| Immune diseases,such as cancer,are complex systemic diseases in nature.Systems biology,which has emerged in recent years,is a discipline that attempts to understand the complex behavior at the systemic level.There are two general methods used in systems biology.One of them is a "top-down" approach which focuses on the correlations of experimental data with possible biological patterns.In contrast,the second one is a "bottom-up" approach that constructs models from the characteristics of the molecules and microbiology themselves.It attempts to infer the behavior of the system based on model studies.Experimentally,the integration of emerging technologies such as deep sequencing of DNA and RNA,mass spectrometry proteomics,metabolomics,and statistical methods allow a glimpse into the dynamical states of the complex system of human cells.The correlation networks so obtained help accelerate the discovery of the overall inner workings of the system.The constructions of comprehensible models for cancers further provide better understanding of the system’s behavior over time.In practical applications,the two methods of systems biology are complementary,rather than mutually exclusive.This study combines both approaches.We use statistical methods to analyze sequencing data and to obtain correlated genes and molecular pathways.The correlation results are then incorporated into the bottom-up modeling studies of complex systems.The combination fulfills a primary goal of our research which aims for two-way intervention,correlation guiding causality and the causality in turn verifying the correlation.Specifically,it appears that tumor development is a typical systemic progress with nonlinear internal interactions among components of the organism.This study builds on the correlations from statistical analysis and then constructs the endogenous network of tumors from a systemic and quantitative perspective and performs kinetic simulations.Current therapeutic agents for immune diseases are often broad-based and may cause many side effects,while immunotherapy for tumors can also incur many clinical uncertainties.These stem from the fact that the drugs are not engineered to disease-specific targets.The latter possess as an important challenge for improving treatment.The present study focuses on the relationship between immune diseases and signaling pathways.Firstly,the sequencing data of thymoma is analyzed by statistical methods,the core T-cell receptor signaling pathway of thymoma is identified,and 14 immune hub genes closely related to thymoma are identified.Also,we analyze the relationship between hub genes and thymoma immune paraneoplastic syndrome(PNS),and determine that defective expression of CD247 and reduced numbers of mature T cells are common features of thymoma,myasthenia gravis,idiopathic pulmonary fibrosis,rheumatoid arthritis,and systemic lupus erythematosus.Then,the endogenous molecular cellular network of thymoma is constructed by combining the basic assumptions of the endogenous network and the biochemical knowledge that has been well studied,while incorporating the core pathways and genes identified in the previous step.The tumor status of type A and B1/2/3 thymoma is reproduced by kinetic simulation.Meanwhile,the pivotal position and mechanism of action of thymoma-specific core pathway are validated,and CD247 is predicted to be an important target for thymoma TCR immunotherapy.Furthermore,the pivotal position and action mechanism of thymoma core pathway are verified,the transformation path between different thymomas is predicted.In addition,leukemia is also a disease of abnormal immune cells,and there have been many reports that leukemia may also be a PNS of thymoma.In particular,the incidence of acute myeloid leukemia(AML)has increased significantly in recent years with a poor prognosis.So we try to extract the core information structure from the high-dimensional data and explain the core mechanism of AML at a lower dimensional level.The specific core pathway of AML is identified as G alpha(i)signalling events by separate analysis of peripheral blood and bone marrow data in AML.The c AMP signaling pathway,downstream of the action of G alpha(i),has been extensively studied for a variety of pharmaceutical targets.g alpha(i)and its downstream pathways may provide new target prediction for pathway therapies in AML.In further work,it is expected that kinetic modeling will be used to validate this pathway and predict pharmaceutical targets in the pathway more precisely.The most important results of this paper are the identification of the TCR signaling pathway as a specific signaling pathway in thymoma,further construction of the endogenous network in thymoma and discussion of its kinetic properties.Kinetic simulations were used to validate the importance of the TCR pathway in thymoma,and to predict CD247 as an important target for TCR therapy in thymoma.And G alpha(i)signalling events is a specific pathway for AML,which provides new guidance for pathway therapy in AML.To summarize,we would like to re-emphasize the innovative aspects of this thesis.We have successfully combined the correlation studies with causality studies to systematically and quantitatively validate and describe cancers.This study appears promising and may provide a new perspective to understand cancer diseases. |
