scholarly journals Mining Featured Biomarkers Linked with Epithelial Ovarian Cancer Based on Bioinformatics

Diagnostics ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 39
Author(s):  
◽  
Chanabasayya Vastrad ◽  
◽  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Target Gene ◽  
Enrichment Analysis ◽  
Microrna Target ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
Go Enrichment ◽  
Gene Regulatory ◽  
Go Enrichment Analysis

: Epithelial ovarian cancer (EOC) is the18th most common cancer worldwide and the 8th most common in women. The aim of this study was to diagnose the potential importance of, as well as novel genes linked with, EOC and to provide valid biological information for further research. The gene expression profiles of E-MTAB-3706 which contained four high-grade ovarian epithelial cancer samples, four normal fallopian tube samples and four normal ovarian epithelium samples were downloaded from the ArrayExpress database. Pathway enrichment and Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) were performed, and protein-protein interaction (PPI) network, microRNA-target gene regulatory network and TFs (transcription factors ) -target gene regulatory network for up- and down-regulated were analyzed using Cytoscape. In total, 552 DEGs were found, including 276 up-regulated and 276 down-regulated DEGs. Pathway enrichment analysis demonstrated that most DEGs were significantly enriched in chemical carcinogenesis, urea cycle, cell adhesion molecules and creatine biosynthesis. GO enrichment analysis showed that most DEGs were significantly enriched in translation, nucleosome, extracellular matrix organization and extracellular matrix. From protein-protein interaction network (PPI) analysis, modules, microRNA-target gene regulatory network and TFs-target gene regulatory network for up- and down-regulated, and the top hub genes such as E2F4, SRPK2, A2M, CDH1, MAP1LC3A, UCHL1, HLA-C (major histocompatibility complex, class I, C) , VAT1, ECM1 and SNRPN (small nuclear ribonucleoprotein polypeptide N) were associated in pathogenesis of EOC. The high expression levels of the hub genes such as CEBPD (CCAAT enhancer binding protein delta) and MID2 in stages 3 and 4 were validated in the TCGA (The Cancer Genome Atlas) database. CEBPD andMID2 were associated with the worst overall survival rates in EOC. In conclusion, the current study diagnosed DEGs between normal and EOC samples, which could improve our understanding of the molecular mechanisms in the progression of EOC. These new key biomarkers might be used as therapeutic targets for EOC.

Deciphering the transcription factor-microRNA-target gene regulatory network associated with graphene oxide cytotoxicity

2018 ◽  
Vol 12 (9) ◽  
pp. 1014-1026 ◽  
Author(s):  
Masoumeh Farahani ◽  
Mostafa Rezaei–Tavirani ◽  
Hakimeh Zali ◽  
Afsaneh Arefi Oskouie ◽  
Meisam Omidi ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Graphene Oxide ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Target Gene ◽  
Microrna Target ◽  
Gene Regulatory

Transcriptome Signatures Reveal Candidate Key Genes in the Peripheral Blood Mononuclear Cells of Patients With Coronary Artery Disease

2020 ◽  
Author(s):  
Vijayakrishna Kolur ◽  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti ◽  
Anandkumar Tengli
Keyword(s):  
Coronary Artery Disease ◽  
Gene Ontology ◽  
Coronary Artery ◽  
Regulatory Network ◽  
Target Gene ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Go Enrichment ◽  
Artery Disease ◽  
Go Enrichment Analysis

