scholarly journals Bioinformatics analysis of expression profiling by high throughput sequencing for identification of potential key genes among SARS-CoV-2/COVID 19

2021 ◽  
Author(s):  
Basavaraj Mallikarjunayya Vastrad ◽  
Chanabasayya Mallikarjunayya Vastrad
Keyword(s):  
High Throughput ◽  
Expression Profiling ◽  
Regulatory Network ◽  
Target Gene ◽  
Molecular Mechanisms ◽  
High Throughput Sequencing ◽  
Enrichment Analysis ◽  
Current Investigation ◽  
Hub Genes ◽  
Key Genes

Abstract Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) is pandemic recently emerged and is rapidly spreading in humans. However, the precise molecular mechanisms of the advancement and progression of SARS-CoV-2 infection remain unclear. The current investigation attempted to identify and functionally analyze the differentially expressed genes (DEGs) between SARS-CoV-2 infection and mock by using comprehensive bioinformatics analyses. The GSE148729 expression profiling by high throughput sequencing was downloaded from the Gene Expression Omnibus (GEO) and analyzed using the limma package in R software to identify DEGs. Pathway and gene ontology (GO) enrichment analysis of the up and down regulated genes were performed in ToppGene. The HIPPIE database was used to evaluate the interactions of up and down regulated genes and to construct a protein-protein interaction (PPI) network using Cytoscape software. Hub genes were selected using the Network Analyzer plugin. Subsequently, extensive target prediction and network analyses methods were used to assess, target gene - miRNA regulatory network and target gene - TF regulatory network. Receiver operating characteristic (ROC) analysis was utilized for validation. A total of 928 DEGs (461 up regulated genes and 467 down regulated genes) were identified between SARS-CoV-2 infection and mock samples. The Pathway enrichment analysis results showed that these up and down regulated genes were significantly enriched in cytokine-cytokine receptor interaction, and ascorbate and aldarate metabolism. Several significant GO terms, including the response to biotic stimulus and oxoacid metabolic process, were identified as being closely associated with these up and down regulated genes. The top hub genes and target genes were screened and included JUN, FBXO6, PCLAF, CFTR, TXNIP, PMAIP1, BRI3BP, FAHD1, PROX1, CXCL11, SERHL2 and CFI. ROC curve analysis showed that messenger RNA levels of these ten genes (DDX58, IFITM2, IRF1, PML, SAMHD1, ACSS1, CYP2U1, DDC, PNMT and UGT2A3) exhibited better diagnostic efficiency for SARS-CoV-2 infection and mock. The current investigation identified a series of key genes and pathways that may be involved in the progression of SARS-CoV-2 infection, providing a new understanding of the underlying molecular mechanisms of SARS-CoV-2 infection.

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 Screen Key Genes Associated with Distraction-Induced Osteogenesis of Stem Cells Using Bioinformatics Methods

2021 ◽  
Vol 22 (12) ◽  
pp. 6505
Author(s):  
Jishizhan Chen ◽  
Jia Hua ◽  
Wenhui Song
Keyword(s):  
Stem Cells ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Negative Control ◽  
Gene Set Enrichment Analysis ◽  
Venn Diagram ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Two Samples ◽  
Key Genes

Applying mesenchymal stem cells (MSCs), together with the distraction osteogenesis (DO) process, displayed enhanced bone quality and shorter treatment periods. The DO guides the differentiation of MSCs by providing mechanical clues. However, the underlying key genes and pathways are largely unknown. The aim of this study was to screen and identify hub genes involved in distraction-induced osteogenesis of MSCs and potential molecular mechanisms. Material and Methods: The datasets were downloaded from the ArrayExpress database. Three samples of negative control and two samples subjected to 5% cyclic sinusoidal distraction at 0.25 Hz for 6 h were selected for screening differentially expressed genes (DEGs) and then analysed via bioinformatics methods. The Gene Ontology (GO) terms and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment were investigated. The protein–protein interaction (PPI) network was visualised through the Cytoscape software. Gene set enrichment analysis (GSEA) was conducted to verify the enrichment of a self-defined osteogenic gene sets collection and identify osteogenic hub genes. Results: Three hub genes (IL6, MMP2, and EP300) that were highly associated with distraction-induced osteogenesis of MSCs were identified via the Venn diagram. These hub genes could provide a new understanding of distraction-induced osteogenic differentiation of MSCs and serve as potential gene targets for optimising DO via targeted therapies.

