GNA15 expression in small intestinal neuroendocrine neoplasia: Functional and signalling pathway analyses

Zanini, Sara; Giovinazzo, Francesco; Alaimo, Daniele; Lawrence, Ben; Pfragner, Roswitha; Bassi, Claudio; Modlin, Irvin M.; Kidd, Mark

doi:10.1016/j.cellsig.2015.02.001

Cited by 13 publications

(16 citation statements)

References 52 publications

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“…In a previous research, EDNRA was found to upregulate in pancreatic neuroendocrine tumors (PanNET) [29]. GNA15 was not expressed in normal neuroendocrine cells but was overexpressed in gastroenteropancreatic neuroendocrine neoplasia (GEP-NEN) cell lines [30]. There are still limit findings of these candidate genes in PDAC research.…”

Section: Discussionmentioning

confidence: 93%

Bioinformatic evidences and analysis of putative biomarkers in pancreatic ductal adenocarcinoma

Feng

Liu

et al. 2019

Heliyon

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Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers. Aberrant expression of genes plays important role in the procession of PDAC. The analysis of gene expression profile will contribute to the research of carcinoma mechanism. Objective: This present study is focused to investigate the differentially expressed genes (DEGs) from 3 PDAC microarray datasets, which would provide candidate genes for putative biomarkers to understand the mechanism of PDAC and potential targets of treatment. Method: Based on the overlap genes obtained from 3 GEO datasets, the hub genes were identified using STRING and Cytoscape plugin MCODE. The enrichment and function analysis were applied using DAVID. The proteinprotein interaction network was performed using cBioPortal and UCSC Xena. The Oncomine was finally used to determine the candidate gene by analyzing their expression between pancreas sample and PDAC sample. Results: 25 hub genes were selected from a total of 1006 DEGs from 3 GEO datasets, consisting of 14 upregulated genes and 11 downregulated genes. The overall decline of hub gene expression enriched in G1 phase of cell cycle in other subtypes of pancreatic cancer. Oncomine database was ultimately performed to determine the 8 candidate genes, including CXCL5, CCL20, NMU, F2R, ANXA1, EDNRA, LPAR6, and GNA15. Conclusions: Conclusively, 8 candidate genes would become the potential PDAC combined biomarkers for diagnosis and therapeutic strategies.

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Section: Discussionmentioning

confidence: 93%

Bioinformatic evidences and analysis of putative biomarkers in pancreatic ductal adenocarcinoma

Feng

Liu

et al. 2019

Heliyon

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“…GNA11 is associated with Congenital Hemangioma 41 . GNA15 is associated with inhibition of proliferation, activation of apoptosis and differential effects 42 . MED1 is associated with some biological process such as prostate cancer cell growth 43 .…”

Section: Resultsmentioning

confidence: 99%

Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data

Liao

et al. 2017

Sci Rep

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Multiple types of high throughput genomics data create a potential opportunity to identify driver patterns in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. To identify candidate driver genes and the corresponding driving patterns for resistant and sensitive tumors from the heterogeneous data, we combined gene co-expression modules with mutation modulators and proposed the method to identify driver patterns. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles through weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNV data and somatic mutation data, and a mutation network is constructed from the mutation matrix. Thirdly, candidate modulators are selected from significant genes by clustering vertexs of the mutation network. Finally, a regression tree model is utilized for module network learning, in which the obtained gene modules and candidate modulators are trained for the driving pattern identification and modulators regulatory exploration. Many identified candidate modulators are known to be involved in biological meaningful processes associated with ovarian cancer, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11, and so on.

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“…GNA11 is associated with Congenital Hemangioma (Ayturk et al 2016). GNA15 is associated with inhibition of proliferation, activation of apoptosis and differential effects (Zanini et al 2015). MED1 is associated with some biological process such as prostate cancer cell growth (Liu et al 2010).…”

Section: Enrichment Analysis For Driver Patternsmentioning

confidence: 99%

Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data

Liu

et al. 2017

Preprint

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The multiple types of high throughput genomics data create a potential opportunity to identify driver pattern in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. However, it is a great challenging work to integrate omics data, including somatic mutations, Copy Number Variations (CNVs) and gene expression profiles, to distinguish interactions and regulations which are hidden in drug response dataset of ovarian cancer. To distinguish the candidate driver genes and the corresponding driving pattern for resistant and sensitive tumor from the heterogeneous data, we combined gene co-expression modules and mutation modulators and proposed the identification driver patterns method. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles via weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNVs and somatic mutations, and a mutation network is constructed from this mutation matrix. The candidate modulators are selected from the significant genes by clustering the vertex of the mutation network. At last, regression tree model is utilized for module networks learning in which the achieved gene modules and candidate modulators are trained for the driving pattern identification and modulator regulatory exploring. Many of the candidate modulators identified are known to be involved in biological meaningful processes associated with ovarian cancer, which can be regard as potential driver genes, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11 and so on, which can help to facilitate the discovery of biomarkers, molecular diagnostics, and drug discovery.

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GNA15 expression in small intestinal neuroendocrine neoplasia: Functional and signalling pathway analyses

Cited by 13 publications

References 52 publications

Bioinformatic evidences and analysis of putative biomarkers in pancreatic ductal adenocarcinoma

Bioinformatic evidences and analysis of putative biomarkers in pancreatic ductal adenocarcinoma

Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data

Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data

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