Abstract BackgroundCoronary artery disease (CAD) is one of the most common disorders in the cardiovascular system. This study aims to explore potential signaling pathways and important biomarkers that drive CAD development. MethodsThe CAD GEO Dataset GSE113079 was featured to screen differentially expressed genes (DEGs). The pathway and Gene Ontology (GO) enrichment analysis of DEGs were analyzed using the ToppGene. We screened hub and target genes from protein-protein interaction (PPI) networks, target gene - miRNA regulatory network and target gene - TF regulatory network, and Cytoscape software. Validations of hub genes were performed to evaluate their potential prognostic and diagnostic value for CAD. Results1,036 DEGs were captured according to screening criteria (525upregulated genes and 511downregulated genes). Pathway and Gene Ontology (GO) enrichment analysis of DEGs revealed that these up and down regulated genes are mainly enriched in thyronamine and iodothyronamine metabolism, cytokine-cytokine receptor interaction, nervous system process, cell cycle and nuclear membrane. Hub genes were validated to find out potential prognostic biomarkers, diagnostic biomarkers and novel therapeutic target for CAD. ConclusionsIn summary, our findings discovered pivotal gene expression signatures and signaling pathways in the progression of CAD. CAPN13, ACTBL2, ERBB3, GATA4, GNB4, NOTCH2, EXOSC10, RNF2, PSMA1 and PRKAA1 might contribute to the progression of CAD, which could have potential as biomarkers or therapeutic targets for CAD.

scholarly journals Transcriptome signatures reveal candidate key genes in the peripheral blood mononuclear cells of patients with coronary artery disease and prediction of small drug molecules

2020 ◽  
Author(s):  
Vijayakrishna Kolur ◽  
Basavaraj Vastrad ◽  
Anandkumar Tengli ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Coronary Artery Disease ◽  
Gene Ontology ◽  
Coronary Artery ◽  
Regulatory Network ◽  
Target Gene ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Go Enrichment ◽  
Artery Disease ◽  
Go Enrichment Analysis

Abstract Coronary artery disease (CAD) is one of the most common disorders in the cardiovascular system. This study aims to explore potential signaling pathways and important biomarkers that drive CAD development. The CAD GEO Dataset GSE113079 was featured to screen differentially expressed genes (DEGs). The pathway and Gene Ontology (GO) enrichment analysis of DEGs were analyzed using the ToppGene. We screened hub and target genes from protein-protein interaction (PPI) networks, target gene - miRNA regulatory network and target gene - TF regulatory network, and Cytoscape software. Validations of hub genes were performed to evaluate their potential prognostic and diagnostic value for CAD. A final, molecular docking study was performed. 1,036 DEGs were captured according to screening criteria (525upregulated genes and 511downregulated genes). Pathway and Gene Ontology (GO) enrichment analysis of DEGs revealed that these up and down regulated genes are mainly enriched in thyronamine and iodothyronamine metabolism, cytokine-cytokine receptor interaction, nervous system process, cell cycle and nuclear membrane. Hub genes were validated to find out potential prognostic biomarkers, diagnostic biomarkers and novel therapeutic target for CAD. A small drug molecule was predicted. In summary, our findings discovered pivotal gene expression signatures and signaling pathways in the progression of CAD. CAPN13, ACTBL2, ERBB3, GATA4, GNB4, NOTCH2, EXOSC10, RNF2, PSMA1 and PRKAA1 might contribute to the progression of CAD, which could have potential as biomarkers or therapeutic targets for CAD.

scholarly journals Identification of potential core genes in hepatoblastoma via bioinformatics analysis

2020 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Gene Ontology ◽  
Regulatory Network ◽  
Target Gene ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Metabolic Process ◽  
Chromosome Organization ◽  
Hub Genes ◽  
Go Enrichment ◽  
Go Enrichment Analysis

AbstractHepatoblastoma is the childhood liver cancer. Profound efforts have been made to illuminate the pathology, but the molecular mechanisms of hepatoblastoma are still not well understood. To identify the candidate genes in the carcinogenesis and progression of hepatoblastoma, microarray dataset GSE131329 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and pathway and Gene Ontology (GO) enrichment analysis were performed. The protein-protein interaction network (PPI), module analysis, target gene - miRNA regulatory network and target gene - TF regulatory network were constructed and analyzed. A total of 996 DEGs were identified, consisting of 499 up regulated genes and 497 down regulated genes. The pathway and Gene Ontology (GO) enrichment analysis of the DEGs include proline biosynthesis, superpathway of tryptophan utilization, chromosome organization and organic acid metabolic process. Twenty-four hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell cycle, chromosome organization, lipid metabolic process and oxidation-reduction process. Validation of hub genes showed that TP53, PLK1, AURKA, CDK1, ANLN, ESR1, FGB, ACAT1, GOT1 and ALAS1 may be involved in the carcinogenesis, invasion or recurrence of hepatoblastoma. In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the carcinogenesis and progression of hepatoblastoma, and provide candidate targets for diagnosis and treatment of hepatoblastoma.

