scholarly journals Integrated analysis of lncRNA–miRNA–mRNA ceRNA network and the potential prognosis indicators in sarcomas

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Lu Gao ◽  
Yu Zhao ◽  
Xuelei Ma ◽  
Ling Zhang
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Gene Prediction ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Integrated Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Cerna Network

Abstract Background Competitive endogenous RNA (ceRNA) networks have revealed a new mechanism of interaction between RNAs, and play crucial roles in multiple biological processes and development of neoplasms. They might serve as diagnostic and prognosis markers as well as therapeutic targets. Methods In this work, we identified differentially expressed mRNAs (DEGs), lncRNAs (DELs) and miRNAs (DEMs) in sarcomas by comparing the gene expression profiles between sarcoma and normal muscle samples in Gene Expression Omnibus (GEO) datasets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were applied to investigate the primary functions of the overlapped DEGs. Then, lncRNA-miRNA and miRNA-mRNA interactions were predicted, and the ceRNA regulatory network was constructed using Cytoscape software. In addition, the protein–protein interaction (PPI) network and survival analysis were performed. Results A total of 1296 DEGs were identified in sarcoma samples by combining the GO and KEGG enrichment analyses, 338 DELs were discovered after the probes were reannotated, and 36 DEMs were ascertained through intersecting two different expression miRNAs sets. Further, through target gene prediction, a lncRNA–miRNA–mRNA ceRNA network that contained 113 mRNAs, 69 lncRNAs and 29 miRNAs was constructed. The PPI network identified the six most significant hub proteins. Survival analysis revealed that seven mRNAs, four miRNAs and one lncRNA were associated with overall survival of sarcoma patients. Conclusions Overall, we constructed a ceRNA network in sarcomas, which might provide insights for further research on the molecular mechanism and potential prognosis biomarkers.

scholarly journals Integrated analysis of lncRNA-miRNA-mRNA ceRNA network and the potential prognosis indicators in sarcomas

2021 ◽  
Author(s):  
Lu Gao ◽  
Yu Zhao ◽  
Xuelei Ma ◽  
Ling Zhang
Keyword(s):  
Survival Analysis ◽  
Kegg Pathway ◽  
Gene Prediction ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Integrated Analysis ◽  
Ppi Network ◽  
Tissue Samples ◽  
Pathway Enrichment ◽  
Cerna Network

Abstract Background: Competitive endogenous RNA (ceRNA) has revealed a new mechanism of interaction between RNAs and been demonstrated to play crucial roles in multiple biological processes and in the development of neoplasms that potentially serve as diagnostic and prognosis markers as well as therapeutic targets.Methods：In this work, we identified differentially expressed mRNAs (DEGs), lncRNAs (DELs) and miRNAs (DEMs) in sarcoma by comparing the genes expression profiles between sarcoma samples and normal tissue samples in Gene Expression Omnibus (GEO) datasets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were applied to investigate the primary functions of the overlapped DEGs. Then, lncRNA-miRNA and miRNA-mRNA interactions were predicted, and the ceRNA regulatory network was constructed in Cytoscape. In addition, the protein-protein interaction (PPI) network was constructed and survival analysis was performed.Results: A total of 1296 DEGs were identified in sarcoma samples by combining the GO and KEGG pathway enrichment analyses, 338 DELs were discovered after the probes were reannotated, and 36 DEMs were ascertained through intersecting two different expression miRNAs sets. Further, through target gene prediction, a lncRNA-miRNA-mRNA ceRNA network that contained 113 mRNAs, 69 lncRNAs and 29 miRNAs was constructed. The PPI network identified the six most significant hub proteins. Survival analysis revealed that seven mRNAs, four miRNAs and one lncRNA were associated with overall survival of sarcoma patients.Conclusions: Overall, we constructed a ceRNA network in sarcomas, which likely provides insights for further research on the molecular mechanism and potential prognosis biomarkers.

scholarly journals Integrated analysis of lncRNA-miRNA-mRNA ceRNA network and the potential prognosis indicators in sarcomas

2020 ◽  
Author(s):  
Lu Gao ◽  
Yu Zhao ◽  
Xuelei Ma ◽  
Ling Zhang
Keyword(s):  
Survival Analysis ◽  
Kegg Pathway ◽  
Gene Prediction ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Integrated Analysis ◽  
Ppi Network ◽  
Tissue Samples ◽  
Pathway Enrichment ◽  
Cerna Network

