Identification of synergistic strategies for m6A methylation-involved immunotherapy enhancement in lower grade gliomas

Identification of synergistic strategies for m6A methylation-involved immunotherapy enhancement in lower grade gliomas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of synergistic strategies for m 6 A methylation-involved immunotherapy enhancement in lower grade gliomas Qing Han, Huimin He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1519458/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Low grade gliomas (LGGs) are often noted for their unpredictable recurrence and transformation of malignancy to higher grade. Due to the extraordinary immunosuppressive microenvironment, LGGs seem to be refractory to T-cell based immunotherapies. Targeting RNA N6-methyladenosine (m 6 A) modulated tumor microenvironment (TME) offers opportunities to trigger anti-LGG immunity. In our study, m 6 Ascore were used for quantifying the m 6 A modification patterns in LGGs. Potential therapeutic drugs and synergistic immunotherapy approaches which might avoid overtreatment and contribute to the TME remodeling were suggested based on the scoring scheme. We found a link between m 6 Ascore and TME diversity. Those patients with high m 6 Ascore and worse outcomes were more likely to experience immunotherapeutic failures. Inhibition of IL-6/JAK/STAT3 signaling that closely correlated with the drug sensitivities was indicated to facilitate the response of LGGs with high-m 6 Ascore instead of overall LGGs to immune checkpoint blockade (ICB). Mechanically, stimulation of CD40 or TNFSF9 might reverse IL-6 inhibition induced insufficient activation of T cells by macrophages. In conclusion, synergistic immunotherapy of anti-IL-6 and ICB may offer insight into the m 6 A related immunotherapy enhancement for LGG patients. Immunology Lower grade gliomas m6A Immunotherapy IL-6 macrophages Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Lower grade gliomas (LGGs), a sort of primary brain and CNS tumor, are taken to denote both World Health Organization grades II and III gliomas 1 . While some advances have been made in imaging 2 and treatment 3 of LGGs, the outcomes of the patients still remain unsatisfactory and a considerable proportion of them would experience the recurrence and transformation of malignancy to higher grade 4 . N6-methyladenosine, namely m 6 A, is a modified base which has been known to present in ribosomal RNA, noncoding RNAs, polyadenylated RNA and mammalian mRNA 5 . To form m 6 A methylation in mRNA accounts is considered to be the most abundant and vital mRNA internal modification which has emerged as an extensively gene expression-modulation mechanism in diverse physiological processes 6 – 8 . m 6 A can be installed by methyltransferases termed "writers", removed by demethylases termed "erasers", and recognized by specific RNA-binding proteins termed "readers" 9 . Increasing evidences have demonstrated the crucial impacts of m 6 A methylation modification on the maintenance of tumor cell stemness 10 , DNA damage response 11 , metastasis 12 and drug resistance 13 , et al . Nevertheless, the roles of m 6 A RNA methylation in LGGs still remain unexplored. Circumvent immune recognition is a hallmark of tumor 14 . It can occur through various mechanisms including tumor cell alterations that decrease immune recognition 15 – 17 and increase resistance to immunity cytotoxic effects 18 , 19 as well as the establishment of the immunosuppressive microenvironment 20 – 22 . The significance of non-neoplastic stromal cells in the development of LGGs has been gradually recognized 23 , 24 . Considering its extraordinary immunosuppressive microenvironment, LGGs seem to be refractory to immunotherapies based on T cells including checkpoint inhibition and chimeric antigen receptor T cell transfer 25 . This highlights the urgent need to identify novel combination strategies to trigger anti-LGG immunity. Present studies have revealed the effects of m 6 A RNA methylation on TME modification 26 – 28 . Here, we systematically correlated the m 6 A modification phenotypes with genomic and clinical characteristics of LGGs and established a novel scoring scheme to quantify the m 6 A methylation modification patterns for individual LGG patients. Based on the scoring scheme, potential therapeutic drugs were prepared for TME remodeling related pathway analysis. We found that IL-6/JAK/STAT3 signaling inhibition had the potential to sensitize those patients with high m 6 Ascore to immune checkpoint blockade (ICB), and suggested the stimulation of CD40 or TNFSF9 might reverse IL-6 inhibition induced insufficient activation of T cells by macrophages (Mϕs). Overall, our research provides possible strategies for overcoming the resistance to chemotherapy and immunotherapy under the specific m 6 A modification pattern for LGG patients. Material And Methods Acquisition and processing of low grade glioma datasets Public attainable expression profiles and the corresponding clinical annotation were obtained from the Cancer Genome Atlas (TCGA) database ( https://cancergenome.nih.gov/ ), the Chinese Glioma Genome Atlas (CGGA) database ( http://cgga.org.cn/ ) and Gene Expression Omnibus database (GSE151213). A total of 1040 LGG patients and mouse bone marrow-derived Mϕs treated with IL-6 were enrolled in the study, including those from TCGA-LGG cohort (n = 457), CCGA_mRNA_seq325 (n = 182), CGGA_mRNA_seq693 (n = 401) and GSE151213. Batch effects of raw data from CGGA were processed by the “sva” package. Then, FPKM values in data from TCGA, CGGA