scholarly journals Identification of Key Genes Potentially Related to Triple Receptor Negative Breast Cancer by Microarray Analysis

2020 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cell Cycle ◽  
Immune Response ◽  
Target Gene ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Go Enrichment ◽  
Go Enrichment Analysis

AbstractTriple receptor negative breast cancer (TNBC) is the type of gynecological cancer in the elderly women. This study is aimed to explore molecular mechanism of TNBC via bioinformatics analysis. The gene expression profiles of GSE88715 (including 38 TNBC and 38 normal control) was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using the limma package in R software. Pathway and gene ontology (GO) enrichment analysis were performed based on various pathway dabases and GO database. Then, InnateDb interactome database, Cytoscape and PEWCC1 were applied to construct the protein-protein interaction (PPI) network and screen hub genes. Similarly, miRNet database, NetworkAnalyst database and Cytoscape were applied to construct the target gene - miRNA network and target gene - TF network, and screen targate genes. Pathway and GO enrichment analysis was further performed for hub genes, gene clusters identified via module analysis and targate genes. The expression of hub genes with prognostic values was validated on the UALCAN, cBio Portal, The Human Protein Atlas, receiver operator characteristic (ROC) curve analysis, RT-PCR analysis and immune infiltration analysis. A total of 949 DEGs were identified in TNBC (469 up regulated genes, and 480 down regulated genes), and they were mainly enriched in the terms of phospholipases, toxoplasmosis, immune response, cell surface, glycolysis, biosynthesis of amino acids, carboxylic acid metabolic process and organic substance catabolic process extracellular space. Hub genes including UBD, HLA-B, MYC and HSP90AB1 were identified via PPI network and modules, which were mainly enriched in immune response, antigen processing and presentation, cell cycle and pathways in cancer. Targate genes including CCDC80, PEG10, HOPX and CCNA2 were identified via target gene - miRNA network and target gene - TF network, which were mainly enriched in extracellular structure organization, validated targets of C-MYC transcriptional activation, ensemble of genes encoding core extracellular matrix including ECM glycoproteins and cell cycle. The top five significantly overexpressed mRNA (ADAM15, BATF, NOTCH3, ITGAX and SDC1) and the top five significantly underexpressed mRNA (RPL4, EEF1G, RPL3, RBMX and ABCC2) were selected for further validation in TNBCpatients and healthy controls. Analysis of the expression of genes in the various databases showed that ADAM15, BATF, NOTCH3, ITGAX, SDC1, RPL4, EEF1G, RPL3, RBMX and ABCC2 expressions have a cancer specific pattern in TNBC. Collectively, ADAM15, BATF, NOTCH3, ITGAX, SDC1, RPL4, EEF1G, RPL3, RBMX and ABCC2 may be useful candidate biomarkers for TNBC diagnosis, prognosis and theraputic targates.

scholarly journals Identification of potential and novel target genes in pituitary prolactinoma by bioinformatics analysis

2020 ◽  
Author(s):  
Vikrant Ghatnatti ◽  
Basavaraj Vastrad ◽  
Swetha Patil ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Gene Ontology ◽  
Signaling Pathways ◽  
Target Gene ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Condensed Chromosome ◽  
Go Enrichment ◽  
Go Enrichment Analysis

