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Identification of a key candidate gene?phenotype network mediated by glycyrrhizic acid using pharmacogenomic analysis.

ABSTRACT: Glycyrrhizic acid (GA) is primarily used as an anti?inflammatory agent in cases of chronic hepatitis. However, its underlying mechanisms in diverse biological processes and its reported benefits are yet to be fully elucidated. In the current study, an analytical method based on pharmacogenomics was established to mine disease?modulatory activities mediated by GA. Five primary protein targets and 138 functional partners were identified for GA by querying open?source databases, including Drugbank and STRING. Subsequently, GA?associated primary and secondary protein targets were integrated into Cytoscape to construct a protein?protein interaction network to establish connectivity. GA?associated target genes were then clustered based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. The tumor necrosis factor axis was revealed to be a primary module regulated by GA?associated targets. Furthermore, 12 hub genes were queried to assess their potential anti?cancer effects using cBioPortal. The results indicated that pharmacogenomics?based analysis improved understanding of the underlying drug?target events of GA and provided predictive and definitive leads for future studies.

SUBMITTER: Wang S

PROVIDER: S-EPMC6691250 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Identification of a key candidate gene‑phenotype network mediated by glycyrrhizic acid using pharmacogenomic analysis.

Wang Shiqun S Li Lu L Shi Long L

Molecular medicine reports 20190712 3

Glycyrrhizic acid (GA) is primarily used as an anti‑inflammatory agent in cases of chronic hepatitis. However, its underlying mechanisms in diverse biological processes and its reported benefits are yet to be fully elucidated. In the current study, an analytical method based on pharmacogenomics was established to mine disease‑modulatory activities mediated by GA. Five primary protein targets and 138 functional partners were identified for GA by querying open‑source databases, including Drugbank ...[more]

PMID: 31322195

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Project description:A cancer immune phenotype characterized by an active T-helper 1 (Th1)/cytotoxic response is associated with responsiveness to immunotherapy and favorable prognosis across different tumors. However, in some cancers, such an intratumoral immune activation does not confer protection from progression or relapse. Defining mechanisms associated with immune evasion is imperative to refine stratification algorithms, to guide treatment decisions and to identify candidates for immune-targeted therapy. Molecular alterations governing mechanisms for immune exclusion are still largely unknown. The availability of large genomic datasets offers an opportunity to ascertain key determinants of differential intratumoral immune response. We follow a network-based protocol to identify transcription regulators (TRs) associated with poor immunologic antitumor activity. We use a consensus of four different pipelines consisting of two state-of-the-art gene regulatory network inference techniques, regularized gradient boosting machines and ARACNE to determine TR regulons, and three separate enrichment techniques, including fast gene set enrichment analysis, gene set variation analysis and virtual inference of protein activity by enriched regulon analysis to identify the most important TRs affecting immunologic antitumor activity. These TRs, referred to as master regulators (MRs), are unique to immune-silent and immune-active tumors, respectively. We validated the MRs coherently associated with the immune-silent phenotype across cancers in The Cancer Genome Atlas and a series of additional datasets in the Prediction of Clinical Outcomes from Genomic Profiles repository. A downstream analysis of MRs specific to the immune-silent phenotype resulted in the identification of several enriched candidate pathways, including NOTCH1, TGF-$\beta $, Interleukin-1 and TNF-$\alpha $ signaling pathways. TGFB1I1 emerged as one of the main negative immune modulators preventing the favorable effects of a Th1/cytotoxic response.

Project description:BackgroundGlioblastoma multiforme (GBM) is the most common aggressive malignant brain tumor. However, the molecular mechanism of glioblastoma formation is still poorly understood. To identify candidate genes that may be connected to glioma growth and development, weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between gene sets and clinical characteristics. We also explored the function of the key candidate gene.MethodsTwo GBM datasets were selected from GEO Datasets. The R language was used to identify differentially expressed genes. WGCNA was performed to construct a gene co-expression network in the GEO glioblastoma samples. A custom Venn diagram website was used to find the intersecting genes. The GEPIA website was applied for survival analysis to determine the significant gene, FUBP3. OS, DSS, and PFI analyses, based on the UCSC Cancer Genomics Browser, were performed to verify the significance of FUBP3. Immunohistochemistry was performed to evaluate the expression of FUBP3 in glioblastoma and adjacent normal tissue. KEGG and GO enrichment analyses were used to reveal possible functions of FUBP3. Microenvironment analysis was used to explore the relationship between FUBP3 and immune infiltration. Immunohistochemistry was performed to verify the results of the microenvironment analysis.ResultsGSE70231 and GSE108474 were selected from GEO Datasets, then 715 and 694 differentially expressed genes (DEGs) from GSE70231 and GSE108474, respectively, were identified. We then performed weighted gene co-expression network analysis (WGCNA) and identified the most downregulated gene modules of GSE70231 and GSE108474, and 659 and 3915 module genes from GSE70231 and GSE108474, respectively, were selected. Five intersection genes (FUBP3, DAD1, CLIC1, ABR, and DNM1) were calculated by Venn diagram. FUBP3 was then identified as the only significant gene by survival analysis using the GEPIA website. OS, DSS, and PFI analyses verified the significance of FUBP3. Immunohistochemical analysis revealed FUBP3 expression in GBM and adjacent normal tissue. KEGG and GO analyses uncovered the possible function of FUBP3 in GBM. Tumor microenvironment analysis showed that FUBP3 may be connected to immune infiltration, and immunohistochemistry identified a positive correlation between immune cells (CD4 + T cells, CD8 + T cells, and macrophages) and FUBP3.ConclusionFUBP3 is associated with immune surveillance in GBM, indicating that it has a great impact on GBM development and progression. Therefore, interventions involving FUBP3 and its regulatory pathway may be a new approach for GBM treatment.

Dataset Information

Identification of a key candidate gene?phenotype network mediated by glycyrrhizic acid using pharmacogenomic analysis.

Publications

Identification of a key candidate gene‑phenotype network mediated by glycyrrhizic acid using pharmacogenomic analysis.

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