A computational bioinformatics analysis of gene expression identifies candidate agents for prostate cancer

Dy, Wen; Geng, Jiang; Li, Wei; Cc, Guo; Zheng, Junhua

doi:10.1111/and.12127

Cited by 10 publications

(9 citation statements)

References 45 publications

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“…Equivalently important, it is necessary to identify and validate new molecular drug targets in order to develop underlying therapeutic agents that may be successful in remedying prostate cancer. Surprisingly, most researches concentrate on an individual genetic result or the results are derived from an alone cohort research through microarray analysis which is not uniform with each other (Derosa et al, ; Mortensen et al, ; Satake et al, ; Wen, Geng, Li, Guo, & Zheng, ). Our study integrated four cohorts profile data sets from different groups, utilised bioinformatics methods to deeply analyse these data sets and identified 217 significantly changed DEGs.…”

Section: Discussionmentioning

confidence: 99%

Identification of key genes in prostate cancer gene expression profile by bioinformatics

Lü

Ding

2018

Andrologia

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The aim of this study was to identify key candidate genes in prostate cancer. The gene expression profiles of GSE32448, GSE45016, GSE46602 and GSE104749 were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between prostate cancer and normal samples were identified by R language. The gene ontology functional and pathway enrichment analyses of DEGs were performed by the Database for Annotation, Visualization and Integrated Discovery software followed by the construction of protein-protein interaction network. Hub gene identification was performed by the plug-in cytoHubba in Cytoscape software. The 217 DEGs were significantly enriched in biological processes including epithelial cell differentiation, response to estradiol and several pathways, mainly associated with protein digestion and absorption pathway in prostate cancer. Epithelial cell adhesion molecule, twist family basic helix-loop-helix transcription factor 1, CD38 molecule and vascular endothelial growth factor A were identified as hub genes. The expression levels of hub genes were consistent with data obtained in The Cancer Genome Atlas for prostate adenocarcinoma. These hub genes may be used as potential targets for prostate cancer diagnosis and treatment.

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Section: Discussionmentioning

confidence: 99%

Identification of key genes in prostate cancer gene expression profile by bioinformatics

Lü

Ding

2018

Andrologia

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“…It could be interesting to explore 8-azaguanine as an anti-Prostate Cancer drug. In a recent study Wen et al (2013) also indicated 8-azaguanine for potential therapeutic efficacy in prostate cancer. the activity, while the rest of the structure is just needed to maintain the shape.…”

Section: Drug Descriptionmentioning

confidence: 98%

Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis

et al. 2014

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Motivation: Analysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how to tailor drug therapies. New methods are required for integrated analyses of a large number of chemical features of drugs against the corresponding genome-wide responses of multiple cell models.Results: In this article, we present the first comprehensive multi-set analysis on how the chemical structure of drugs impacts on genome-wide gene expression across several cancer cell lines [Connectivity Map (CMap) database]. The task is formulated as searching for drug response components across multiple cancers to reveal shared effects of drugs and the chemical features that may be responsible. The components can be computed with an extension of a recent approach called Group Factor Analysis. We identify 11 components that link the structural descriptors of drugs with specific gene expression responses observed in the three cell lines and identify structural groups that may be responsible for the responses. Our method quantitatively outperforms the limited earlier methods on CMap and identifies both the previously reported associations and several interesting novel findings, by taking into account multiple cell lines and advanced 3D structural descriptors. The novel observations include: previously unknown similarities in the effects induced by 15-delta prostaglandin J2 and HSP90 inhibitors, which are linked to the 3D descriptors of the drugs; and the induction by simvastatin of leukemia-specific response, resembling the effects of corticosteroids.Availability and implementation: Source Code implementing the method is available at: http://research.ics.aalto.fi/mi/software/GFAsparseContact: suleiman.khan@aalto.fi or samuel.kaski@aalto.fiSupplementary Information: Supplementary data are available at Bioinformatics online.

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“…High proliferative and antiapoptotic potential are related to the up-regulation of growth hormone receptor and calmodulins [ 31 ]. The top 20 genes which were found to be up-regulated in SCC early passage cells in comparison to SCC horn tissue were investigated to have roles in other cancers as well as SCC in human and domestic animals ( Table-7 ) [ 32 - 61 ] and vice versa ( Table-8 ) [ 62 - 95 ].…”

Section: Resultsmentioning

confidence: 99%

Transcriptomic comparison of primary bovine horn core carcinoma culture and parental tissue at early stage

Shil

Joshi

et al. 2017

Vet World

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Aim:Squamous cell carcinoma or SCC of horn in bovines (bovine horn core carcinoma) frequently observed in Bos indicus affecting almost 1% of cattle population. Freshly isolated primary epithelial cells may be closely related to the malignant epithelial cells of the tumor. Comparison of gene expression in between horn’s SCC tissue and its early passage primary culture using next generation sequencing was the aim of this study.Materials and Methods:Whole transcriptome sequencing of horn’s SCC tissue and its early passage cells using Ion Torrent PGM were done. Comparative expression and analysis of different genes and pathways related to cancer and biological processes associated with malignancy, proliferating capacity, differentiation, apoptosis, senescence, adhesion, cohesion, migration, invasion, angiogenesis, and metabolic pathways were identified.Results:Up-regulated genes in SCC of horn’s early passage cells were involved in transporter activity, catalytic activity, nucleic acid binding transcription factor activity, biogenesis, cellular processes, biological regulation and localization and the down-regulated genes mainly were involved in focal adhesion, extracellular matrix receptor interaction and spliceosome activity.Conclusion:The experiment revealed similar transcriptomic nature of horn’s SCC tissue and its early passage cells.

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A computational bioinformatics analysis of gene expression identifies candidate agents for prostate cancer

Cited by 10 publications

References 45 publications

Identification of key genes in prostate cancer gene expression profile by bioinformatics

Identification of key genes in prostate cancer gene expression profile by bioinformatics

Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis

Transcriptomic comparison of primary bovine horn core carcinoma culture and parental tissue at early stage

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