scholarly journals Identification of Crucial lncRNAs, miRNAs, mRNAs, and Potential Therapeutic Compounds for Polycystic Ovary Syndrome by Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Zhi Zeng ◽  
Xia Lin ◽  
Tingting Xia ◽  
Wenxiu Liu ◽  
Xiaohui Tian ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Polycystic Ovary ◽  
Gene Expression Omnibus ◽  
Messenger Rnas ◽  
Ppi Network ◽  
Ovary Syndrome ◽  
Protein Protein Interaction ◽  
Cerna Network ◽  
Therapeutic Drugs ◽  
Therapeutic Compounds

Background. This study was aimed at mining crucial long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) for the development of polycystic ovary syndrome (PCOS) based on the coexpression and the competitive endogenous RNA (ceRNA) theories and investigating the underlying therapeutic drugs that may function by reversing the expression of lncRNAs, miRNAs, and mRNAs. Methods. RNA (GSE106724, GSE114419, GSE137684, and GSE138518) or miRNA (GSE84376 and GSE138572) expression profile datasets of PCOS patients were downloaded from the Gene Expression Omnibus database. The weighted gene coexpression network analysis (WGCNA) using four RNA datasets was conducted to construct the lncRNA-mRNA coexpression networks, while the common differentially expressed miRNAs in two miRNA datasets and module RNAs were used to establish the ceRNA network. A protein-protein interaction (PPI) network was created to explore the potential interactions between genes. Gene Ontology and KEGG pathway enrichment analyses were performed to explore the functions of genes in networks. Connectivity Map (CMap) and Comparative Toxicogenomics Database (CTD) analyses were performed to identify potential therapeutic agents for PCOS. Results. Three modules (black, magenta, and yellow) were identified to be PCOS-related after WGCNA analysis, in which KLF3-AS1-PLCG2, MAPKAPK5-AS1-MAP3K14, and WWC2-AS2-TXNIP were important coexpression relationship pairs. WWC2-AS2-hsa-miR-382-PLCG2 was a crucial ceRNA loop in the ceRNA network. The PPI network showed that MAP3K14 and TXNIP could interact with hub genes PLK1 ( degree = 21 ) and TLR1 ( degree = 18 ), respectively. These genes were enriched into mitosis (PLK1), immune response (PLCG2 and TLR1), and cell cycle (TXNIP and PLK1) biological processes. Ten small molecule drugs (especially quercetin) were considered to be therapeutical for PCOS. Conclusion. Our study may provide a novel insight into the mechanisms and therapy for PCOS.

scholarly journals Development and analysis of long non-coding RNA-associated competing endogenous RNA network for osteosarcoma metastasis

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Yucheng Fu ◽  
Qi Liu ◽  
Qiyuan Bao ◽  
Junxiang Wen ◽  
Zhuochao Liu ◽  
...  
Keyword(s):  
Malignant Neoplasm ◽  
Gene Expression Omnibus ◽  
Messenger Rnas ◽  
Competing Endogenous Rna ◽  
Survival Analyses ◽  
Protein Protein Interaction ◽  
Cerna Network ◽  
Competing Endogenous Rna Network ◽  
New Perspective ◽  
Pathway Analyses

Abstract Background Osteosarcoma is the primary bone malignant neoplasm that often develops metastasis. Increasing evidences have shown that non-coding RNAs (ncRNAs) relate to the progression of osteosarcoma. However, the ncRNAs’ roles in osteosarcoma metastasis are still unknown. Methods Differentially expressed (DE) RNAs were identified from Gene Expression Omnibus (GEO) database. Protein-protein interaction (PPI) of DE messenger RNAs (DEmRNAs) was built through STRING database. The target mRNAs and long ncRNAs (lncRNAs) of microRNAs (miRNA) were predicted through miRDB, Targetscan and Genecode databases, which then cross-checked with previously obtained DERNAs to construct competing endogenous RNA (ceRNA) network. All networks were visualized via Cytoscape and the hub RNAs were screened out through Cytoscape plug-in Cytohubba. The gene functional and pathway analyses were performed through DAVID and MirPath databases. The survival analyses of hub RNAs were obtained through Kaplan-Meier (KM) survival curves. Results Five hundred sixty-four DEmRNAs, 16 DElncRNAs and 22 DEmiRNAs were screened out. GO functional and KEGG pathway analyses showed that DERNAs were significantly associated with tumor metastasis. The ceRNA network including 6 lncRNAs, 55 mRNAs and 20 miRNAs were constructed and the top 10 hub RNAs were obtained. Above all, PI3K/AKT signaling pathway was identified as the most important osteosarcoma metastasis-associated pathway and its hub ceRNA module was constructed. The survival analyses showed that the RNAs in hub ceRNA module closely related to osteosarcoma patients’ prognosis. Conclusions The current study provided a new perspective on osteosarcoma metastasis. More importantly, the RNAs in hub ceRNA module might act as the novel therapeutic targets and prognostic factors for osteosarcoma patients.

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.