AbstractPituitary prolactinoma is one of the most complicated and fatally pathogenic pituitary adenomas. Therefore, there is an urgent need to improve our understanding of the underlying molecular mechanism that drives the initiation, progression, and metastasis of pituitary prolactinoma. The aim of the present study was to identify the key genes and signaling pathways associated with pituitary prolactinoma using bioinformatics analysis. Transcriptome microarray dataset GSE119063 was acquired from Gene Expression Omnibus datasets, which included 5 pituitary prolactinoma samples and 4 normal pituitaries samples. We screened differentially expressed genes (DEGs) with limma and investigated their biological function by pathway and Gene Ontology (GO) enrichment analysis. A protein-protein interaction (PPI) network of the up and down DEGs were constructed and analyzed by HIPPIE and Cytoscape software. Module analyses were performed. In addition, a target gene - miRNA network and target gene - TF network of the up and down DEGs were constructed by NetworkAnalyst and Cytoscape software. The set of DEGs exhibited an intersection consisting of 989 genes (461 up-regulated and 528 down-regulated), which may be associated with pituitary prolactinoma. Pathway enrichment analysis showed that the 989 DEGs were significantly enriched in the retinoate biosynthesis II, signaling pathways regulating pluripotency of stem cells, ALK2 signaling events, vitamin D3 biosynthesis, cell cycle and aurora B signaling. Gene Ontology (GO) enrichment analysis also showed that sensory organ morphogenesis, extracellular matrix, hormone activity, nuclear division, condensed chromosome and microtubule binding. In the PPI network and modules, SOX2, PRSS45, CLTC, PLK1, B4GALT6, RUNX1 and GTSE1 were considered as hub genes. In the target gene miRNA network and target gene - TF network, LINC00598, SOX4, IRX1 and UNC13A were considered as hub genes. Using integrated bioinformatics analysis, we identified candidate genes in pituitary prolactinoma, which may improve our understanding of the mechanisms of the pathogenesis and integration; genes may be therapeutic targets and prognostic markers for pituitary prolactinoma.

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 Effects of Fibronectin 1 on Cell Proliferation, Senescence and Apoptosis of Human Glioma Cells Through the PI3K/AKT Signaling Pathway

2018 ◽  
Vol 48 (3) ◽  
pp. 1382-1396 ◽  
Author(s):  
Yu-Xiang Liao ◽  
Zhi-Ping Zhang ◽  
Jie Zhao ◽  
Jing-Ping Liu
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Cell Proliferation ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Akt Signaling ◽  
Pathway Enrichment Analysis ◽  
Akt Signaling Pathway ◽  
Go Enrichment ◽  
Go Enrichment Analysis

Background/Aims: The current study aimed to investigate the role by which fibronectin 1 (FN1) influences the cell cycle, senescence and apoptosis in human glioma cells through the PI3K/ AKT signaling pathway. Methods: Differentially expressed genes (DEGs) were identified based on gene expression data (GSE12657, GSE15824 and GSE45921 datasets) and probe annotation files from Gene Expression Omnibus. The DEGs were identified in connection with gene ontology (GO) enrichment analysis and with the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The positive expression of the FN1 protein was detected by immunohistochemistry. The glioma cell lines U251 and T98G were selected and assigned into blank, negative control (NC) and siRNA-FN1 groups. A dual luciferase reporter gene assay was used to investigate the effects of FN1 on transcriptional activity through the PI3K/AKT signaling pathway. An MTT assay was applied for the detection of cell proliferation, while flow cytometry was employed for cell cycle stage and cellular apoptosis detection. β-galactosidase staining was utilized to detect cellular senescence, a scratch test was applied to evaluate cell migration, and a transwell assay was used to analyze cell invasion. Western blotting and qRT-PCR methods were used to detect the protein and mRNA expression levels, respectively, of the FN1 gene and the related genes in the PI3K/AKT pathway (PI3K, AKT and PTEN), the cell cycle (pRb, CDK4 and Cyclin D1) and cell senescence (p16 and p21) among the collected tissues and cells. Results: GSE12657 profiling revealed FN1 to be the most upregulated gene in glioma. Regarding the GSE12657 and GSE15824 datasets, FN1 gene expression was higher in glioma tissues than in normal tissues. GO enrichment analysis and KEGG pathway enrichment analysis indicated that FN1 is involved in the synthesis of extracellular matrix (ECM) components and the PI3K/AKT signaling pathway. Verification was provided, indicating the role played by the FN1 gene in the regulation of the PI3K/AKT signaling pathway, as silencing the FN1 gene was found to inhibit cell proliferation, promote cell apoptosis and senescence, and reduce migration and invasion through the down-regulation of FN1 gene expression and disruption of the PI3K-AKT signaling pathway. Conclusion: The findings of this study provide evidence highlighting the prominent role played by FN1 in stimulating glioma growth, invasion, and survival through the activation of the PI3K/AKT signaling pathway.

