scholarly journals In Silico Integration Approach Reveals Key MicroRNAs and Their Target Genes in Follicular Thyroid Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Shengqing Hu ◽  
Yunfei Liao ◽  
Juan Zheng ◽  
Luoning Gou ◽  
Anita Regmi ◽  
...  
Keyword(s):  
Thyroid Carcinoma ◽  
Target Genes ◽  
Follicular Thyroid Carcinoma ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Biological Targets ◽  
Protein Protein Interaction ◽  
Bioinformatic Tools ◽  
The Common

To better understand the molecular mechanism for the pathogenesis of follicular thyroid carcinoma (FTC), this study aimed at identifying key miRNAs and their target genes associated with FTC, as well as analyzing their interactions. Based on the gene microarray data GSE82208 and microRNA dataset GSE62054, the differentially expressed genes (DEGs) and microRNAs (DEMs) were obtained using R and SAM software. The common DEMs from R and SAM were fed to three different bioinformatic tools, TargetScan, miRDB, and miRTarBase, respectively, to predict their biological targets. With DEGs intersected with target genes of DEMs, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed through the DAVID database. Then a protein-protein interaction (PPI) network was constructed by STRING. Finally, the module analysis for PPI network was performed by MCODE and BiNGO. A total of nine DEMs were identified, and their function might work through regulating hub genes in the PPI network especially KIT and EGFR. KEGG analysis showed that intersection genes were enriched in the PI3K-Akt signaling pathway and microRNAs in cancer. In conclusion, the study of miRNA-mRNA network would offer molecular support for differential diagnosis between malignant FTC and benign FTA, providing new insights into the potential targets for follicular thyroid carcinoma diagnosis and treatment.

scholarly journals Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein–protein interaction network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suthanthiram Backiyarani ◽  
Rajendran Sasikala ◽  
Simeon Sharmiladevi ◽  
Subbaraya Uma
Keyword(s):  
Candidate Genes ◽  
Protein Interaction ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Auxin Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
The Difference

AbstractBanana, one of the most important staple fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein–protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By further validating these candidate genes in seeded and seedless accession of Musa spp. we put forward MaAGL8, MaMADS16, MaGH3.8, MaMADS29, MaRGA1, MaEXPA1, MaGID1C, MaHK2 and MaBAM1 as possible target genes in the study of natural parthenocarpy. In contrary, expression profile of MaACLB-2 and MaZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. By exploring the PPI of validated genes from the network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLAVATA(CLV)–WUSHEL(WUS) signaling pathway in addition to gibberellin mediated auxin signaling in parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through PPI network.

scholarly journals Identification of Key miRNAs in the Treatment of Dabrafenib-Resistant Melanoma

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Guangyu Gao ◽  
Zhen Yao ◽  
Jiaofeng Shen ◽  
Yulong Liu
Keyword(s):  
Drug Resistance ◽  
Molecular Mechanism ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Obvious Association

Dabrafenib resistance is a significant problem in melanoma, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism of drug resistance of dabrafenib and to explore the key genes and pathways that mediate drug resistance in melanoma. GSE117666 was downloaded from the Gene Expression Omnibus (GEO) database and 492 melanoma statistics were also downloaded from The Cancer Genome Atlas (TCGA) database. Besides, differentially expressed miRNAs (DEMs) were identified by taking advantage of the R software and GEO2R. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) and FunRich was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify potential pathways and functional annotations linked with melanoma chemoresistance. 9 DEMs and 872 mRNAs were selected after filtering. Then, target genes were uploaded to Metascape to construct protein-protein interaction (PPI) network. Also, 6 hub mRNAs were screened after performing the PPI network. Furthermore, a total of 4 out of 9 miRNAs had an obvious association with the survival rate ( P < 0.05 ) and showed a good power of risk prediction model of over survival. The present research may provide a deeper understanding of regulatory genes of dabrafenib resistance in melanoma.

scholarly journals Prognostic values and prospective pathway signaling of MicroRNA-182 in ovarian cancer: a study based on gene expression omnibus (GEO) and bioinformatics analysis

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Yaowei Li ◽  
Li Li
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Gynecological Cancer ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction

