Dataset Information

Comparative Analysis of Prostate Cancer Gene Regulatory Networks via Hub Type Variation.

ABSTRACT: BACKGROUND:Prostate cancer is one of the most widespread cancers in men and is fundamentally a genetic disease. Identifying regulators in cancer using novel systems biology approaches will potentially lead to new insight into this disease. It was sought to address this by inferring gene regulatory networks (GRNs). Moreover, dynamical analysis of GRNs can explain how regulators change among different conditions, such as cancer subtypes. METHODS:In our approach, independent gene regulatory networks from each prostate state were reconstructed using one of the current state-of-art reverse engineering approaches. Next, crucial genes involved in this cancer were highlighted by analyzing each network individually and also in comparison with each other. RESULTS:In this paper, a novel network-based approach was introduced to find critical transcription factors involved in prostate cancer. The results led to detection of 38 essential transcription factors based on hub type variation. Additionally, experimental evidence was found for 29 of them as well as 9 new transcription factors. CONCLUSION:The results showed that dynamical analysis of biological networks may provide useful information to gain better understanding of the cell.

SUBMITTER: Khosravi P

PROVIDER: S-EPMC4388891 | biostudies-literature | 2015 Jan-Mar

REPOSITORIES: biostudies-literature

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Comparative Analysis of Prostate Cancer Gene Regulatory Networks via Hub Type Variation.

Khosravi Pegah P Gazestani Vahid H VH Akbarzadeh Mohammad M Mirkhalaf Samira S Sadeghi Mehdi M Goliaei Bahram B

Avicenna journal of medical biotechnology 20150101 1

<h4>Background</h4>Prostate cancer is one of the most widespread cancers in men and is fundamentally a genetic disease. Identifying regulators in cancer using novel systems biology approaches will potentially lead to new insight into this disease. It was sought to address this by inferring gene regulatory networks (GRNs). Moreover, dynamical analysis of GRNs can explain how regulators change among different conditions, such as cancer subtypes.<h4>Methods</h4>In our approach, independent gene reg ...[more]

PMID: 25926947

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Project description:BackgroundThe aim of this study was to identify novel genes in promoting primary prostate cancer (PCa) progression and to explore its role in the prognosis of prostate cancer.MethodsFour microarray datasets containing primary prostate cancer samples and benign prostate samples were downloaded from Gene Expression Omnibus (GEO), then differentially expressed genes (DEGs) were identified by R software (version 3.6.2). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to identify the function of DEGs. Using STRING and Cytoscape (version 3.7.1), we constructed a protein-protein interaction (PPI) network and identified the hub gene of prostate cancer. Clinical data on GSE70770 and TCGA was collected to show the role of hub gene in prostate cancer progression. The correlations between hub gene and clinical parameters were also indicated by cox regression analysis. Gene Set Enrichment Analysis (GSEA) was performed to highlight the function of Ubiquitin-conjugating enzyme complex (UBE2C) in prostate cancer.Results243 upregulated genes and 298 downregulated genes that changed in at least two microarrays have been identified. GO and KEGG analysis indicated significant changes in the oxidation-reduction process, angiogenesis, TGF-beta signaling pathway. UBE2C, PDZ-binding kinase (PBK), cyclin B1 (CCNB1), Cyclin-dependent kinase inhibitor 3 (CDKN3), topoisomerase II alpha (TOP2A), Aurora kinase A (AURKA) and MKI67 were identified as the candidate hub genes, which were all correlated with prostate cancer patient' disease-free survival in TCGA. In fact, only UBE2C was highly expressed in prostate cancer when compared with benign prostate tissue in TCGA and the expression of UBE2C was also in parallel with the Gleason score of prostate cancer. Cox regression analysis has indicated UBE2C could function as the independent prognostic factor of prostate cancer. GSEA showed UBE2C had played an important role in the pathway of prostate cancer, such as NOTCH signaling pathway, WNT-β-catenin signaling pathway.ConclusionsUBE2C was pivotal for the progression of prostate cancer and the level of UBE2C was important to predict the prognosis of patients.

Dataset Information

Comparative Analysis of Prostate Cancer Gene Regulatory Networks via Hub Type Variation.

Publications

Comparative Analysis of Prostate Cancer Gene Regulatory Networks via Hub Type Variation.

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