Abstract Background: Competitive endogenous RNA (ceRNA) has been demonstrated to play crucial roles in multiple biological processes and in the development of neoplasms that potentially serve as diagnostic and prognosis markers as well as therapeutic targets.Methods: In this work, we identified differentially expressed mRNAs (DEGs), lncRNAs (DELs) and miRNAs (DEMs) in sarcoma by comparing the genes expression profiles between sarcoma samples and normal tissue samples in Gene Expression Omnibus (GEO) datasets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were applied to investigate the primary functions of the overlapped DEGs. Then, lncRNA-miRNA and miRNA-mRNA interactions were predicted, and the ceRNA regulatory network was constructed in Cytoscape. In addition, the protein-protein interaction (PPI) network was constructed and survival analysis was performed.Results: A total of 1296 DEGs were identified in sarcoma samples by combining the GO and KEGG pathway enrichment analyses, 338 DELs were discovered after the probes were reannotated, and 36 DEMs were ascertained through intersecting two different expression miRNAs sets. Further, through target gene prediction, a lncRNA-miRNA-mRNA ceRNA network that contained 113 mRNAs, 69 lncRNAs and 29 miRNAs was constructed. The PPI network identified the six most significant hub proteins. Survival analysis revealed that seven mRNAs, four miRNAs and one lncRNA were associated with overall survival of sarcoma patients.Conclusions: Overall, we constructed a ceRNA network in sarcomas, which likely provides insights for further research on the molecular mechanism and potential prognosis biomarkers.

scholarly journals Identification of Key Genes and Pathways for Enchondromas by Bioinformatics Analysis

Dose-Response ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 155932582090753
Author(s):  
Tianlong Wu ◽  
Honghai Cao ◽  
Lei Liu ◽  
Kan Peng
Keyword(s):  
Gene Expression ◽  
Endoplasmic Reticulum ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Protein Processing ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Exact Etiology

Background: The risk of malignant transformation of enchondromas (EC) toward central chondrosarcoma is increased up to 35%, while the exact etiology of EC is unknown. The purpose of this research was to authenticate gene signatures during EC and reveal their potential mechanisms in occurrence and development of EC. Methods: The gene expression profiles was acquired from Gene Expression Omnibus database (no. GSE22855). The gene ontology (GO), protein–protein interaction (PPI) network and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were utilized to identify differentially expressed genes (DEGs). Results: Finally, 242 DEGs were appraisal, containing 200 overregulated genes and 42 downregulated genes. The outcomes of GO analysis indicated that upregulated DEGs were mainly enriched in several biological processes containing response to hypoxia, calcium ion, and negative regulation extrinsic apoptotic signaling pathway. Furthermore, the upregulated DEGs were enriched in extracellular matrix (ECM)–receptor interaction, protein processing in endoplasmic reticulum and ribosome, which was analyzed by KEGG pathway. From the PPI network, the top 10 hub genes were identified, which were related to significant pathways containing ribosome, protein processing in endoplasmic reticulum, and ECM-receptor interaction. Conclusion: In conclusion, the present study may be helpful for understanding the diagnostic biomarkers of EC.

scholarly journals Identification of Lung-Cancer-Related Genes with the Shortest Path Approach in a Protein-Protein Interaction Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Bi-Qing Li ◽  
Jin You ◽  
Lei Chen ◽  
Jian Zhang ◽  
Ning Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Shortest Path ◽  
Protein Interaction ◽  
Expression Profiles ◽  
Shortest Paths ◽  
Gene Expression Profiles ◽  
Cancer Genes ◽  
Ppi Network ◽  
Protein Protein Interaction

Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutationPvalue less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.

scholarly journals Identification of Differential Gene Expression and Related Signaling Pathways In SARS-COV-2 Infected Human Cells

2021 ◽  
Author(s):  
Tian-Ao Xie ◽  
Hou-He Li ◽  
Zu-En Lin ◽  
Xiao-Ye Lin ◽  
Xin Meng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Vaccine Development ◽  
Virus Disease ◽  
Expression Profiles ◽  
Biological Significance ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Human Cells ◽  
Hub Genes ◽  
Protein Protein Interaction

Abstract Background: The Corona Virus Disease 2019 (COVID-19) pandemic poses a serious public health threat to the survival and health of people all over the world. We analyzed related mRNA data and gene expression profiles of human cell lines infected with SARS-CoV-2 obtained from GEO (GSE148729), using bioinformatics tools. Differentially expressed genes (DEGs) of human cells infected with SARS-CoV-2 were identified.Method: The GSE148729 datasets were downloaded from the Gene Expression Omnibus (GEO) database. To explore the Biological significance of DEGs, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of the DEGs was performed. Protein-protein interaction (PPI) networks of the DEGs were constructed by using the STRING database. The hub genes were selected using the Cytoscape Software, and a t-test was performed to validate the hub genes.Result: A total of 1241 DEGs were screened, including 1049 up-regulated genes and 192 down-regulated genes. Besides, 10 hub genes were obtained from the PPI network, among which the expression level of CXCL2, Etv7, and HIST1H2BG was found to be statistically significant.Conclusion: In conclusion, bioinformatics analysis reveals genes and cellular pathways that are significantly altered in SARS-CoV-2 infected cells. This is conducive to further guide the clinical study of SARS-CoV-2 and provides new perspectives for vaccine development.