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 Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis

2021 ◽  
Vol 15 ◽  
pp. 117793222110673
Author(s):  
G Prashanth ◽  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Shivakumar Kotrashetti
Keyword(s):  
Target Gene ◽  
Operating Characteristic ◽  
Receiver Operating Characteristic Analysis ◽  
Enrichment Analysis ◽  
Characteristic Analysis ◽  
Hub Genes ◽  
Go Enrichment ◽  
Diagnostic Values ◽  
Upregulated Genes ◽  
Go Enrichment Analysis

Introduction: 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 remdesivir treatment for SARS-CoV-2 infection remain unclear. Here, we used bioinformatics to investigate the candidate genes associated in the molecular pathogenesis of remdesivir-treated SARS-CoV-2-infected patients. Methods: Expression profiling by high-throughput sequencing dataset (GSE149273) was downloaded from the Gene Expression Omnibus, and the differentially expressed genes (DEGs) in remdesivir-treated SARS-CoV-2 infection samples and nontreated SARS-CoV-2 infection samples with an adjusted P value of <.05 and a |log fold change| > 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 NetworkAnalyzer plugin and the other bioinformatics approaches including protein-protein interaction network analysis, module analysis, target gene—miRNA regulatory network, and target gene—TF regulatory network. Finally, a receiver-operating characteristic analysis was performed for diagnostic values associated with hub genes. Results: A total of 909 DEGs were identified, including 453 upregulated genes and 457 downregulated genes. As for the pathway and GO enrichment analysis, the upregulated genes were mainly linked with influenza A and defense response, whereas downregulated genes were mainly linked with drug metabolism—cytochrome P450 and reproductive process. In addition, 10 hub genes (VCAM1, IKBKE, STAT1, IL7R, ISG15, E2F1, ZBTB16, TFAP4, ATP6V1B1, and APBB1) were identified. Receiver-operating characteristic analysis showed that hub genes (CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5, and PDE1A) had good diagnostic values. Conclusion: This study provided insights into the molecular mechanism of remdesivir-treated SARS-CoV-2 infection that might be useful in further investigations.

scholarly journals Identification of Hub Genes Associated With Progression and Prognosis of Bladder Cancer by Integrated Bioinformatics Analysis.

2021 ◽  
Author(s):  
Daoquan Liu ◽  
Jianhong Ma ◽  
Sheng Wei ◽  
Jianmin Liu ◽  
Mingzhou Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Muscle Contraction ◽  
Focal Adhesion ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Expression Levels ◽  
Qrt Pcr ◽  
Go Enrichment ◽  
Go Enrichment Analysis

Abstract Background: Bladder cancer (BLCA) is the most popular malignant carcinomas in genitourinary system which has a high incidence and is prone to relapse. However, the molecular mechanism of BLCA remains to be unclear. Moreover, there is still a shortage of effective biomarkers that can predict progression and prognosis of BLCA. The objective of current study is to screen significant genes as biomarkers to forecast the progression and prognosis of BLCA patients.Methods: Gene expression profile downloaded from TCGA database and GEO database was used. Differential gene expression analysis and WGCNA were conducted to identify differential co-expression genes. In addition, GO enrichment analysis and KEGG pathway analysis were used to explore the functions of these genes. Moreover, PPI network, OS and DFS were used to identify survival-related hub genes. Finally, the expression levels of these genes were validated by qRT-PCR and HPA database.Results: About 124 differential co-expression genes were identified. And these genes were mainly enriched in muscle system process and muscle contraction (BP), contractile fiber, myofibril, sarcomere, focal adhesion and cell-substrate junction (CC) and actin binding (MF) in GO enrichment analysis, while enriched in vascular smooth muscle contraction, focal adhesion, cardiac muscle contraction, hypertrophic cardiomyopathy, dilated cardiomyopathy and regulation of actin cytoskeleton in KEGG analysis. Furthermore, five survival-related hub genes (MYH11, ACTA2, CALD1, TPM1, MYLK) were identified via overall OS and DFS. In addition, the expression levels of the five survival-related genes were upregulated with the procession of BLCA, such as grade, stage and TNM stage. Finally, all survival-related hub genes were found to be down-regulated in BLCA via qRT-PCR and HPA database.Conclusions: Our current study verified five new key genes in BLCA, which could help us better understand the pathogenesis of BLCA. And these five hub genes may be involved in the development and progression of BLCA and served as potential biomarkers.