scholarly journals Identification and Interaction Analysis of Molecular Markers in Myocardial Infarction by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Hub Gene ◽  
Pathway Enrichment ◽  
Network Modules ◽  
Gene Regulatory

AbstractMyocardial infarction (MI) is the leading cardiovascular diseases in worldwide, yet relatively little is known about the genes and signaling pathways involved in MI progression. The present investigation aimed to elucidate potential crucial candidate genes and pathways in MI. expression profiling by high throughput sequencing dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 20 MI samples and 12 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the DESeq2 R package. These DEGs were subsequently investigated by Gene Ontology (GO) and pathway enrichment analysis, a protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed and analyzed. Hub genes were validated by receiver operating characteristic curve (ROC) analysis. In total, 958 DEGs were identified, of which 480 were up regulated and 478 were down regulated. GO and pathway enrichment analysis results revealed that the DEGs were mainly enriched in, immune system, neuronal system, response to stimulus, and multicellular organismal process. A PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network was constructed by using Cytoscape software, and CFTR, CDK1, RPS13, RPS15A, RPS27, NOTCH1, MRPL12, NOS2, CCDC85B and ATN1 were identified as the hub genes. Our results highlight the important roles of the genes including CFTR, CDK1, RPS13, RPS15A, RPS27, NOTCH1, MRPL12, NOS2, CCDC85B and ATN1 in MI pathogenesis or therapeutic management.

scholarly journals Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in severe acute respiratory syndrome corona virus 2 infection/COVID 19

2020 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Regulatory Network ◽  
Target Gene ◽  
Enrichment Analysis ◽  
Molecular Pathogenesis ◽  
Hub Genes ◽  
Corona Virus ◽  
Go Enrichment ◽  
Diagnostic Values ◽  
Go Enrichment Analysis

Abstract Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infections (COVID 19) is a progressive viral infection that has been investigated extensively. However, genetic features and molecular pathogenesis underlying SARS-CoV-2 infection remain unclear. Here we used bioinformatics to investigate the candidate genes associated in the molecular pathogenesis of SARS-CoV-2 infection. Expression profiling by high throughput sequencing (GSE149273) was downloaded from the Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) in remdesivir traded SARS-CoV-2 infection samples and non treated SARS-CoV-2 infection samples with an adjusted P-value < 0.05 and a |log fold change (FC)| > 1.3 were first identified by limma in R software package. Next, Pathway and Gene Ontology (GO) enrichment analysis of these DEGs was performed. Then, the hub genes were identified by the Network Analyzer plugin and the other bioinformatics approaches including protein-protein interaction (PPI) network analysis, module analysis, target gene - miRNA regulatory network, and target gene - TF regulatory network construction was also performed. Finally, receiver‐operating characteristic (ROC) analyses were for diagnostic values associated with hub genes. A total of 909 DEGs were identified, including 453 up regulated genes and 457 down regulated genes. As for the pathway and GO enrichment analysis, the up regulated genes were mainly linked with influenza A and defense response, whereas down regulated genes were mainly linked with Drug metabolism - cytochrome P450 and reproductive process. Additionally, 10 hub genes (VCAM1, IKBKE, STAT1, IL7R, ISG15, E2F1, ZBTB16, TFAP4, ATP6V1B1 and APBB1) were identified. ROC analysis showed that hub genes (CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5 and PDE1A) had good diagnostic values. In summary, the data may produce new insights regarding pathogenesis of SARS-CoV-2 infection and treatment. Hub genes and candidate drugs may improve individualized diagnosis and therapy for SARS-CoV-2 infection in future.