scholarly journals Integrated analysis of lncRNA-miRNA-mRNA ceRNA network and the potential prognosis indicators in sarcomas

2020 ◽  
Author(s):  
Ling Zhang ◽  
Lu Gao ◽  
Yu Zhao ◽  
Xuelei Ma
Keyword(s):  
Survival Analysis ◽  
Protein Interactions ◽  
Sequence Data ◽  
Gene Prediction ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Integrated Analysis ◽  
Pathway Enrichment Analysis ◽  
Tissue Samples ◽  
Cerna Network

Abstract The ceRNA network has been demonstrated to play crucial roles in multiple biological processes and the development of neoplasms, which have the potential to become diagnostic and prognosis markers and therapeutic targets. In this work, we comparing the expression profiles between sarcoma identified differentially expressed genes (DEGs), lncRNAs (DELs) and miRNAs (DEMs) in sarcomas and normal tissue samples in GEO datasets. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied to investigate the major functions of the overlapping DEGs. Then, lncRNA-miRNA interactions and miRNA-mRNA interactions were predicted, and a ceRNA regulatory network was constructed. In addition, the mRNAs included in ceRNA network were used to construct the protein-protein interactions network, and the survival analysis of sarcomas was performed according to the biomarkers included in the ceRNA network. According to the RNA sequence data from GEO dataset, 1296 DEGs were identified in sarcoma samples by combining the GO and Pathway enrichment analysis, 338 DELs were discovered after re-annotating the probes, and 36 DEGs were ascertained through intersecting two different expression miRNAs sets. Further, 448 miRNA-mRNA interactions and 454 miRNA-lncRNA interactions were obtained through target gene prediction, and then, we constructed a lncRNA-miRNA-mRNA ceRNA network containing 9 miRNAs, 69 lncRNAs and 113 mRNAs. PPI network showed that the hub up-regulated nodes include IGF1, PRKCB and GNAI3, and the hub down-regulated nodes include AR, CYCS and PPP1CB. Survival analysis revealed that the expression levels of 12 RNAs involved in the ceRNA network were associated with overall survival of sarcoma patients. Our study showed that the ceRNA network in sarcomas based on that lncRNA could serve as ceRNA and discovered the potential indicators for prognosis of sarcoma patients.

scholarly journals Acute Pancreatitis-Associated Genes Identified by Integrative Analysis of Gene Expression Data

2021 ◽  
Author(s):  
Chunyan Zeng ◽  
Yin Zhu ◽  
Youxiang Chen
Keyword(s):  
Gene Expression ◽  
Acute Pancreatitis ◽  
Expression Profiles ◽  
Adherens Junction ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Diagnosis And Treatment ◽  
Expression Data ◽  
Protein Protein Interaction ◽  
And Control

Abstract Background Acute pancreatitis (AP) is a common digestive disease. A better understanding of the biology and molecular pathogenesis of AP may provide the basis for the prevention and therapy of AP.Methods The gene expression datasets GSE109227, GSE3644 and GSE65146, which included gene expression profiles of pancreas samples from AP and control mice, were obtained from the Gene Expression Omnibus database. All differentially expressed genes (DEGs) were identified using GEO2R and annotated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Results Thirty-eight DEGs were found to overlap among the three datasets. A protein-protein interaction (PPI) network was constructed; hub proteins were identified; and functional modules were extracted. Three genes (Cdh1, Iqgap1 and Actn4), which are involved in the adherens junction pathway, were identified using PPI analysis and may provide potential biomarkers for the diagnosis and treatment of AP.Conclusions We will further study the effects of the Cdh1, Iqgap1 and Actn4 genes on the occurrence and development of acute pancreatitis, which may provide new targets for the diagnosis and treatment of AP.

scholarly journals Identification and characterization of critical genes associated with tamoxifen resistance in breast cancer

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10468
Author(s):  
Kai Zhang ◽  
Kuikui Jiang ◽  
Ruoxi Hong ◽  
Fei Xu ◽  
Wen Xia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Target Genes ◽  
Expression Profiles ◽  
Tamoxifen Resistance ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Hub Genes ◽  
Mcf 7

