Quantitative Detection Of Glioma Stem Cell Components Based On Transcriptome Data

Background:Glioblastoma(GBM)is the most common invasive brain tumor with a high degree of malignancy(WHO Ⅳ).At present,the standard treatment plan is surgery supplemented with radiotherapy and chemotherapy.About 90%of patients undergo relapse within 8 months after treatment.After recurrence,anti-angiogenic therapy is mainly used.And the average survival is less than 7 months.Rapid recurrence after surgery and adjuvant treatment is the main cause of its high mortality.A large number of studies have shown that glioma stem cell(GSC)play an important role in the resistance and recurrence of GBM treatment.Quantitative analysis of fraction of GSC(components)is an important basis for the research and application of targeted GSC.In addition,the genetic basis of the differences in GSC components remains unclear,which needs to be clarified.Methods:This study was based on transcription data of GSC cell lines,glioma cell lines,normal brain tissue samples and various types of normal cell samples for differential expression analysis.The expression characteristics of GSC were identified,and the analysis method of GSC component was constructed and verified.We collected transcriptome data and clinical data of 7 sets of GBM tumor tissue samples.And the relationship between GSC components and prognosis of GBM patients was explored by survival analysis.At the same time,we focused on deconstructing tumor microenvironment heterogeneity and intra-tumor heterogeneity in GBM patients with different GSC components.By constructing the co-expression network and protein interaction network of GSC marker genes,Metascape and GSEA were used for functional analysis.We found that potential miRNA targets of GSC marker genes can be regulated.Finally,based on the data of multiple groups,we systematically explored the differences of genetic and epigenetic in different GSC component samples.Results:After differential expression analysis and characteristic gene screening,15 markers for quantitative detection of GSC components were constructed and verified.The GSC component analysis showed that there were significant differences in different GBM patients.It is suggesting that there is a GSC heterogeneity between the tumors.The results of survival analysis show that GSC components can effectively predict the clinical prognosis of patients with GBM.The survival time of GBM patients with high GSC components was significantly lower than that of patients with low GSC components(TCGA Log Rank P value=0.0306;GSE16011 Log Rank P value=4.29e05).It’s suggesting that there is an intrinsic link between GSC components and GBM malignancy.We found that GSC components were negatively correlated with tumor purity and positive correlation with immune cell components.Further studies showed that the fraction of macrophages in the high GSC component samples was significantly higher than the low GSC component samples.And it was significantly associated with the poor prognosis of patients(P value=9.33e-10).It is suggested that GSC and immune microenvironment interact with each other to synergize the effect of treatment resistance,tumor recurrence and metastasis.The intratumoral heterogeneity analysis showed that the area of infiltrating tumor was rich in non-tumor cells and GSC.And the GSC component in the area of core tumor was significantly lower than the area of infiltrating tumor(P value=0.0478).In addition,the results of network analysis and function analysis show that the GSC markers mainly involved in many biological processes of cancer,such as inflammatory reaction,angiogenesis,hypoxia,epithelial mesenchymal transformation,DNA repair,cell cycle regulation and the regulation of cell differentiation.MiRNet was used to identify the miRNA regulating GSC markers which provided a potential target for targeted GSC therapy.Finally,we found there were significant genetic and epigenetic differences between different GSC components samples.Conclusions:In this study,we identified the expression markers of GSC,which can quantitatively detect the GSC components and can effectively predict the prognosis.Subsequent analysis showed that different GSC component samples had differences in immune microenvironment heterogeneity,intratumoral heterogeneity,subtype of molecular subtype,genetic and t epigenetic.