Comprehensive Analysis of a lncRNA-miRNA-mRNA Competing Endogenous RNA Network in Heart Failure

Abstract Background: Acute heart failure caused by progressive heart failure is a common disease in intensive care units (ICU). The growing incidence rate of heart failure and its high mortality rate result are very important sociosanitary problems. Therefore, it is important to identify the molecular mechanism by which heart failure occurs and to identify treatment for this mechanism. Recently, the mechanism of ceRNA has attracted increasing attention. The aim of the present study was to identify the candidate ceRNA network in the progression of heartfailure.Method: Microarray datasets GSE9128, GSE61741 and GSE77399 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and identification of hub-genes was performed using STRING and Cytoscape. Furthermore, according to the ceRNA theory, network of ceRNA was constructed.Result: In the present study, based on the ceRNA theory and above series of analyses, network of ceRNA which include 7 mRNAs (BCL2A1, DUSP1, EGR1, MYC, NR4A2, PTGS2 and RAC2), 3 miRNAs (miR-20a, miR-129-59 and miR-185-5p) and 3 lncRNAs (GAS5, H19 and PCGEM1) were obtained.Conclusion: In conclusion, these findings can be used to carrying on further study to identify the important roles of the ceRNA, biological function, appropriate treatment targets and biomarkers in the progression of heart failure.

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Integrate analysis and identification for different expression genes in chondrogenesis

10.21203/rs.2.20142/v1 ◽

2020 ◽

Author(s):

Keda Liu ◽

Nanjue Cao ◽

Yuhe Zhu ◽

Wei Wang

Keyword(s):

Interaction Network ◽

Bioinformatic Analysis ◽

Gene Expression Omnibus ◽

Data Sets ◽

Protein Protein Interaction ◽

Related Data ◽

Integrate Analysis ◽

And Function ◽

Microarray Datasets ◽

Geo Database

Abstract Background: The intricate mechanisms of articular chondrogenesis are largely unknown. Gradually, with the help of high-throughput platforms, microarrays have become an important and useful method to testify hub genes in desease. Today, advanced bioinformatic analysis of available microarray data can provide more reliable and accurate screening results by duplicating related data sets. Results: Microarray datasets GSE9451 and GSE104113 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were performed, and function enrichment analyses were demonstrated. The protein-protein interaction network (PPI) was constructed and the module analysis was performed by using STRING and Cytoscape. Quantitative PCR was used to confirm the results of bioinformatics analysis. Conclusion: Compared to individual studies, this study can provide extra reliable and accurate screening results by duplicating relevant records. Additional molecular experiments are required to confirm the discovery of candidate genes identified by chondrogenesis. S100A4 is predicted to integrate with miR-325-3p to promote osteogenesis.

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An in-silico method leads to recognition of hub genes and crucial pathways in survival of patients with breast cancer

Scientific Reports ◽

10.1038/s41598-020-76024-2 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Sepideh Dashti ◽

Mohammad Taheri ◽

Soudeh Ghafouri-Fard

Keyword(s):

Breast Cancer ◽

Clinical Importance ◽

Interaction Network ◽

R Package ◽

Gene Expression Omnibus ◽

Hub Genes ◽

Protein Protein Interaction ◽

Ncbi Gene Expression Omnibus ◽

Kaplan Meier ◽

Microarray Datasets

Abstract Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. In the current study, we aim to use a system biology strategy to identify key genes and signaling pathways in breast cancer. We have retrieved data of two microarray datasets (GSE65194 and GSE45827) from the NCBI Gene Expression Omnibus database. R package was used for identification of differentially expressed genes (DEGs), assessment of gene ontology and pathway enrichment evaluation. The DEGs were integrated to construct a protein–protein interaction network. Next, hub genes were recognized using the Cytoscape software and lncRNA–mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. Finally, the clinical importance of the obtained genes was assessed using Kaplan–Meier survival analysis. In the present study, 887 DEGs including 730 upregulated and 157 downregulated DEGs were detected between breast cancer and normal samples. By combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 2 hub genes including MAD2L1 and CCNB1 were selected. We also identified 12 lncRNAs with significant correlation with MAD2L1 and CCNB1 genes. According to The Kaplan–Meier plotter database MAD2L1, CCNA2, RAD51-AS1 and LINC01089 have the most prediction potential among all candidate hub genes. Our study offers a framework for recognition of mRNA–lncRNA network in breast cancer and detection of important pathways that could be used as therapeutic targets in this kind of cancer.

