Human Glioma Proteomics And Transcriptomics Data Analysis And Database System Construction

Glioma is one of the most common malignant brain tumors in adults and is classified to astrocytomas,oligodendrogliomas,and mixed gliomas.The pathological grades are grades ? to ?.Glioblastoma multiforme(GBM)is grade IV and it is the most common and malignant glioma.The diagnosis of glioma is traditionally based on histological identification,but studies on the molecular characteristics of gliomas are expected to provide new ideas for new diagnostic treatments.With the development of high-throughput technology,genomics,transcriptomics,proteomics and other omics methods have gradually been applied to the study of the molecular mechanism of glioma.Proteomics analysis focuses on proteins which play a direct role in almost all biological processes.With the development of technology and further research,more and more proteomics data have been accumulated.Therefore,it is necessary to develop a technology platform to standardize management storage,annotation,analysis and visualization of proteomics data.This research falls into two parts.First,we conducted bioinformatics analysis on human glioma proteomics data and further identification of possible biomarkers in combination with transcriptome data;Second,we established a human glioma proteomics database system,which can be used for data standardized management,retrieval,visualization and online analysis.As for the first part,we focused on the molecular differences between GBM(grade IV)and LGA(Grade ?),in order to discover the mechanisms related to tumor development.We used proteomics mass spectrometry followed by iTRAQ labeling quantification experiments to obtain the values of protein expression in LGA and GBM.The proteins with significant fold change were selected to do Gene Ontology enrichment anaylsis and pathway enrichment analysis.Combining the RNA-seq data of LGA and GBM in the TCGA database,we used the strategy of combining transcriptome and proteome to further determine the key genes involved in the development of gliomas and provide valuable insights into important biomarkers for the development of gliomas.The results showed that a total of 3226 proteins were identified in the proteome experiments,of which 42 proteins showed significant differences in the two samples.There are 22 up-regulated proteins and 20 down-regulated proteins in the GBM samples.In the transcript data,1002 differentially expressed genes were found,of which 456 were up-regulated and 546 were down-regulated.By analyzing the results,we found that proteomics and transcriptomics can obtain 13 common genes.Conclusion:BST2,HLA-DRB1 and PSMB9 which can promote immune response were up-regulated in our study.SEZ6L,PLP1,ERMN and MOG associated with myelination were down-regulated in our study,suggesting that the malignant progression of glioma maybe enhancing the immune response,affect the physiological function of myelin.This study provided new ideas for follow-up research and provided support for the early diagnosis and treatment of diseases.In order to explore the methodology for managing and utilizing massive proteomics data effectively,we built the Human Glioma Proteome Database System(hgPDS,http://hgPDS.bmicc.cn).The system can be used for data standardized management,retrieval,visualization and online analysis.To achieve effective management of human glioma proteomic metadata and experimental data,we developed metadata standards and terminology standards for this system first.The sample and experimental metadata collection conforms to the guidelines in Minimum Information About a Proteomics Experiment(MIAPE)and PSI-MS control vocabularies by Proteome Standard Initiatives(PSI).The clinical metadata are standardized with National Cancer Institute Thesaurus(NCIT).Then based on this data standard,a data model was developed.The data schema is modulized to manage different data types,such as metadata from clinical record,sample and experiment,as well as those of experimental raw and annotated data.The hgPDS is built by using Bootstrap,Java and MySQL technology.Bioinformatics analysis functions are achieved by R.Online data analyses include cluster heatmap;Gene Ontology enrichment and pathway enrichment.In summary,hgPDS achieves the functions of standardization,efficient management,annotation,visualization,and bioinformatics data of glioma proteomics data,helping scientists to analyse data,simplifying manual operations,and improving research efficiency.The system is practical,expandable,easy to be access and easy to be maintain.In this study,we conducted proteomic and transcriptomic research between GBM and LGA.We identified 13 genes maybe closely related to the malignant mechanism of glioma.And we found that three of the up-regulated genes were involved in the immune response and four of the down-regulated genes affected the growth and development of the myelin sheath of neurons.We also analyzed the pathogenic molecular mechanism.And then we established the human glioma proteome database system,which provides standardized data storage and provides tools for target data retrieval and proteome bioinformatics analysis.