scholarly journals HSCVFNT: Inference of Time-Delayed Gene Regulatory Network Based on Complex-Valued Flexible Neural Tree Model

2018 ◽  
Vol 19 (10) ◽  
pp. 3178 ◽  
Author(s):  
Bin Yang ◽  
Yuehui Chen ◽  
Wei Zhang ◽  
Jiaguo Lv ◽  
Wenzheng Bao ◽  
...  
Keyword(s):  
Time Series ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Target Gene ◽  
Repair System ◽  
Tree Model ◽  
Scoring Method ◽  
Maximum Information ◽  
Gene Regulatory ◽  
Complex Valued

Gene regulatory network (GRN) inference can understand the growth and development of animals and plants, and reveal the mystery of biology. Many computational approaches have been proposed to infer GRN. However, these inference approaches have hardly met the need of modeling, and the reducing redundancy methods based on individual information theory method have bad universality and stability. To overcome the limitations and shortcomings, this thesis proposes a novel algorithm, named HSCVFNT, to infer gene regulatory network with time-delayed regulations by utilizing a hybrid scoring method and complex-valued flexible neural network (CVFNT). The regulations of each target gene can be obtained by iteratively performing HSCVFNT. For each target gene, the HSCVFNT algorithm utilizes a novel scoring method based on time-delayed mutual information (TDMI), time-delayed maximum information coefficient (TDMIC) and time-delayed correlation coefficient (TDCC), to reduce the redundancy of regulatory relationships and obtain the candidate regulatory factor set. Then, the TDCC method is utilized to create time-delayed gene expression time-series matrix. Finally, a complex-valued flexible neural tree model is proposed to infer the time-delayed regulations of each target gene with the time-delayed time-series matrix. Three real time-series expression datasets from (Save Our Soul) SOS DNA repair system in E. coli and Saccharomyces cerevisiae are utilized to evaluate the performance of the HSCVFNT algorithm. As a result, HSCVFNT obtains outstanding F-scores of 0.923, 0.8 and 0.625 for SOS network and (In vivo Reverse-Engineering and Modeling Assessment) IRMA network inference, respectively, which are 5.5%, 14.3% and 72.2% higher than the best performance of other state-of-the-art GRN inference methods and time-delayed methods.

scholarly journals Identification of biomarkers, pathways and potential therapeutic targets for heart failure using bioinformatics analysis

2021 ◽  
Author(s):  
Basavaraj Mallikarjunayya Vastrad ◽  
Chanabasayya Mallikarjunayya Vastrad
Keyword(s):  
Heart Failure ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Characteristic Curve ◽  
Topological Analysis ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Regulatory

Heart failure (HF) is a complex cardiovascular diseases associated with high mortality. To discover key molecular changes in HF, we analyzed next-generation sequencing (NGS) data of HF. In this investigation, differentially expressed genes (DEGs) were analyzed using limma in R package from GSE161472 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network, miRNA-hub gene regulatory network and TF-hub gene regulatory network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), REACTOME pathway, STRING, HiPPIE, miRNet, NetworkAnalyst and Cytoscape. Finally, we performed receiver operating characteristic curve (ROC) analysis of hub genes. A total of 930 DEGs 9464 up regulated genes and 466 down regulated genes) were identified in HF. GO and REACTOME pathway enrichment results showed that DEGs mainly enriched in localization, small molecule metabolic process, SARS-CoV infections and the citric acid (TCA) cycle and respiratory electron transport. Subsequently, the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed, and 10 hub genes in these network were focused on by centrality analysis and module analysis. Furthermore, data showed that HSP90AA1, ARRB2, MYH9, HSP90AB1, FLNA, EGFR, PIK3R1, CUL4A, YEATS4 and KAT2B were good diagnostic values. In summary, this study suggests that HSP90AA1, ARRB2, MYH9, HSP90AB1, FLNA, EGFR, PIK3R1, CUL4A, YEATS4 and KAT2B may act as the key genes in HF.