Abstract Background Ovarian carcinoma (OC) is a common cause of death among women with gynecological cancer. MicroRNAs (miRNAs) are believed to have vital roles in tumorigenesis of OC. Although miRNAs are broadly recognized in OC, the role of has-miR-182-5p (miR-182) in OC is still not fully elucidated. Methods We evaluated the significance of miR-182 expression in OC by using analysis of a public dataset from the Gene Expression Omnibus (GEO) database and a literature review. Furthermore, we downloaded three mRNA datasets of OC and normal ovarian tissues (NOTs), GSE14407, GSE18520 and GSE36668, from GEO to identify differentially expressed genes (DEGs). Then the targeted genes of hsa-miR-182-5p (TG_miRNA-182-5p) were predicted using miRWALK3.0. Subsequently, we analyzed the gene overlaps integrated between DEGs in OC and predicted target genes of miR-182 by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network and the prognostic effects of the hub genes were analyzed. Results A common pattern of up-regulation for miR-182 in OC was found in our review of the literature. A total of 268 DEGs, both OC-related and miR-182-related, were identified, of which 133 genes were discovered from the PPI network. A number of DEGs were enriched in extracellular matrix organization, pathways in cancer, focal adhesion, and ECM-receptor interaction. Two hub genes, MCM3 and GINS2, were significantly associated with worse overall survival of patients with OC. Furthermore, we identified covert miR-182-related genes that might participate in OC by network analysis, such as DCN, AKT3, and TIMP2. The expressions of these genes were all down-regulated and negatively correlated with miR-182 in OC. Conclusions Our study suggests that miR-182 is essential for the biological progression of OC.

scholarly journals Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses

2020 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Bioinformatics Analyses ◽  
Go Enrichment ◽  
Jakob Disease

AbstractSporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened. Pathway and GO enrichment analyses of DEGs were performed. Furthermore, the protein-protein interaction (PPI) network was predicted using the IntAct Molecular Interaction Database and visualized with Cytoscape software. In addition, hub genes and important modules were selected based on the network. Finally, we constructed target genes - miRNA regulatory network and target genes - TF regulatory network. Hub genes were validated. A total of 891 DEGs 448 of these DEGs presented significant up regulated, and the remaining 443 down regulated were obtained. Pathway enrichment analysis indicated that up regulated genes were mainly linked with glutamine degradation/glutamate biosynthesis, while the down regulated genes were involved in melatonin degradation. GO enrichment analyses indicated that up regulated genes were mainly linked with chemical synaptic transmission, while the down regulated genes were involved in regulation of immune system process. hub and target genes were selected from the PPI network, modules, and target genes - miRNA regulatory network and target genes - TF regulatory network namely YWHAZ, GABARAPL1, EZR, CEBPA, HSPB8, TUBB2A and CDK14. The current study sheds light on the molecular mechanisms of sCJD and may provide molecular targets and diagnostic biomarkers for sCJD.

scholarly journals Identification of lncRNA-mRNA Regulatory Module to Explore the Pathogenesis and Prognosis of Melanoma

2020 ◽  
Vol 8 ◽  
Author(s):  
Jiaqi Zhang ◽  
Hui Liu ◽  
Wenhao Zhang ◽  
Yinfang Li ◽  
Zhigang Fan ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Target Genes ◽  
Normal Skin ◽  
Regulation Of Gene Expression ◽  
Growth Factor Receptor ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Non Coding Rna ◽  
Regulatory Module

Skin cutaneous melanoma (SKCM) is an aggressive form of skin cancer that results in high mortality rate worldwide. It is vital to discover effective prognostic biomarkers and therapeutic targets for the treatment of melanoma. Long non-coding RNA (lncRNA) has been verified to play an essential role in the regulation of gene expression in diseases and tumors. Therefore, it is significant to explore the function of lncRNAs in the development and progression of SKCM. In this paper, a set of differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were first screened out using 471 cutaneous melanoma samples and 813 normal skin samples. Gene Ontology and KEGG pathway enrichment analysis were performed to obtain the significant function annotations and pathways of DEmRNAs. We also ran survival analysis on both DElncRNAs and DEmRNAs to identify prognostic-related lncRNAs and mRNAs. Next, a set of hub genes derived from protein-protein interaction (PPI) network analysis and lncRNA target genes screened from starbase-ENCORI database were integrated to construct a lncRNA-mRNA regulatory module, which includes 6 lncRNAs 4 target mRNAs. We further checked the capacity of these lncRNA and mRNA in the diagnosis of melanoma, and found that single lncRNA can effectively distinguish tumor and normal tissue. Moreover, we ran CMap analysis to select a list of small molecule drugs for SKCM, such as EGFR inhibitor AG-490, growth factor receptor inhibitor GW-441756 and apoptosis stimulant betulinic-acid, which have shown therapeutic effect in the treatment of melanoma.

scholarly journals CCDC114, DNAI2 and TOP2A involves in the effects of tibolone treatment on postmenopausal endometrium

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yanhua Lv ◽  
Yanqing Liu ◽  
Yueqiang Wang ◽  
Fanrong Kong ◽  
Qiuxiang Pang ◽  
...  
Keyword(s):  
Postmenopausal Women ◽  
Molecular Mechanisms ◽  
Ion Homeostasis ◽  
Enrichment Analysis ◽  
Control Group ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Target Interaction ◽  
Protein Protein Interaction ◽  
Movement Function