Key genes and pathways of ovarian granulosa cells in polycystic ovary syndrome identified by bioinformatics analysis

2021 ◽  
Author(s):  
Qianqian Liang ◽  
Dai-Jun Wang
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Polycystic Ovary ◽  
Platelet Activating Factor ◽  
Diagnosis And Treatment ◽  
Analysis Tool ◽  
Hub Genes ◽  
Ovary Syndrome ◽  
Online Analysis ◽  
Protein Protein Interaction ◽  
Matrix Metallopeptidase

Abstract Purpose Polycystic ovary syndrome (PCOS) is one of the factors leading to infertility. The specific pathogenesis of PCOS is still unclear. The purpose of this study was to determine key changes in gene expression during the formation of PCOS and to provide a theoretical basis for the clinical diagnosis and treatment of PCOS. Methods We analyzed differentially expressed genes (DEGs) in the dataset GSE34526 from the bioinformatics array research tool (BART) online analysis tool (bart.salk.edu). Then, through the Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/) online analysis software for gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) rich path analysis, STRING (https://string-db.org/) online analysis tool for protein-protein interaction (PPI) network, Cytoscape software for Mcode module and HUB gene analysis Results a total of 91 DEGs, 7 up-regulated and 84 down-regulated, were found. Seven central HUB genes were identified, including integrin alpha-M (ITGAM), cytochrome BMUR 245 beta chain (CYBB), toll like receptor 1 (TLR1), platelet activating factor receptor (PTAFR), CD163 molecule, caspase 1 (CASP1), and matrix metallopeptidase 9 (MMP 9). Conclusion the DEGs, HUB genes and signaling pathways identified in this study help us understand the molecular mechanism of PCOS formation and provide new targets for the diagnosis and treatment of PCOS.

scholarly journals Exploring the Pharmacological Mechanism of Quercetin-Resveratrol Combination for Polycystic Ovary Syndrome: A Systematic Pharmacological Strategy-Based Research

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kailin Yang ◽  
Liuting Zeng ◽  
Tingting Bao ◽  
Zhiyong Long ◽  
Bing Jin
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Molecular Mechanism ◽  
Polycystic Ovary ◽  
Pathway Enrichment Analysis ◽  
Biological Processes ◽  
Ppi Network ◽  
Ovary Syndrome ◽  
Potential Target ◽  
Target Network ◽  
Compound Target

AbstractResveratrol and quercetin have effects on polycystic ovary syndrome (PCOS). Hence, resveratrol combined with quercetin may have better effects on it. However, because of the limitations in animal and human experiments, the pharmacological and molecular mechanism of quercetin-resveratrol combination (QRC) remains to be clarified. In this research, a systematic pharmacological approach comprising multiple compound target collection, multiple potential target prediction, and network analysis was used for comparing the characteristic of resveratrol, quercetin and QRC, and exploring the mechanism of QRC. After that, four networks were constructed and analyzed: (1) compound-compound target network; (2) compound-potential target network; (3) QRC-PCOS PPI network; (4) QRC-PCOS-other human proteins (protein-protein interaction) PPI network. Through GO and pathway enrichment analysis, it can be found that three compounds focus on different biological processes and pathways; and it seems that QRC combines the characteristics of resveratrol and quercetin. The in-depth study of QRC further showed  more PCOS-related biological processes and pathways. Hence, this research not only offers clues to the researcher who is interested in comparing the differences among resveratrol, quercetin and QRC, but also provides hints for the researcher who wants to explore QRC’s various synergies and its pharmacological and molecular mechanism.

scholarly journals Identification of key pathways and genes in polycystic ovary syndrome via integrated bioinformatics analysis and prediction of small therapeutic molecules

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Praveenkumar Devarbhavi ◽  
Lata Telang ◽  
Basavaraj Vastrad ◽  
Anandkumar Tengli ◽  
Chanabasayya Vastrad ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Gene Network ◽  
Mirna Target ◽  
Target Gene ◽  
Bioinformatics Analysis ◽  
Polycystic Ovary ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Ovary Syndrome

AbstractTo enhance understanding of polycystic ovary syndrome (PCOS) at the molecular level; this investigation intends to examine the genes and pathways associated with PCOS by using an integrated bioinformatics analysis. Based on the expression profiling by high throughput sequencing data GSE84958 derived from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) between PCOS samples and normal controls were identified. We performed a functional enrichment analysis. A protein-protein interaction (PPI) network, miRNA- target genes and TF - target gene networks, were constructed and visualized, with which the hub gene nodes were identified. Validation of hub genes was performed by using receiver operating characteristic (ROC) and RT-PCR. Small drug molecules were predicted by using molecular docking. A total of 739 DEGs were identified, of which 360 genes were up regulated and 379 genes were down regulated. GO enrichment analysis revealed that up regulated genes were mainly involved in peptide metabolic process, organelle envelope and RNA binding and the down regulated genes were significantly enriched in plasma membrane bounded cell projection organization, neuron projection and DNA-binding transcription factor activity, RNA polymerase II-specific. REACTOME pathway enrichment analysis revealed that the up regulated genes were mainly enriched in translation and respiratory electron transport and the down regulated genes were mainly enriched in generic transcription pathway and transmembrane transport of small molecules. The top 10 hub genes (SAA1, ADCY6, POLR2K, RPS15, RPS15A, CTNND1, ESR1, NEDD4L, KNTC1 and NGFR) were identified from PPI network, miRNA - target gene network and TF - target gene network. The modules analysis showed that genes in modules were mainly associated with the transport of respiratory electrons and signaling NGF, respectively. We find a series of crucial genes along with the pathways that were most closely related with PCOS initiation and advancement. Our investigations provide a more detailed molecular mechanism for the progression of PCOS, detail information on the potential biomarkers and therapeutic targets.

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