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FRI0584 SCREENING AND IDENTIFICATION OF BIOMARKERS IN MYOSITIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.1831 ◽

2020 ◽

Vol 79 (Suppl 1) ◽

pp. 896-897

Author(s):

W. Liu ◽

X. Zhang

Keyword(s):

Gene Expression ◽

Interstitial Lung Disease ◽

Lung Disease ◽

Molecular Mechanisms ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Hub Genes ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network ◽

Geo Database

Background:Myositis, including dermatomyositis and polymyositis, is autoimmune disorders that is characterized by muscle degeneration in the proximal extremities, with the complications of weakness of muscles, interstitial lung disease and vascular lesions, even leading to death in an acute progressive process[1,2]. However, the molecular mechanisms of myositis are rarely understood.Objectives:Identify the candidate genes in myositis.Methods:Microarray datasets GSE128470, GSE48280 and GSE39454 were extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and function enrichment analyses were conducted. The protein-protein interaction network and the analyses of hub genes were performed with STRING and Cytoscape.Results:There were 98 DEGs, of which the function and pathways enrichment analyses showed defense response, immune response, response to virus, inflammatory response, response to wounding, cell adhesion, cell proliferation, cell death and macromolecule metabolic process. 20 hub genes were identified, of which 7 including IRF9 TRIM22 MX2 IFITM1 IFI6 IFI44 IFI44L had not been reported in the literature, related to the response to virus, immune response, transcription from RNA polymerase II promoter, cell apoptosis, cell death. The verification analysis about the 7 genes in GSE128314 showed significant differences in myositis.Conclusion:In conclusion, DEGs and hub genes identified in our study showed the potential molecular mechanisms in myositis, providing the helpful targets for diagnosis and clinical strategy of myositis.References:[1] Wu H, Geng D, Xu J. An approach to the development of interstitial lung disease in dermatomyositis: a study of 230 cases in China[J]. Journal of International Medical Research. 2013;41(2):493–501.[2] Fathi M, Dastmalchi M, Rasmussen E, Lundberg IE, Tornling G. Interstitial lung disease, a common manifestation of newly diagnosed polymyositis and dermatomyositis[J]. Annals of the Rheumatic Diseases. 2004;63(3):297–301.Figure 1.The protein-protein interaction network of 20 hub genesFigure 2.7 genes in GSE128314 showed significant differences in myositisAcknowledgments:The authors acknowledge the efforts of the Gene Expression Omnibus (GEO) database. The interpretation and reporting of these data are the sole responsibility of the authors.Disclosure of Interests:None declared

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Bioinformatics analysis of high-throughput data to validate potential novel biomarkers and small molecule drugs for glioblastoma multiforme

Journal of International Medical Research ◽

10.1177/0300060520924541 ◽

2020 ◽

Vol 48 (7) ◽

pp. 030006052092454

Author(s):

Fuwei Qi ◽

Qing Li ◽

Xiaojun Lu ◽

Zhihua Chen

Keyword(s):

Gene Expression ◽

Glioblastoma Multiforme ◽

Molecular Mechanisms ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Protein Protein Interaction ◽