Abstract Background This study aimed to explore the molecular mechanisms of tibolone treatment in postmenopausal women. Methods The gene set enrichment profile, GSE12446, which includes 9 human endometrial samples from postmenopausal women treated with tibolone (tibolone group) and 9 control samples (control group), was downloaded from GEO database for analysis. Differentially expressed genes (DEGs) in tibolone vs. control groups were identified and then used for function and pathway enrichment analysis. Protein–protein interaction (PPI) network and module analyses were also performed. Finally, drug–target interaction was predicted for genes in modules, and then were validated in Pubmed. Results A total of 238 up-regulated DEGs and 72 down-regulated DEGs were identified. These DEGs were mainly enriched in various biological processed and pathways, such as cilium movement (e.g., CCDC114 and DNAI2), calcium ion homeostasis, regulation of hormone levels and complement/coagulation cascades. PPI network contained 368 interactions and 166 genes, of which IGF1, DNALI1, CCDC114, TOP2A, DNAH5 and DNAI2 were the hue genes. A total of 96 drug–gene interactions were obtained, including 94 drugs and eight genes. TOP2A and HTR2B were found to be targets of 28 drugs and 38 drugs, respectively. Among the 94 obtained drugs, only 12 drugs were reported in studies, of which 7 drugs (e.g., epirubicin) were found to target TOP2A. Conclusions CCDC114 and DNAI2 might play important roles in tibolone-treated postmenopausal women via cilium movement function. TOP2A might be a crucial target of tibolone in endometrium of postmenopausal women.

scholarly journals A Network Pharmacology Approach to Uncover the Potential Mechanism of Yinchensini Decoction

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Guoming Chen ◽  
Chuyao Huang ◽  
Yunyun Liu ◽  
Tengyu Chen ◽  
Ruilan Huang ◽  
...  
Keyword(s):  
Rna Polymerase Ii ◽  
Enrichment Analysis ◽  
Drug Binding ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Regulation Of Transcription ◽  
Potential Mechanism ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
System Pharmacology

Objective. To predict and explore the potential mechanism of Yinchensini decoction (YCSND) based on systemic pharmacology. Method. TCMSP database was searched for the active constituents and related target proteins of YCSND. Cytoscape 3.5.1 was used to construct the active ingredient-target interaction of YCSND and network topology analysis, with STRING online database for protein-protein interaction (PPI) network construction and analysis; and collection from the UniProt database of target protein gene name, with the DAVID database for the gene ontology (GO) functional analysis, KEGG pathway enrichment analysis mechanism and targets of YCSND. Results. The results indicate the core compounds of YCSND, namely, kaempferol, 7-Methoxy-2-methyl isoflavone, and formononetin. And its core targets are prostaglandin G/H synthase 2, estrogen receptor, Calmodulin, heat shock protein HSP 90, etc. PPI network analysis shows that the key components of the active ingredients of YCSND are JUN, TP53, MARK1, RELA, MYC, and so on. The results of the GO analysis demonstrate that extracellular space, cytosol, and plasma membrane are the main cellular components of YCSND. Its molecular functions are mainly acting on enzyme binding, protein heterodimerization activity, and drug binding. The biological process of YCSND is focused on response to drug, positive regulation of transcription from RNA polymerase II promoter, the response to ethanol, etc. KEGG results suggest that the pathways, including pathways in cancer, hepatitis B, and pancreatic cancer, play a key role in YCSND. Conclusion. YCSND exerts its drug effect through various signaling pathways and acts on kinds of targets. By system pharmacology, the potential role of drugs and the mechanism of action can be well predicted.

scholarly journals Network Pharmacology-Based Study on the Mechanism of Gegen Qinlian Decoction against Colorectal Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Qiaowei Fan ◽  
Lin Guo ◽  
Jingming Guan ◽  
Jing Chen ◽  
Yujing Fan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Target Genes ◽  
Enrichment Analysis ◽  
R Package ◽  
Gastrointestinal Diseases ◽  
Gene Expression Omnibus ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Ppi Network

