Pan-cancer Guiding Glioma Mesenchymal Subtype Reclassification According To Driving Mechanisms And Choice Of Treatment Strategy

Introduction:Glioma is the most common malignant tumor in the central nervous system,accounting for more than 50%of the primary tumors of the central nervous system,and has the characteristics of high mortality and high recurrence rate.It is of great urgency to find new treatment strategies.Although the diagnosis and treatment concepts and methods for glioma have been improving,the median survival time of patients with glioblastoma is only 15 months.Gliomas can be divided into four types according to their characteristic gene expression:neural type(NE),classical type(CL),preneuronal type(PN)and mesenchymal type(MES).MES glioma has the characteristics of high invasiveness,resistance to treatment,easy recurrence and poor prognosis.Studies have shown that glioma is revolutional,which can be transformed from one type to another.Mesenchymal transformation is often related to treatment resistance and malignant progression.Therefore,exploring the driving mechanism and driving genes of mesenchymal transition is of great significance for the treatment of glioma.Preliminary studies believe that this process may be involved in the microenvironment,inflammatory factors,and epigenetic factors,but the specific mechanism still needs to be further explored in detail.At present,cancer research has entered into the pan-cancer era.Well-known cancer research projects including TCGA and ICGC have begun to study cancer from the perspective of pan-cancer.This work will study the mesenchymal transition of glioma under the background of pan-cancer scale.Methods:(1)Database information and tissue specimens:The data used for bioinformatics analysis was obtained from TCGA,CGGA,GEO,GDSC and related article.We selected the TCGA(The Cancer Genome Atlas)data platform as the main analysis database and selected other data platforms as external verification database.The tissue specimens required for molecular biology experiment verification were obtained by our department with the informed consent of the patient and their family members.The relevant experimental plan was reviewed and approved by the ethics committee of the First Affiliated Hospital of China Medical University.(2)Bioinformatics analysis method:Concensus cluster was used to classify the tumor mesenchymal transition state;principal component analysis(PCA)was used to analyze the clustering effect;differential gene screening was done using Limma R Package and was visualized using the Metascape database Cytoscape software;Gene Set Enrichment Analysis(GSEA)was used to further verify the enriched differential functions and pathways;ss GSEA was used to evaluate single-sample pathway activity;Estimate R package was used to calculate the purity,immune score and stromal score of the tumor sample;the Cibersort R package was used to calculate the relative content of each non-tumor cell component in the sample;machine learning method was used to reclassify individual samples.(3)Cell line model:human glioma cell lines LN229,U87,U251,macrophage cell line THP-1,human breast cancer cell lines MDA-231,MDA-468 were purchased from the Shanghai Cell Bank of the Chinese Academy of Sciences;human glioma cell line T98and mouse glioma cell line GL261 were purchased from ATCC;the primary human glioma cell line was extracted by the author's team;the spontaneous murine glioma cell line was constructed through the sleeping beauty transposon system.(4)Molecular biology experiment:q PCR,WB,immunohistochemistry and immunofluorescence were used to verify molecular expression;Transwell experiment was used to detect the invasion and migration ability of glioma cells/macrophages;MTS is used to determine cell proliferation viability;Intracranial xenograft model was used for in vivo testing of phenotype or mechanism;DCFH-DA kit was used to detect the content of reactive oxygen species.(5)Flow cytometry and Cy TOF experiments:Flow cytometry related experiments were used for immune microenvironment analysis.The flow cytometry experiment was completed by our team,and the Cy TOF was done by Novogene Co.,Ltd(Beijing,China).(6)Statistical analysis:Chi-square analysis,analysis of variance,and independent sample t-test were used to investigate whether there are differences between grouping variables.Cox regression analysis was used to determine whether the factors or targets affect the survival and prognosis of patients.The Kaplan-Meier curve was used to describe the survival status of patients,and the Log-rank test was performed to compare the differences in survival status between patients in different groups.The correlation analysis was completed by Pearson test.Graph Pad Prism 7 and R3.6.1 were used to complete the above related analysis.FDR correction was used to test the p-value to eliminate false positive results.The two-tailed p value or FDR value less than 0.05 was defined as the standard for the difference of statistical significance.Results:First partUsing protein interaction and gene co-expression analysis,a gene set containing 35 genes was constructed and used to classify 9,415 patients of 24 types of epithelial cancers types based on the transcriptomic profile in the TCGA data set.Four subtypes were established according to the mesenchymal transition status(MT^Low,MT^Mid,MT^High-YAP and MT^High-AKT).The evaluation of the different mechanisms of the mesenchymal transition between the two MT^Highsubtypes showed that MT^High-YAP is driven by several classic somatic changes(such as FAT1).FAT1 inhibited the Hippo pathway and therefore promoted YAP nuclear translocation.The FAT1 mutation,the deletion of CDKN2A and the MYC mutation together led to abnormal cell cycle in MT^High-YAP type.In addition,the mesenchymal state of MT^High-AKT type was maintained by interacting with infiltrating macrophages in an AKT-dependent manner.In addition,the interaction between tumor cells and macrophages also led to the immunosuppressive phenotype of the MT^High-AKT type which was unlikely to benefit from immune checkpoint blocking therapy.In vivo experiments proved that inhibiting the AKT pathway was an effective method for improving immune checkpoint blocking therapy response.Furthermore,Tumor MT(http://tumormt.neuroscience.org.cn/),a freely available website,was developed for mesenchymal classification.CGGA GBM dataset was used to test the accuracy of Tumor MT.Second partUsing in vitro and in vivo functional experiments,we found that CLCF1 was related to the malignant behavior of gliomas,and might be involved in the chemotaxis of macrophages.Through gene enrichment analysis,we found that the metabolism of reactive oxygen species in MT^High-AKT type was significantly increased.Using five dimensional ROS metabolic evaluating system,we found the content of reactive oxygen species in glioma tissue was significantly higher than that in other tumor tissues.Using DCFH-DA kit,we found that macrophages have the highest reactive oxygen content in the microenvironment of gliomas,thus proposing the mechanism of macrophages/reactive oxygen species/glioma-AKT/CLCF1.We confirmed that CLCF1acted mainly through the NF-KB pathway.CLCF1 could promote the phosphorylation and nucleic import of NF-KB.Finally,we confirmed the combination value of NK-KB inhibition with ROS modulation.Conclusion:First partThrough pan-cancer analysis,we confirmed that tumors had different states of mesenchymal transformation and their driving mechanisms were different.The mesenchymal state of tumors should be fully considered for accurate classification and treatment.Second partCLCF1 was a key cytokine that mediated the interaction between tumors and macrophages,which could promote the malignant behavior of gliomas and the chemotaxis of macrophages.The development of targeted therapy for CLCF1 had certain significance for the treatment of glioma.