scholarly journals High Expression of PDE8B and DUOX2 Associated with Ability of Metastasis in Thyroid Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhenguo Sun ◽  
Xiaoshuai Yuan ◽  
Peng Du ◽  
Peng Chen
Keyword(s):  
Thyroid Carcinoma ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Thyroid Stimulating Hormone ◽  
Hub Genes ◽  
Gene Expressions ◽  
Go Enrichment ◽  
Poorly Differentiated ◽  
Microarray Datasets ◽  
Go Enrichment Analysis

Background. Hormone is an independent factor that induces differentiation of thyroid cancer (TC) cells. The thyroid-stimulating hormone (TSH) could promote the progression and invasion in TC cells. However, few genes related to hormone changes are studied in poorly differentiated metastatic TC. This study is aimed at constructing a gene set’s coexpression correlation network and verifying the changes of some hub genes involved in regulating hormone levels. Methods. Microarray datasets of TC samples were obtained from public Gene Expression Omnibus (GEO) databases. R software and bioinformatics packages were utilized to identify the differentially expressed genes (DEGs), important gene module eigengenes, and hub genes. Subsequently, the Gene Ontology (GO) enrichment analysis was constructed to explore important biological processes that are associated with the mechanism of poorly differentiated TC. Finally, some hub gene expressions were validated through real-time PCR and immunoblotting. Results. Gene chip with category number GSE76039 was analyzed, and 1190 DEGs were screened with criteria of P < 0.05 and ∣ log 2 foldchange ∣ > 2 . Our analysis showed that human dual oxidase 2 (DUOX2) and phosphodiesterase 8B (PDE8B) are the two important hub genes in a coexpression network. In addition, the validated experimental results showed that the expression levels of both DUOX2 and PDE8B were elevated in poorly differentiated metastatic TC tissues. Conclusion. This study identified and validated that DUOX2 and PDE8B were significantly associated with the metastasis ability of thyroid carcinoma.

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.

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 the Core MicroRNAs and Potential Molecular Mechanismsin Sarcoidosis Using Bioinformatics Analysis

2021 ◽  
Vol 8 ◽  
Author(s):  
Yuan Cao ◽  
Hua Zhang ◽  
Lulu Zheng ◽  
Qiao Li
Keyword(s):  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
The Core ◽  
Ppi Networks ◽  
Go Enrichment ◽  
Mirnas Expression ◽  
Go Enrichment Analysis ◽  
Insight Into

Sarcoidosis is a systemic heterogeneous inflammatory disease; however, the etiology and pathogenesis of sarcoidosis are still unknown. Herein, we investigated the core microRNAs and potential molecular mechanisms in sarcoidosis. The DE-miRNAs were diagnosed using the LIMMA software package. DIANA-mirPath was employed to perform pathway and GO enrichment analysis of the DE-miRNAs. PPI networks and miRNA-target gene regulatory networks were used to obtain insight into the actions of DE-miRNAs. Expression of the hub genes along with miRNAs was validated in clinical specimens. Overall, 266 DE-miRNAs were screened. Among these DE-miRNAs, hsa-miR-144, hsa-miR-126, as well as hsa-miR-106a were the upmost upregulated miRNAs; hsa-miR-151-3p, hsa-miR-320d, and hsa-miR-324-3p were the top downregulated miRNAs. NR3C1, ZBTB7A, NUFIP2, BZW1, ERGIC2, and VEGFA were mapped as the most targeted hub genes in the upregulation of miRNAs, and MCL1 and SAE1 were the most targeted hub genes in the downregulation of miRNA. VEGFA and NR3C1 were selected and potentially modulated by hsa-miR-20b, hsa-miR-126, and hsa-miR-106a. In sarcoidosis pathological tissue, hsa-miR-126 was highly expressed, and VEGFA and NR3C1 were overexpressed. In conclusion, our results revealed the dysregulation of hsa-miR-126 and a potential regulatory mechanism for pathogenesis in sarcoidosis.

Bioinformatics Analysis of Prognostic value and prospective Pathway signal of miR-30a in Ovarian Cancer

2020 ◽  
Author(s):  
Weijia Lu ◽  
Yunyu Wu ◽  
CanXiong Lu ◽  
Ting Zhu ◽  
ZhongLu Ren ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Target Genes ◽  
Ovarian Tissue ◽  
Critical Role ◽  
Differentially Expressed Gene ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Go Enrichment

Abstract Objective: MicroRNAs (MiRNAs) is thought to play an critical role in the initiation and progress of ovarian cancer(OC). Although miRNAs has been widely recognized in ovarian cancer, the role of hsa-miR-30a-5p (miR-30a) in OC has not been fully elucidated.Methods:Three mRNA datasets of normal ovarian tissue and OC, GSE18520 ,GSE14407 and GSE36668, were downloaded from Gene Expression Omnibus(GEO) to find the differentially expressed gene (DEG). Then the target genes of hsa-miR-30a-5p were predicted by miRWALK3.0 and TargetScan. Then, the gene overlap between DEG and the predicted target genes of miR-30a in OC was analyzed by Gene Ontology (GO) enrichment analysis. Protein-protein interaction (PPI) network was conducted by STRING and Cytoscape, and the effect of HUB gene on the outcome of OC was analyzed.Results：A common pattern of up-regulation of miR-30a in OC was found. A total of 225 DEG, were identified, both OC-related and miR-30a-related. Many DEG are enriched in the interactions of intracellular matrix tissue, ion binding and biological process regulation. Among the 10 major Hub genes analyzed by PPI, five Hub genes were significantly related to the overall poor survival of OC patients, in which the low expression of ESR1 ,MAPK10, Tp53 and the high expression of YKT ,NSF were related to poor prognosis of OC.Conclusion：Our results indicate that miR-30a is of significance for the biological progress of OC.

scholarly journals Bioinformatics analysis of prognostic value and prospective pathway signal of miR-30a in ovarian cancer

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Weijia Lu ◽  
Yunyu Wu ◽  
Can Xiong Lu ◽  
Ting Zhu ◽  
Zhong Lu Ren ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Target Genes ◽  
Ovarian Tissue ◽  
Critical Role ◽  
Differentially Expressed Gene ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Go Enrichment

Abstract Objective MicroRNAs (MiRNAs) is thought to play a critical role in the initiation and progress of ovarian cancer (OC). Although miRNAs has been widely recognized in ovarian cancer, the role of hsa-miR-30a-5p (miR-30a) in OC has not been fully elucidated. Methods Three mRNA datasets of normal ovarian tissue and OC, GSE18520,GSE14407 and GSE36668, were downloaded from Gene Expression Omnibus (GEO) to find the differentially expressed gene (DEG). Then the target genes of hsa-miR-30a-5p were predicted by miRWALK3.0 and TargetScan. Then, the gene overlap between DEG and the predicted target genes of miR-30a in OC was analyzed by Gene Ontology (GO) enrichment analysis. Protein-protein interaction (PPI) network was conducted by STRING and Cytoscape, and the effect of HUB gene on the outcome of OC was analyzed. Results A common pattern of up-regulation of miR-30a in OC was found. A total of 225 DEG, were identified, both OC-related and miR-30a-related. Many DEG are enriched in the interactions of intracellular matrix tissue, ion binding and biological process regulation. Among the 10 major Hub genes analyzed by PPI, five Hub genes were significantly related to the overall poor survival of OC patients, in which the low expression of ESR1,MAPK10, Tp53 and the high expression of YKT,NSF were related to poor prognosis of OC. Conclusion Our results indicate that miR-30a is of significance for the biological progress of OC.

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 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.

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