Purpose. Gegen Qinlian decoction (GQD) has been used to treat gastrointestinal diseases, such as diarrhea and ulcerative colitis (UC). A recent study demonstrated that GQD enhanced the effect of PD-1 blockade in colorectal cancer (CRC). This study used network pharmacology analysis to investigate the mechanisms of GQD as a potential therapeutic approach against CRC. Materials and Methods. Bioactive chemical ingredients (BCIs) of GQD were collected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. CRC-specific genes were obtained using the gene expression profile GSE110224 from the Gene Expression Omnibus (GEO) database. Target genes related to BCIs of GQD were then screened out. The GQD-CRC ingredient-target pharmacology network was constructed and visualized using Cytoscape software. A protein-protein interaction (PPI) network was subsequently constructed and analyzed with BisoGenet and CytoNCA plug-in in Cytoscape. Gene Ontology (GO) functional and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis for target genes were then performed using the R package of clusterProfiler. Results. One hundred and eighteen BCIs were determined to be effective on CRC, including quercetin, wogonin, and baicalein. Twenty corresponding target genes were screened out including PTGS2, CCNB1, and SPP1. Among these genes, CCNB1 and SPP1 were identified as crucial to the PPI network. A total of 212 GO terms and 6 KEGG pathways were enriched for target genes. Functional analysis indicated that these targets were closely related to pathophysiological processes and pathways such as biosynthetic and metabolic processes of prostaglandins and prostanoids, cytokine and chemokine activities, and the IL-17, TNF, Toll-like receptor, and nuclear factor-kappa B (NF-κB) signaling pathways. Conclusion. The study elucidated the “multiingredient, multitarget, and multipathway” mechanisms of GQD against CRC from a systemic perspective, indicating GQD to be a candidate therapy for CRC treatment.

scholarly journals Network Pharmacological Analysis of Huanglian Jiedu Decoction for Anti- Atherosclerosis

2020 ◽  
Author(s):  
Kerui Wu ◽  
Lu Jiang ◽  
Lanlin Huang ◽  
Yaxing He ◽  
Xia Yan ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Signaling Pathway ◽  
Fluid Shear Stress ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Active Ingredients ◽  
Active Components ◽  
Protein Protein Interaction ◽  
Target Regulation

Abstract Objective: We aimed to predict the possible active components,key targets and pathways of Huanglian Jiedu Decoction(HLJDD) for anti-atherosclerosis. Methods: The TCMSP database was searched to obtain the active components and targets of HLJDD, the GeneCards and OMIM databases were searched to obtain related targets of atherosclerosis, and we obtain the intersection targets of them, which are the potential targets of HLJDD for anti-atherosclerosis.Application of Cytoscape 3.6.0 software to build a herbal-active ingredient-potential target regulation network.We perform protein-protein interaction(PPI) network analysis of potential targets through STRING 11.0 database and obtain the key targets,and the results of PPI network of key targets were visualized by Cytoscape3.6.0 software. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the key targets were performed using STRING11.0 database, and we finally constructed the possible pharmacological network of HLJDD for anti-atherosclerosis .Results: We finally obtained 14 key active ingredients of HLJDD, 65 key targets of anti-atherosclerosis, and 14 key active ingredients corresponded to 52 of these targets. These targets are mainly involved in biological processes such as reaction to organic substance, reaction to chemical stimulation,etc.They mainly involved in biological signaling pathways such as pathways in cancer,IL-17 signaling pathway,etc. Conclusion: HLJDD may act on 52 key targets such as PTGS2, HSP90AA1 and RELA through 14 key active ingredients, and influence the signaling pathways including fluid shear stress and atherosclerosis,PI3K-Akt signaling pathway,IL-17 signaling pathway,AGE-RAGE signaling pathway in diabetic complications,TNF signaling pathway,etc.Thus, it may play an anti-atherosclerosis role by inhibiting inflammatory reaction, oxidative stress and improving hemodynamics,etc.

scholarly journals Differentially expressed genes SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma

2020 ◽  
Author(s):  
Sheng Chang ◽  
Yang Cao
Keyword(s):  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Osteogenic Sarcoma ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Potential Biomarkers

Abstract Background: Osteosarcoma (osteogenic sarcoma, OS) is a primary cause of morbidity and mortality and is associated with poor prognosis in the field of orthopedic. Globally, rates of OS are highest among 15 to 25-year-old adolescent. However, the mechanism of gene regulation and signaling pathway is unknown. Material and Methods: GSE9508, including 34 OS samples and 5 non-malignant bone samples, was gained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were picked out by GEO2R online R soft tool. Furthermore, the protein-protein interaction (PPI) network between the DEGs was molded utilizing STRING online software. Afterward, PPI network of DEGs was constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out on DAVID online tool and visualized via cytoscape software. Subsequently, module analysis of PPI was performed by using MCODE app. What’s more, prognosis-related genes were screened by using online databases including GEPIA, UALCAN and cBioPortal databases. Results: Totally, 671 DEGs were picked out, including 501 up-regulated genes and 170 down-regulated genes. Moreover, 22 hub genes were identified to be significantly expressed in PPI network (16 up-regulated and 6 down-regulated). We found that spliceosome signaling pathway may provide a potential target in OS. Furthermore, on the basis of common crucial pathway, PRPF38A and SNRPC were closely associated with spliceosome. Conclusion: This study showed that SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma.

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