scholarly journals Exploring the Molecular Mechanism of the Drug-Treated Breast Cancer Based on Gene Expression Microarray

Biomolecules ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 282
Author(s):  
Alshabi ◽  
BasavarajVastrad ◽  
Shaikh ◽  
Vastrad
Keyword(s):  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Network Module ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Go Enrichment ◽  
Human Protein Atlas ◽  
Go Enrichment Analysis

: Breast cancer (BRCA) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers expressed during estradiol and tamoxifen treatment of BRCA. The microarray dataset of E-MTAB-4975 from Array Express database was downloaded, and the differential expressed genes (DEGs) between estradiol-treated BRCA sample and tamoxifen-treated BRCA sample were identified by limma package. The pathway and gene ontology (GO) enrichment analysis, construction of protein-protein interaction (PPI) network, module analysis, construction of target genes—miRNA interaction network and target genes-transcription factor (TF) interaction network were performed using bioinformatics tools. The expression, prognostic values, and mutation of hub genes were validated by SurvExpress database, cBioPortal, and human protein atlas (HPA) database. A total of 856 genes (421 up-regulated genes and 435 down-regulated genes) were identified in T47D (overexpressing Split Ends (SPEN) + estradiol) samples compared to T47D (overexpressing Split Ends (SPEN) + tamoxifen) samples. Pathway and GO enrichment analysis revealed that the DEGs were mainly enriched in response to lysine degradation II (pipecolate pathway), cholesterol biosynthesis pathway, cell cycle pathway, and response to cytokine pathway. DEGs (MCM2, TCF4, OLR1, HSPA5, MAP1LC3B, SQSTM1, NEU1, HIST1H1B, RAD51, RFC3, MCM10, ISG15, TNFRSF10B, GBP2, IGFBP5, SOD2, DHF and MT1H) , which were significantly up- and down-regulated in estradiol and tamoxifen-treated BRCA samples, were selected as hub genes according to the results of protein-protein interaction (PPI) network, module analysis, target genes—miRNA interaction network and target genes-TF interaction network analysis. The SurvExpress database, cBioPortal, and Human Protein Atlas (HPA) database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. A comprehensive bioinformatics analysis was performed, and potential therapeutic applications of estradiol and tamoxifen were predicted in BRCA samples. The data may unravel the future molecular mechanisms of BRCA.

scholarly journals Identification of 10 Hub genes related to the progression of colorectal cancer by co-expression analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9633
Author(s):  
Jie Meng ◽  
Rui Su ◽  
Yun Liao ◽  
Yanyan Li ◽  
Ling Li
Keyword(s):  
Colorectal Cancer ◽  
Network Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Data Sets ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Tissues ◽  
Go Enrichment ◽  
Go Enrichment Analysis

Background Colorectal cancer (CRC) is the third most common cancer in the world. The present study is aimed at identifying hub genes associated with the progression of CRC. Method The data of the patients with CRC were obtained from the Gene Expression Omnibus (GEO) database and assessed by weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses performed in R by WGCNA, several hub genes that regulate the mechanism of tumorigenesis in CRC were identified. Differentially expressed genes in the data sets GSE28000 and GSE42284 were used to construct a co-expression network for WGCNA. The yellow, black and blue modules associated with CRC level were filtered. Combining the co-expression network and the PPI network, 15 candidate hub genes were screened. Results After validation using the TCGA-COAD dataset, a total of 10 hub genes (MT1X, MT1G, MT2A, CXCL8, IL1B, CXCL5, CXCL11, IL10RA, GZMB, KIT) closely related to the progression of CRC were identified. The expressions of MT1G, CXCL8, IL1B, CXCL5, CXCL11 and GZMB in CRC tissues were higher than normal tissues (p-value < 0.05). The expressions of MT1X, MT2A, IL10RA and KIT in CRC tissues were lower than normal tissues (p-value < 0.05). Conclusions By combinating with a series of methods including GO enrichment analysis, KEGG pathway analysis, PPI network analysis and gene co-expression network analysis, we identified 10 hub genes that were associated with the progression of CRC.

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.

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