commonly considered the standard approach for such studies. This finding substantiates the conclusion that the detected community represents a more connected and informative cluster of genes for the DRD2 community, and therefore better elucidates the behavior of this module of strongly related DRD2 genes. Because this gene plays a relevant role in Schizophrenia, this finding of a more specific DRD2 community will improve the understanding of the genetic factors related with this disorder.

Project description:The aim of the present study was to identify the important mRNAs, micro (mi)RNAs and long non‑coding (lnc)RNAs that are associated with osteosarcoma recurrence. The GSE3905 dataset, which contains two sub‑datasets (GSE39040 and GSE39055), was downloaded from the Gene Expression Omnibus (GEO). Prognosis‑associated RNAs were identified by performing Cox regression univariate analysis and were subsequently used to construct a competing endogenous (ce)RNA regulatory network for Gene Set Enrichment Analysis (GSEA). Kaplan‑Meier survival analysis was used to determine the associations between expression levels and survival prognosis. In addition, another independent miRNA profile, GSE79181, was downloaded from GEO for validation. Among the differentially expressed RNAs, 417 RNAs (5 lncRNAs, 19 miRNAs, and 393 mRNAs) were observed to be associated with prognosis. The GSEA for the ceRNA regulatory network revealed that 'Mitogen‑activated protein kinase (MAPK) signaling pathway', 'Chemokine signaling pathway' and 'Spliceosome' were markedly associated with osteosarcoma. In addition, three lncRNAs [long intergenic non‑protein coding RNA 28 (LINC00028), LINC00323, and small nucleolar RNA host gene 1 (SNHG1)] and two miRNAs (hsa‑miR‑124 and hsa‑miR‑7) regulating three mRNAs [Ras‑related protein Rap‑1b (RAP1B), activating transcription factor 2 (ATF2) and protein phosphatase Mg2+/Mn2+ dependent 1B (PPM1B)] participated in the MAPK signaling pathway. The Kaplan‑Meier survival analysis also demonstrated that samples with lower expression levels of LINC00323 and SNHG1 had better prognosis, and samples with increased expression levels of LINC00028, hsa‑miR‑124 and hsa‑miR‑7 had better prognosis. Overexpression of RAP1B, ATF2 and PPM1B was positively associated with osteosarcoma recurrence. The roles of hsa‑miR‑124 and hsa‑miR‑7 in osteosarcoma recurrence were also validated using GSE79181. Thus, in conclusions, the three lncRNAs (LINC00028, LINC00323 and SNHG1), two miRNAs (hsa‑miR‑124 and hsa‑miR‑7) and three mRNAs (RAP1B, ATF2, and PPM1B) were associated with osteosarcoma recurrence.

Project description:BackgroundOsteosarcoma is a common malignant primary bone tumor in adolescents and children. Numerous studies have shown that circRNAs were involved in the proliferation and invasion of various tumors. However, the role of circRNAs in osteosarcoma remains unclear. Here, we aimed to explore the regulatory network among circRNA-miRNA-mRNA in osteosarcoma.MethodsThe circRNA (GSE140256), microRNA (GSE28423), and mRNA (GSE99671) expression profiles of osteosarcoma were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs, miRNAs and mRNAs were identified. CircRNA-miRNA interactions and miRNA-mRNA interactions were determined by Circular RNA Interactome (CircInteractome) database and microRNA Data Integration Portal (mirDIP) database, respectively. Then, we constructed a regulatory network. Function enrichment analysis of miRNA and mRNA was performed by DIANA-miRPath v3.0 and Metascape database, respectively. mRNAs with significant prognostic value were identified based on expression profiles from The Cancer Genome Atlas (TCGA) database, and we constructed a subnetwork for them. To make the most of the network, we used the CLUE database to predict potential drugs for the treatment of osteosarcoma based on mRNA expression in the network. And we used the STITCH database to analyze and validate the interactions among these drugs and mRNAs, and to further screen for potential drugs.ResultsA total of 9 circRNAs, 19 miRNAs, 67 mRNAs, 54 pairs of circRNA-miRNA interactions and 110 pairs of miRNA-mRNA interactions were identified. A circRNA-miRNA-mRNA network was constructed. Function enrichment analysis indicated that these miRNAs and mRNAs in the network were involved in the process of tumorigenesis and immune response. Among these mRNAs, STC2 and RASGRP2 with significantly prognostic value were identified, and we constructed a subnetwork for them. Based on mRNA expression in the network, three potential drugs, quinacridine, thalidomide and zonisamide, were screened for the treatment of osteosarcoma. Among them, quinacridine and thalidomide have been proved to have anti-tumor effects in previous studies, while zonisamide has not been reported. And a corresponding drug-protein interaction network was constructed.ConclusionOverall, we constructed a circRNA-miRNA-mRNA regulatory network to investigate the possible mechanism in osteosarcoma, and predicted that quinacridine, thalidomide and zonisamide could be potential drugs for the treatment of osteosarcoma.

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Pivotal regulatory network and genes in osteosarcoma.

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Pivotal regulatory network and genes in osteosarcoma.

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