Bioinformatics Research On The Molecular Mechanism Of Glioma Formation And Progression

Objectives: 1.Whole exon sequencing results of multi-point samples taken during the operation constructs a new glioblastoma(GBM)gene mutation map,and detected out that GBM driver gene mutations that are not easily found in a database constructed by traditional single-point sequencing.Through the analysis of tumor mutation phylogenetic tree,the key evolution path of GBM mutation was found,which provided the direction of thinking for new targeted therapeutic schemes.2.Using lnc RNA,m RNA,mi RNA sequencing and array databases from CGGA,and combining Target Scan,Mi Randa and Pic Tar three databases for mi RNA target gene prediction,construct a brand-new ce RNA network to explore the core gene.The multidimensional gene interaction network of glioma occurrence and progress provides a new perspective for exploring the molecular mechanism of glioma.3.Using exon sequencing and ce RNA bioanalysis results,and combine with MPC nanocapsules for delivering targeted therapeutic drugs in order to improve the therapeutic effect of clinical drugs or expand the range of available drugs.Methods: 1.Intraoperative multi-point and multi-position sampling was performed for GBM patients,and total exon sequencing was performed for samples of each site.Combined with the sequencing results of blood cell DNA,somatic cell mutation sequencing results were obtained.2.Somatic INDEL locus was detected by Strelka software and annotated by Annovar software.Somatic CNV and SV in paired tumor and normal tissue samples were detected using control-freec and lumpy software,and a new GBM somatic mutation map was drawn.3.Mu Si C software was used to analyze high-frequency mutated genes in tumors,and KEGG,PID and Reactome database were used to annotate gene function of high-frequency mutated genes.4.The somatic mutations were compared with the three known tumor driver gene databases,CGC513,Bert Vogelstein125 and SMG127,to screen the known driver genes in the tumor samples,and a new GBM tumor driver gene mutation map was drawn.5.According to Vaf(variant allele frequency)and copy number variation of Somatic site,Pyclone software was used to calculate CCF(cancer cell fraction)of tumor mutant cells to study clone structure.According to CCF obtained,Clon Evol software was used to analyze the evolutionary relationship between tumor samples.6.The glioma ce RNA interaction network was constructed to identify the genes at the center of the network and most closely related to the interaction with other genes.In vitro cytological experiments and in vivo animal experiments were used to verify the specific effect of core genes on tumor development and progression.Results: 1.The multi-point sampling to obtain somatic cell mutation map is more comprehensive than the traditional sampling method,and gene mutations that only appear in some tumor regions due to tumor heterogeneity can be found.2.MUC16,the gene with the highest mutation frequency in the new gene mutation map,predicted the good prognosis of GBM patients after standard treatment.3.Evolutionary tree analysis results suggest that EGFR p.l.861 q mutation plays an important role in the evolution of primary GBM to recurrence.4.Ct DNA in the blood can predict the overall genetic information of GBM.5.Long non-coding RNA HERC2P2 is at the core of the ce RNA network constructed by lnc RNA,mi RNA and m RNA.6.HERC2P2 is positively correlated with the expression of many genes in the ce RNA network,and has the function of indicating tumor base level and high survival rate.7.The overexpression of HERC2P2 attenuated the migration and colony formation of U87 and N33 glioma cells,and inhibited the growth of glioma in vivo.Conclusions: 1.Multi-point sampling sequencing can provide more comprehensive information of GBM gene mutation.2.MUC16 gene mutation can predict better prognosis of GBM patients.3.Based on the mutation information of tumor samples,phylogenetic tree calculations can be used to infer the originating factors that cause tumorigenesis.4.Constructing a network of interactions between genes based on multiomics information can better understand the mechanisms of glioma occurrence and malignant progression.3.HERC2P2,the core gene of Ce RNA network,can inhibit the development of glioma.