Diagnosis And Prognosis ◽

Novel Biomarkers ◽

Patient Prognosis ◽

Microarray Datasets

Objective There have been no recent improvements in the glioblastoma multiforme (GBM) outcome, with median survival remaining 15 months. Consequently, the need to identify novel biomarkers for GBM diagnosis and prognosis, and to develop targeted therapies is high. This study aimed to establish biomarkers for GBM pathogenesis and prognosis. Methods In total, 220 overlapping differentially expressed genes (DEGs) were obtained by integrating four microarray datasets from the Gene Expression Omnibus database (GSE4290, GSE12657, GSE15824, and GSE68848). Then a 140-node protein–protein interaction network with 343 interactions was constructed. Results The immune response and cell adhesion molecules were the most significantly enriched functions and pathways, respectively, among DEGs. The designated hub genes ITGB5 and RGS4, which have a high degree of connectivity, were closely correlated with patient prognosis, and GEPIA database mining further confirmed their differential expression in GBM versus normal tissue. We also determined the 20 most appropriate small molecules that could potentially reverse GBM gene expression, Prestwick-1080 was the most promising and had the highest negative scores. Conclusions This study identified ITGB5 and RGS4 as potential biomarkers for GBM diagnosis and prognosis. Insights into molecular mechanisms governing GBM occurrence and progression will help identify alternative biomarkers for clinical practice.

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Bioinformatics Analysis of C3 and CXCR4 act as Potential Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma (ccRCC)

10.21203/rs.3.rs-319333/v1 ◽

2021 ◽

Author(s):

Jing Quan ◽

YUCHEN BAI ◽

YunBei Yang ◽

ErLei Han ◽

Hong Bai ◽

...

Keyword(s):

Candidate Genes ◽

Interaction Network ◽

Gene Expression Omnibus ◽

High Expression ◽

Individual Treatment ◽

Hub Genes ◽

Cell Renal Cell Carcinoma ◽

Protein Protein Interaction ◽

Microarray Datasets ◽

Upregulated Genes

Abstract Background: The molecular pathogenesis of ccRCC was still unknown. Hence, the ccRCC-associated genes needs to explored.Methods: Three ccRCC expression microarray datasets (GSE14762, GSE66270 and GSE53757) downloaded from gene expression omnibus (GEO) database. The distinguish of expressed genes (DEGs) between ccRCC and normal tissue was discuss and explored. the function of our DEGs was analyzed by Gene Ontology (GO) ,Kyoto Encyclopedia of Genes and Genomes (KEGG) .Then the protein‑protein interaction network (PPI) was established in order to screen the hub genes. Then the expressions of hub genes were identified by oncomine database.The prognostic values of hub genes were analyzed by GEPIA database in ccRCC patients. Result: A total of 137 DREs were analyzed, which including 63 upregulated genes and 74 downregulated genes. According to our result,137 DREs were mainly enriched in 82 functional terms and 24 pathways. 14 highest-scoring genes were screened as hub gene in the PPI network which including 12 upregulated candidate genes and 2 downregulated candidate genes. The result reveals that patients with higher C3 expression related to poor OS, while patients with high expression of CTSS and TLR3 related to better OS. Patients with high C3 and CXCR4 expression had a poor DFS, while ccRCC patients with high expression of TLR3 had better DFS. At last, C3 and CXCR4 were selected to detect the prognosis of patients with ccRCC.Conclusion: The result identified the C3 and CXCR4 as candidate biomarkers and potential therapeutic targets in the molecular mechanism and individual treatment of ccRCC.

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Identification of Functional Genes in Pterygium Based on Bioinformatics Analysis

BioMed Research International ◽

10.1155/2020/2383516 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Yuting Xu ◽

Chen Qiao ◽

Siying He ◽

Chen Lu ◽

Shiqi Dong ◽

...