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 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 Screening a prognosis-related target gene in patients with HER-2 positive breast cancer by bioinformatics analysis

2021 ◽  
Author(s):  
Song Wang ◽  
Yi Quan
Keyword(s):  
Breast Cancer ◽  
Target Gene ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Study Objective ◽  
Her 2 ◽  
Positive Breast Cancer

Objective: HER-2 positive breast cancer has a high risk of for relapse, metastasis and drug resistance, and is related to a poor prognosis. Thus, the study objective was to determine a target gene and explore the associated molecular mechanisms in HER-2 positive breast cancer. Methods: Three RNA expression profiles were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), and were used to identify differentially expressed genes (DEGs) using R software. A Protein-Protein Interaction (PPI) network was constructed and hub genes were determined. Subsequently, the relationship between clinical parameters and hub genes was examined to screen target gene. Next, DNA methylation and genomic alterations of the target gene were evaluated. To further explore potential molecular mechanisms, genes co-expressed with the target gene were performed functional enrichment analysis Results: The differential expression analysis revealed 217 DEGs in HER-2 positive breast cancer tissues compared to normal breast tissues. RRM2 was the only hub gene closely associated with lymphatic metastasis and prognosis in HER-2 positive breast cancer. Additionally, RRM2 was frequently often amplified and negatively associated with the methylation level. Functional enrichment analysis showed that the co-expression genes were mainly involved in cell cycle. Conclusions: The present study identified RRM2 as a target gene associated with the initiation, progression and prognosis of HER-2 positive breast cancer, which may contribute to provide a new biomarker and therapeutic target.

scholarly journals Identification of hub genes related to the progression of type 1 diabetes by computational analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
G. Prashanth ◽  
Basavaraj Vastrad ◽  
Anandkumar Tengli ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Immune System ◽  
Regulatory Network ◽  
Innate Immune System ◽  
Target Gene ◽  
Molecular Mechanisms ◽  
Innate Immune ◽  
Pcr Analysis ◽  
Rt Pcr ◽  
Hub Genes

Abstract Background Type 1 diabetes (T1D) is a serious threat to childhood life and has fairly complicated pathogenesis. Profound attempts have been made to enlighten the pathogenesis, but the molecular mechanisms of T1D are still not well known. Methods To identify the candidate genes in the progression of T1D, expression profiling by high throughput sequencing dataset GSE123658 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and gene ontology (GO) and pathway enrichment analyses were performed. The protein-protein interaction network (PPI), modules, target gene - miRNA regulatory network and target gene - TF regulatory network analysis were constructed and analyzed using HIPPIE, miRNet, NetworkAnalyst and Cytoscape. Finally, validation of hub genes was conducted by using ROC (Receiver operating characteristic) curve and RT-PCR analysis. A molecular docking study was performed. Results A total of 284 DEGs were identified, consisting of 142 up regulated genes and 142 down regulated genes. The gene ontology (GO) and pathways of the DEGs include cell-cell signaling, vesicle fusion, plasma membrane, signaling receptor activity, lipid binding, signaling by GPCR and innate immune system. Four hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell-cell signaling, cytokine signaling in immune system, signaling by GPCR and innate immune system. ROC curve and RT-PCR analysis showed that EGFR, GRIN2B, GJA1, CAP2, MIF, POLR2A, PRKACA, GABARAP, TLN1 and PXN might be involved in the advancement of T1D. Molecular docking studies showed high docking score. Conclusions DEGs and hub genes identified in the present investigation help us understand the molecular mechanisms underlying the advancement of T1D, and provide candidate targets for diagnosis and treatment of T1D.

scholarly journals Screening key genes and signaling pathways in COVID-19 infection and its associated complications by integrated bioinformatics analysis