scholarly journals Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

2021 ◽  
Author(s):  
Basavaraj Mallikarjunayya Vastrad ◽  
Chanabasayya Mallikarjunayya Vastrad
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Hub Gene ◽  
Network Modules ◽  
Gene Regulatory

Type 1 diabetes mellitus (T1DM) is a metabolic disorder for which the underlying molecular mechanisms remain largely unclear. This investigation aimed to elucidate essential candidate genes and pathways in T1DM by integrated bioinformatics analysis. In this study, differentially expressed genes (DEGs) were analyzed using DESeq2 of R package from GSE162689 of the Gene Expression Omnibus (GEO). Gene ontology (GO) enrichment analysis, REACTOME pathway enrichment analysis, and construction and analysis of protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, and validation of hub genes were then performed. A total of 952 DEGs (477 up regulated and 475 down regulated genes) were identified in T1DM. GO and REACTOME enrichment result results showed that DEGs mainly enriched in multicellular organism development, detection of stimulus, diseases of signal transduction by growth factor receptors and second messengers, and olfactory signaling pathway. The top hub genes such as MYC, EGFR, LNX1, YBX1, HSP90AA1, ESR1, FN1, TK1, ANLN and SMAD9 were screened out as the critical genes among the DEGs from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Receiver operating characteristic curve (ROC) analysis and RT-PCR confirmed that these genes were significantly associated with T1DM. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the advancement and progression of T1DM, and certain genes might be used as candidate target molecules to diagnose, monitor and treat T1DM.

scholarly journals Exploring the Oxidative Stress Mechanism of Buyang Huanwu Decoction in Intervention of Vascular Dementia Based on Systems Biology Strategy

2021 ◽  
Vol 2021 ◽  
pp. 1-29
Author(s):  
Kailin Yang ◽  
Liuting Zeng ◽  
Anqi Ge ◽  
Yaqiao Yi ◽  
Shanshan Wang ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Molecular Docking ◽  
Systems Biology ◽  
Vascular Dementia ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Buyang Huanwu Decoction ◽  
Pathway Enrichment ◽  
Go Enrichment ◽  
Go Enrichment Analysis

Objective. To explore the oxidative stress mechanism of modified Buyang Huanwu decoction (MBHD) in intervention of vascular dementia (VD) based on systems biology strategy. Methods. In this study, through the reverse virtual target prediction technology and transcriptomics integration strategy, the active ingredients and potential targets of MBHD treatment of VD were analyzed, and the drug-disease protein-protein interaction (PPI) network was constructed. Then, bioinformatics analysis methods are used for Gene Ontology (GO) enrichment analysis and pathway enrichment analysis, and finally find the core biological process. After that, in animal models, low-throughput technology is used to detect gene expression and protein expression of key molecular targets in oxidative stress-mediated inflammation and apoptosis signaling pathways to verify the mechanism of MBHD treatment of VD rats. Finally, the potential interaction relationship between MBHD and VD-related molecules is further explored through molecular docking technology. Results. There are a total of 54 MBHD components, 252 potential targets, and 360 VD genes. The results of GO enrichment analysis and pathway enrichment analysis showed that MBHD may regulate neuronal apoptosis, nitric oxide synthesis and metabolism, platelet activation, NF-κB signaling pathway-mediated inflammation, oxidative stress, angiogenesis, etc. Among them, SIRT1, NF-κB, BAX, BCL-2, CASP3, and APP may be important targets for MBHD to treat VD. Low-throughput technology (qRT-PCR/WB/immunohistochemical technology) detects oxidative stress-mediated inflammation and apoptosis-related signaling pathway molecules. The molecular docking results showed that 64474-51-7, cycloartenol, ferulic acid, formononetin, kaempferol, liquiritigenin, senkyunone, wallichilide, xanthinin, and other molecules can directly interact with NF-κB p65, BAX, BCL-2, and CASP3. Conclusion. The active compounds of MBHD interact with multiple targets and multiple pathways in a synergistic manner, and have important therapeutic effects on VD mainly by balancing oxidative stress/anti-inflammatory and antiapoptotic, enhancing metabolism, and enhancing the immune system.

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