Keyword(s):

Protein Interaction ◽

Growth Regulation ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Node Degree ◽

Hub Genes ◽

Protein Protein Interaction ◽

Cerna Network ◽

R Programming ◽

Geo Database

Purpose. The competing endogenous RNA (ceRNA) network regulatory has been investigated in the occurrence and development of many diseases. This research aimed at identifying the key RNAs of ceRNA network in pterygium and exploring the underlying molecular mechanism. Methods. Differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs were obtained from the Gene Expression Omnibus (GEO) database and analyzed with the R programming language. LncRNA and miRNA expressions were extracted and pooled by the GEO database and compared with those in published literature. The lncRNA-miRNA-mRNA network was constructed of selected lncRNAs, miRNAs, and mRNAs. Metascape was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on mRNAs of the ceRNA network and to perform Protein-Protein Interaction (PPI) Network analysis on the String website to find candidate hub genes. The Comparative Toxicogenomic Database (CTD) was used to find hub genes closely related to pterygium. The differential expressions of hub genes were verified using the reverse transcription-real-time fluorescent quantitative PCR (RT-qPCR). Result. There were 8 lncRNAs, 12 miRNAs, and 94 mRNAs filtered to construct the primary ceRNA network. A key lncRNA LIN00472 ranking the top 1 node degree was selected to reconstruct the LIN00472 network. The GO and KEGG pathway enrichment showed the mRNAs in ceRNA networks mainly involved in homophilic cell adhesion via plasma membrane adhesion molecules, developmental growth, regulation of neuron projection development, cell maturation, synapse assembly, central nervous system neuron differentiation, and PID FOXM1 PATHWAY. According to the Protein-Protein Interaction Network (PPI) analysis on mRNAs in LINC00472 network, 10 candidate hub genes were identified according to node degree ranking. Using the CTD database, we identified 8 hub genes closely related to pterygium; RT-qPCR verified 6 of them were highly expressed in pterygium. Conclusion. Our research found LINC00472 might regulate 8 hub miRNAs (miR-29b-3p, miR-183-5p, miR-138-5p, miR-211-5p, miR-221-3p, miR-218-5p, miR-642a-5p, miR-5000-3p) and 6 hub genes (CDH2, MYC, CCNB1, RELN, ERBB4, RB1) in the ceRNA network through mainly PID FOXM1 PATHWAY and play an important role in the development of pterygium.

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Title: Weighted Gene Co-Expression Network Analysis Identifies Five Hub Genes Associated with Metastasis in Synovial Sarcoma

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210628112429 ◽

2021 ◽

Vol 24 ◽

Author(s):

Hongzeng Wu ◽

Benzheng Zhang ◽

Jiazheng Zhao ◽

Yi Zhao ◽

Xiaowei Ma ◽

...

Keyword(s):

Synovial Sarcoma ◽

Tumor Metastasis ◽

Gene Expression Omnibus ◽

Survival Ratio ◽

Hub Genes ◽

Protein Protein Interaction ◽

Metastatic Lesions ◽

Ppi Networks ◽

Malignant Soft Tissue ◽

Geo Database

Background: Synovial sarcoma (SS) refers to a malignant soft tissue sarcoma (STS) which often occurs in children and adults and has a poor prognosis in elderly patients. Patients with local lesions can be treated with extensive surgical resection combined with adjuvant or radiotherapy, whereas about half of the cases have recurrent diseases and metastatic lesions, and five-year survival ratio is assessed within the range of 27% - 55% only. Method: We downloaded a set of expression profile data (GSE40021) related to SS metastasis based on the Gene Expression Omnibus (GEO) database, and selected distinctly represented genes (DEGs) related to tumor metastasis. WGCNA was used to emphasize the DEGs related to tumor metastasis and obtain co-expression modules. Then, the module most related to SS metastasis was screened out. The genes enriched in this module were analyzed by Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway improvement analysis. Cytoscape software was used for constructing protein-protein interaction (PPI) networks, and hub genes were screened in Oncomine analysis. Result: We selected 514 DEGs, consisting of 210 up-regulated genes and 304 down-regulated genes. Through WGCAN, we got seven co-expression modules and the module most related to SS metastasis was the turquoise module, which contained 66 genes. Finally, we screened out five hub genes (HJURP, NCAPG, TPX2, CENPA, NDC80) through CytoHubba and Oncomine analysis. Conclusion: In this study, we screened five hub genes that may help in clinical diagnosis and serve as the latent purpose of SS treatment.