2021 ◽  
Author(s):  
Basavaraj Mallikarjunayya Vastrad ◽  
Chanabasayya Mallikarjunayya Vastrad
Keyword(s):  
Immune System ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Regulatory ◽  
Key Genes

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2)/ coronavirus disease 2019 (COVID-19) infection is the leading cause of respiratory tract infection associated mortality worldwide. The aim of the current investigation was to identify the differentially expressed genes (DEGs) and enriched pathways in COVID-19 infection and its associated complications by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid next-generation sequencing (NGS) data of 93 COVID 19 samples and 100 non COVID 19 samples (GSE156063) were obtained from the Gene Expression Omnibus database. Gene ontology (GO) and REACTOME pathway enrichment analysis was conducted to identify the biological role of DEGs. In addition, a protein-protein interaction network, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network and receiver operating characteristic curve (ROC) analysis were used to identify the key genes. A total of 738 DEGs were identified, including 415 up regulated genes and 323 down regulated genes. Most of the DEGs were significantly enriched in immune system process, cell communication, immune system and signaling by NTRK1 (TRKA). Through PPI, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network analysis, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were selected as hub genes, which were expressed in COVID-19 samples relative to those in non COVID-19 samples, respectively. Among them, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were suggested to be diagonstic factors for COVID-19. The findings from this bioinformatics analysis study identified molecular mechanisms and the key hub genes that might contribute to COVID-19 infection and its associated complications.

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 Screening key genes related to ovarian cancer and exploring their possible molecular mechanisms

2019 ◽  
Author(s):  
Junzui Li ◽  
Yuehua Zhang ◽  
Zhixiong Huang ◽  
Bin Zhao ◽  
Ke Huang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Early Diagnosis ◽  
Candidate Genes ◽  
Molecular Mechanisms ◽  
Clinical Symptoms ◽  
Malignant Tumors ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Key Genes

Abstract Background As one of the common malignant tumors in women, ovarian cancer (OC) often exerts the atypically early clinical symptoms. Therefore, it is particularly important for seeking more effectively early diagnosis of OC (biomarkers). Besides, although a lot of sequencing and chip research have been done on the pathogenesis of OC, the pathogenesis, clinical and genetic features of OC is still not very clear.Methods In this study, 4 GEO data (GSE66957, GSE119054, GSE14407 and GSE54388) were selected for differential expression gene analysis (DEGs), and the important template of the 4 DEGS overlapping genes was taken as Hub genes. Then, the GO and pathway enrichment analysis were conducted to confirm the enrichment of these Hub genes, and these Hub genes were identified as key genes. In addition, the transcriptional levels of these Hub genes in OC and their impacts on the overall survival rate of OC were validated via the UCSC and TCGA datasets.. Besides, cBioPortal, TargetScan, UCSC, DiseaseMeth and TIMER software were performed to explore the potential biological functions of these key genes in OC.Results We screened out 10 Hub genes related to OC including VEGFA, ZWINT, CDKN2A, SLC2A1, TOP2A, MKI67, CCND1, KPNA2, FGF2 and SMC4, and further demonstrated that they were most significantly enriched in protein binding, cytoplasm, nucleus, extracellular exosome, membrane, cell division, cell adhesion and pathways in cancer. Meanwhile, CCND1, TOP2A, SMC4 and FGF2 were screened out as key candidate genes associated with OC. Further analysis proved these key candidate genes may regulate the occurrence and development of OC through mediating the gene mutation, miRNAs and genetic epigenetics such as methylation and acetylation.Conclusion These data would improve our understanding of the causes and underlying molecular events of OC, be of clinical significance for the early diagnosis and prevention of OC, and may provide the promising therapeutic targets in OC.

scholarly journals Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

2021 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Anandkumar Tengli ◽  
Chanabasayya Vastrad
Keyword(s):  
Heart Failure ◽  
Extracellular Matrix ◽  
Regulatory Network ◽  
Target Gene ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Pcr Analysis ◽  
Hub Genes ◽  
Ppi Networks ◽  
Neutrophil Degranulation

AbstractHeart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules. The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein-protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene - miRNA regulatory network and target gene - TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed. A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene - miRNA regulatory network and target gene - TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed. The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

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