Dongbin Bi1, Hao Ning1, Shuai Liu1, Xinxiang Que1, Kejia Ding2. 1. Department of Urology, Provincial Hospital affiliated to Shandong University, Jinan, PR China. 2. Department of Urology, Provincial Hospital affiliated to Shandong University, Jinan, PR China. Electronic address: kejia_ding@yeah.net.
Abstract
OBJECTIVES: To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. METHODS: The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. RESULTS: Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. CONCLUSIONS: Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC.
OBJECTIVES: To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. METHODS: The differentially expressed genes (DEGs) between bladder carcinomapatients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. RESULTS: Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. CONCLUSIONS: Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC.
Authors: Hai Huang; Qin Zhang; Chen Ye; Jian-Min Lv; Xi Liu; Lu Chen; Hao Wu; Lei Yin; Xin-Gang Cui; Dan-Feng Xu; Wen-Hui Liu Journal: J Cancer Res Clin Oncol Date: 2017-08-28 Impact factor: 4.553
Authors: Ovidiu D Iancu; Alexander Colville; Nicole A R Walter; Priscila Darakjian; Denesa L Oberbeck; James B Daunais; Christina L Zheng; Robert P Searles; Shannon K McWeeney; Kathleen A Grant; Robert Hitzemann Journal: Addict Biol Date: 2017-02-28 Impact factor: 4.280