| Systems biology is a discipline that based on the view of holistic theory,attempting to systematically understand the biological structure,function and behaviours by researching interactions and influences among genes,proteins and other metabolic molecules.Network as a model of systems biology has been widely used in studying biological problems.This dissertation mainly focus on the gene regulation which is a crucial mechanism of living organisms.By constructing transcriptional regulatory network including regulators of transcription factors and miRNAs,we tried to uncover regulatory mechanisms for tissue-specific genes and housekeeping genes,as well as tissue-specific regulatory patterns to discover basic laws of structures and regulations for gene regulatory network.Furthermore,by utilizing network structure,we identified biomarkers to discrimiante between solid pseudopapillary neoplasms and malignant pancreatic tumours including pancreatic neuroendocrine tumours and ductal adenocarcinomas in gene regulatory network.The research is divided into three sections in detail:1.Regulatory features for tissue-specific,housekeeping and disease genesTranscription factors(TFs)and miRNAs are essential for the regulation of gene expression;however,the global view of human gene regulatory networks remains poorly understood.For example,how is the expression of so many genes regulated by limited cohorts of regulators and how are genes differentially expressed in different tissues despite the genetic code being the same in all tissues?We analyzed the network properties of housekeeping and tissue-specific genes in gene regulatory networks from seven human tissues.Our results show that different classes of genes behave quite differently in these networks.Tissue-specific miRNAs show a higher average target number compared with non-tissue specific miRNAs,i.e.average targets for tissue specific miRNAs and non-tissue specific miRNAs in seven tissues are 278.55 and 55.011.The result indicates that most of non-tissue specific miRNAs tend to regulate identical targets,while distinct tissue-specific miRNAs are prone to regulate different sets of targets.It coud infer that non-tissue specific miRNAs and their targets formed a core module to excute basic functions of cells,and tissue specific miRNAs with their target attached to the core to achieve cell specific functions in evolution.Tissue-specific TFs exhibit higher in-degree,out-degree,cluster coefficient and betweenness values,i.e.average out-degree,in-degree,cluster coiefficient and betweenness for brain-specific TFs are 28.75,8.25,0.005 and 0.000374 which all significantly higher for brain-non-specific TFs(15.24,5.5,0.002 and 0.000228),indicating that tissue-specific TFs occupy central positions in the regulatory network and that they transfer genetic information from upstream genes to downstream genes more quickly than other TFs.Housekeeping TFs tend to have higher cluster coefficients compared with other genes that are neither housekeeping nor tissue specific,indicating that housekeeping TFs tend to regulate their targets synergistically.Several topological properties of disease-associated miRNAs and genes were found to be significantly different from those of non-disease-associated miRNAs and genes.Tissue-specific miRNAs,TFs and disease genes have particular topological properties within the transcriptional regulatory networks of the seven human tissues examined.The tendency of tissue-specific miRNAs to regulate different sets of genes shows that a particular tissue-specific miRNA and its target gene set may form a regulatory module to execute particular functions in the process of tissue differentiation.The regulatory patterns of tissue-specific TFs reflect their vital role in regulatory networks and their importance to biological functions in their respective tissues.The topological differences between disease and non-disease genes may aid the discovery of new disease genes or drug targets.Determining the network properties of these regulatory factors will help define the basic principles of human gene regulation and the molecular mechanisms of disease.2.Mining and analysis for tissue-specific modulesThe complex functions of a living cell are carried out through the coordinated activity by many genes and gene products.While gene expression is concerted controlled by transcription factors and miRNAs at transcription and post-transcription level respectively.Tancscription factors,miRNAs and their targets constitue a complicate and fine regulatory systems to determine many biological processes,and play vital role in origination and development of disease.Various functions of a living cell are fulfilled by functional modules or complex of regulators and target genes which share common funtions.However,the mechanisms about co-regulation is still illusive.Considering tissue-specific regulations are critical for functions and morphological formation of relative tissues,We attempted to investigate the mechanism by analyzing tissue modules within seven human tissue regulatory networks.We firstly acquired 20,11,15,12,16,16 and 15 regulatory modules for brain,heart,kidney,liver,ovary,spleen and testis respectively.Then we tried to find tissue-specific moduels by module comparisons among seven tissues.Kindey-specific and brain-specifc modules were found.Particularly,kindey-specific module is closely correlated with renal cancer.Heart-specifc module is important for formation of ventricular septum and it is crucial for functions of cardiac cells.3.Identifying biomarkers for discrimination between Solid pseudopapillary neoplasms and malignant pancreatic tumoursSolid pseudopapillary neoplasms(SPNs)are pancreatic tumours with low malignant potential and good prognosis.However,correct diagnosis of SPNs is challenging because some of their features resemble other types of pancreatic malignant tumours,such as pancreatic neuroendocrine tumours and ductal adenocarcinomas.If the patients with SPNs were misdiagnoosed as malignant pancreatic types in preoperative diagnosis,it could cause over-treament and thus detrimental effect for prognosis.This study tried to identify biomarkers for the differential diagnosis of SPNs and malignant pancreatic tumours by examining the gene regulatory network of SPNs.In the study,we constructed gene regulatory network for SPNs by co-expression model.Then genes that have been reported to be correlated with SPNs were identified by data mining.According to the shortest path approach,more SPN-related genes were found.By means of the KNN classifier evaluated by the jackknife test,sets of genes to distinguish SPNs and malignant pancreatic tumours were determined.By analyzing shortest paths for previously reported SPN-related genes in gene regulatory network,we identified 43 additional SPN-relevant genes.By means of the KNN classifier evaluated by the jackknife test,we found miR-194 and miR-7 along with 7 transcription factors such as SOX11,SMAD3 and SOX4 could correctly differentiate SPNs from pancreatic neuroendocrine tumours,while for SPNs vs ductal adenocarcinomas,miR-204 and 4 transcription factors MAFG,SOX9,TCF7 and PPARD were uncovered.These results provide an effective basis for further research of molecular pathology and clinical diagnosis of disease. |
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