High-throughput Gene Expression Profiling: Its Application in Studying Transfusion-Transmitted Infections

Liu, Bing

doi:10.4172/1948-593x.1000e111

Cited by 1 publication

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References 18 publications

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“…These data can be further used to discover common or new therapeutic approaches in the area of cancer heterogeneity, drug discovery, biomarker development, key genes identification to understand cancer progression [11]. Especially, microarray technology helps to unveil thousands of genes expressed at a time which can be associated with meta-analysis approach in order to find the best targets [12,13]. In this study, we aim to analyze the gene expression data and differentially expressed genes (DEGs) in GBSCs using meta-analysis approach.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2018

RJLBPCS

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Glioblastoma (GB) type IV, the most vulnerable brain tumor is worsened by the role of glioblastoma stem cells leads to multiple targets in its disease physiology. The objective of the present study is to identify potential key genes, miRNAs and pathways in glioblastoma stem cells (GBSCs) using meta-analysis approach. Using the seven publicly available GEO datasets, 368 differentially expressed genes (DEGs) are actively involved in the GBSCs population in which 172 and 196 DEGs were down-and up-regulated in the GBSCs samples, respectively. Grouping of DEGs revealed the primary involvement of blood coagulation process (P = 9.16E-06) whereas its associated immunological processes including complement and coagulation cascades (P = 6.56E-06) were noted in the KEGG pathway enrichment analysis. Furthermore, the protein-protein interaction networks showed the prominent hub of proteins: Topoisomerase (DNA) IIα (TOP2; degree= 24), Von Willebrand factor (VWF; degree= 23), Fibronectin 1 (FN1; degree= 21) and Secreted phosphoprotein 1 (SPP1; degree= 21). Overall, results suggested the active role of several genes associated with the blood coagulation and complement coagulation cascades in GBSCs and could be regarded as potential biomarkers.

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Section: Introductionmentioning

confidence: 99%

Untitled

2018

RJLBPCS

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High-throughput Gene Expression Profiling: Its Application in Studying Transfusion-Transmitted Infections

Cited by 1 publication

References 18 publications

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