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Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

Bioscience Reports ◽

10.1042/bsr20201704 ◽

2020 ◽

Vol 40 (11) ◽

Author(s):

Wei Han ◽

Biao Huang ◽

Xiao-Yu Zhao ◽

Guo-Liang Shen

Keyword(s):

Gene Expression ◽

Tumor Microenvironment ◽

Metastatic Melanoma ◽

Interaction Network ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Protein Protein Interaction ◽

Subtype Identification ◽

Cancer Genome Atlas ◽

Immune Related Genes

Abstract Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune-related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune-related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for metastatic melanoma.

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Identification of Potential Biomarkers and Pathways in Neonatal Hypoxic-Ischemic Brain Injury: Based on Bioinformatics Technology

10.21203/rs.3.rs-777916/v1 ◽

2021 ◽

Author(s):

Shangbin Li ◽

Shuangshuang Li ◽

Qian Zhao ◽

Jiayu Huang ◽

Jinfeng Meng ◽

...

Keyword(s):

Signaling Pathways ◽

Gene Expression Omnibus ◽

Biological Processes ◽

Ischemic Brain ◽

Protein Protein Interaction ◽

Immune Mediated ◽

Disability Rate ◽

Potential Biomarker ◽

Microarray Datasets ◽

Cerebral Cortex Tissue

Abstract Background Neonatal hypoxic-ischemic brain damage (HIBD) is one of the most common serious diseases in newborns, with a high mortality and disability rate. This study aims to use the bioinformatics analysis to identify potential hematologic/immune systems tissue-specific genes and related signaling pathways neonatal HIBD.Methods Microarray datasets in HIBD were downloaded from the Gene Expression Omnibus database, and DEGs were identified by R software.Enrichment analyses were performed and protein–protein interaction networks were constructed to understand the functions and enriched pathways of DEGs and to identify central genes and key modules. Results In the cerebral cortex tissue with HIBD, 2598 DEGs were identified, including 2362 up-regulated and 236 down-regulated DEGs. In the blood with HIBD, 1442 DEGs were identified, including 540 up-regulated and 902 down-regulated DEGs. The results of biological processes and KEGG enrichment were very similar in DEGs of the two kinds of tissues, and both involved inflammation, immunity and apoptosis. The common DEGs of the two kinds of tissues also showed similar results in biological processes and KEGG enrichment.and four hematologic/immune system tissues specifically expressed potential biomarker genes were confirmed through a variety of methods, which were verified by GEO datasets and published experimental research. Conclusion The DEGs of HIBD including the potential peripheral biomarkers TYROBP, ITGAM, EGR1 and HMOX1, which may play a role in the pathogenesis of HIBD through inflammation and immune-mediated signaling pathways.

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Identification and validation of a hub gene prognostic index for hepatocellular carcinoma

Future Oncology ◽

10.2217/fon-2020-1112 ◽

2021 ◽

Author(s):

Q Shi ◽

Z Meng ◽

XX Tian ◽

YF Wang ◽

WH Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Prognostic Index ◽

Interaction Network ◽

Gene Signature ◽

Gene Expression Omnibus ◽

Cox Regression Analysis ◽

Protein Protein Interaction ◽

Normal Tissues ◽

Significant Independent Prognostic Factor

Aims: We aim to provide new insights into the mechanisms of hepatocellular carcinoma (HCC) and identify key genes as biomarkers for the prognosis of HCC. Materials & methods: Differentially expressed genes between HCC tissues and normal tissues were identified via the Gene Expression Omnibus tool. The top ten hub genes screened by the degree of the protein nodes in the protein–protein interaction network also showed significant associations with overall survival in HCC patients. Results: A prognostic model containing a five-gene signature was constructed to predict the prognosis of HCC via multivariate Cox regression analysis. Conclusion: This study identified a novel five-gene signature ( CDK1, CCNB1, CCNB2, BUB1 and KIF11) as a significant independent prognostic factor.

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