Background Tamoxifen resistance in breast cancer is an unsolved problem in clinical practice. The aim of this study was to determine the potential mechanisms of tamoxifen resistance through bioinformatics analysis. Methods Gene expression profiles of tamoxifen-resistant MCF-7/TR and MCF-7 cells were acquired from the Gene Expression Omnibus dataset GSE26459, and differentially expressed genes (DEGs) were detected with R software. We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using Database for Annotation, Visualization and Integrated Discovery. A protein–protein interaction (PPI) network was generated, and we analyzed hub genes in the network with the Search Tool for the Retrieval of Interacting Genes database. Finally, we used siRNAs to silence the target genes and conducted the MTS assay. Results We identified 865 DEGs, 399 of which were upregulated. GO analysis indicated that most genes are related to telomere organization, extracellular exosomes, and binding-related items for protein heterodimerization. PPI network construction revealed that the top 10 hub genes—ACLY, HSPD1, PFAS, GART, TXN, HSPH1, HSPE1, IRAS, TRAP1, and ATIC—might be associated with tamoxifen resistance. Consistently, RT-qPCR analysis indicated that the expression of these 10 genes was increased in MCF-7/TR cells comparing with MCF-7 cells. Four hub genes (TXN, HSPD1, HSPH1 and ATIC) were related to overall survival in patients who accepted tamoxifen. In addition, knockdown of HSPH1 by siRNA may lead to reduced growth of MCF-7/TR cell with a trend close to significance (P = 0.07), indicating that upregulation of HSPH1 may play a role in tamoxifen resistance. Conclusion This study revealed a number of critical hub genes that might serve as therapeutic targets in breast cancer resistant to tamoxifen and provided potential directions for uncovering the mechanisms of tamoxifen resistance.

scholarly journals Study of Osteoarthritis-Related Hub Genes Based on Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zhengqing Zhu ◽  
Lei Zhong ◽  
Ronghang Li ◽  
Yuzhe Liu ◽  
Xiangrun Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Protein Protein Interaction ◽  
Polymerase Chain ◽  
Upregulated Genes ◽  
Protein Protein Interaction Network

Osteoarthritis (OA) is a common cause of morbidity and disability worldwide. However, the pathogenesis of OA is unclear. Therefore, this study was conducted to characterize the pathogenesis and implicated genes of OA. The gene expression profiles of GSE82107 and GSE55235 were downloaded from the Gene Expression Omnibus database. Altogether, 173 differentially expressed genes including 68 upregulated genes and 105 downregulated genes in patients with OA were selected based on the criteria of ∣log fold‐change∣>1 and an adjusted p value < 0.05. Protein-protein interaction network analysis showed that FN1, COL1A1, IGF1, SPP1, TIMP1, BGN, COL5A1, MMP13, CLU, and SDC1 are the top ten genes most closely related to OA. Quantitative reverse transcription-polymerase chain reaction showed that the expression levels of COL1A1, COL5A1, TIMP1, MMP13, and SDC1 were significantly increased in OA. This study provides clues for the molecular mechanism and specific biomarkers of OA.

scholarly journals Identification of significant alteration genes, pathways and TFs induced by LPS in ARDS via bioinformatical analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Weina Lu ◽  
Ran Ji
Keyword(s):  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Integrated Analysis ◽  
Pathway Enrichment Analysis ◽  
Signal Pathways ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction

Abstract Background and aims Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is one of the most common acute thoracopathy with complicated pathogenesis in ICU. The study is to explore the differentially expressed genes (DEGs) in the lung tissue and underlying altering mechanisms in ARDS. Methods Gene expression profiles of GSE2411 and GSE130936 were available from GEO database, both of them included in GPL339. Then, an integrated analysis of these genes was performed, including gene ontology (GO) and KEGG pathway enrichment analysis in DAVID database, protein–protein interaction (PPI) network construction evaluated by the online database STRING, Transcription Factors (TFs) forecasting based on the Cytoscape plugin iRegulon, and their expression in varied organs in The Human Protein Atlas. Results A total of 39 differential expressed genes were screened from the two datasets, including 39 up-regulated genes and 0 down-regulated genes. The up-regulated genes were mainly enriched in the biological process, such as immune system process, innate immune response, inflammatory response, and also involved in some signal pathways, including cytokine–cytokine receptor interaction, Salmonella infection, Legionellosis, Chemokine, and Toll-like receptor signal pathway with an integrated analysis. GBP2, IFIT2 and IFIT3 were identified as hub genes in the lung by PPI network analysis with MCODE plug-in, as well as GO and KEGG re-enrichment. All of the three hub genes were regulated by the predictive common TFs, including STAT1, E2F1, IRF1, IRF2, and IRF9. Conclusions This study implied that hub gene GBP2, IFIT2 and IFIT3, which might be regulated by STAT1, E2F1, IRF1, IRF2, or IRF9, played significant roles in ARDS. They could be potential diagnostic or therapeutic targets for ARDS patients.

Sign in / Sign up

Export Citation Format

Share Document