and GEO were transformed into transcripts per kilobase million (TPM) format for further analysis. Phenotype Classification Unsupervised clustering analysis was performed to identify m 6 A methylation modification phenotypes based on the expression of 24 m 6 A regulators and m 6 A-modulated phenotypes based on the expression of perturbation genes through “ConsensuClusterPlus” package 29 . Functional annotation To investigate the contrasts on biological process among m 6 A methylation modification phenotypes, GSVA enrichment analysis 30 was utilized using “GSVA” packages. The hallmark gene sets were obtained from MSigDB database. Gene Ontology (GO) analysis on m 6 A signature genes was performed via “clusterProfiler” package 31 , which was also used to carry out “GSEA” analysis 32 to determine pathways up- and downregulated between LGG-m 6 Ascore high and low groups. Adjusted p values less than 0.05 were held to be statistically significant. Exploration of gene expression perturbation among m6A phenotypes To identify genes correlated with m 6 A modification patterns, LGG patients were grouped into m 6 A clusters. Differently expression genes (DEGs) among three clusters were determined using the “DEseq2” package 33 . The counts data of TCGA-LGG were downloaded from UCSC XENA ( https://xenabrowser.net/ ). The significance criteria for gene filtering were set as log2FC > 1 while adjusted p < 0.05. Tumor microenvironment estimation and immune response prediction The infiltration abundance of each cell in the tumor microenvironment (TME) was quantified by ssGSEA algorithm. The gene sets marked immune cell types were achieved from the research of Charoentong 34 . CIBERSORT, a deconvolution algorithm was performed to infer cell type proportions in bulk tumor samples 35 . In addition, we used the ESTIMATE algorithm to predict immune and stromal cells infiltration and further infer tumor purity 36 . Finally, TIDE algorithm was utilized to quantify the tumor immune evasion patterns in tumor samples and predict immunotherapy benefits of each patient 37 . Generation of m 6 A methylation modification signature To quantify the m 6 A modification patterns in individual samples, a scoring system termed m6Ascore was contrasted as following procedures: The DEGs among different m 6 A clusters were identified for the overlap gene extract. Then, the LGG patients were stratified for deeper analysis by the overlap DEGs using unsupervised clustering method. After that, principal component analysis (PCA) was constructed to generate m 6 A modification signature. Both principal component 1 and component 2 were considered to serve as signature scores. The advantage of this method focuses the score on the largest block of well-correlated/ inverse-correlated genes in the set, while the contributions of genes which little track with other members were weighted down. Finally, we adopted a former formula to define the m6Ascore of each LGG patient 38 , 39 : m6Ascore= ∑(PC1i + PC2i), where i is the gene expression in the signature. Acquisition and processing of drug sensitivity data Expression profile data from human cancer cell lines were obtained from the Broad Institute Cancer Cell Line Encyclopedia (CCLE) project ( https://portals.broadinstitute.org/ccle/ ) for working as the training set. Drug sensitivity data was achieved from the Cancer Therapeutics Response Portal (CTRP) and Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset. Both two datasets provide AUC (the area under the curve) values as a drug sensitivity measure. Compounds with more than one fifth of missing data were removed, while K nearest neighbor (k-NN) imputation was used for imputing the missing values. We identified the candidate compounds with different AUC values between LGG-m 6 Ascore groups (log2FC > 0.1). Spearman correlation analysis was adopted to estimate the relationship between AUC value and m 6 Ascore (Spearman’s r < − 0.30). Statistical analysis R-4.0.2 was employed for statistical analyses. For quantified data, statistical significance between normally distributed variables was calculated by Student’s t-tests, while that between non-normally distributed variables was checked by the Wilcoxon test. Chi-square test or Fisher exact test was used to exam correlation between two categorical variables. For survival analysis, Kaplan-Meier curves and the Cox proportional hazards models were performed by ‘Survminer’ package. The receiver operating characteristic (ROC) curves was utilized to verify the prognostic prediction performance via ‘timeROC’ package. Statistical significance was corresponded to p values: ns > 0.05, * <0.05, ** <0.01, *** <0.001. Results Construction of m6A methylation signatures in LGGs The structural framework of this study was depicted ( Fig. 1 ). To systematically investigate the m 6 A modification patterns, a total of 24 m 6 A regulators (the landscape of genetic alterations and prognostic analysis were shown in Figure S1) were enrolled for stratifying TCGA-LGG patients, among which IGF2BPs were significantly associated to the patients’ prognosis (Fig. 2 A, Figure S2A-G ). Those patients with high immune infiltration were observed to have poor prognosis (Fig. 2 B-D). Then, we tested known signatures to better describe the function of m 6 A methylation modification in LGG, and confirmed its important regulatory roles in immune, stromal as well as mismatch repair (Fig. 2 E). To learn more about the biological behaviors among the distinct m 6 A modification phenotypes, GSVA analysis was performed. The phenotypes with poor prognosis related to pathways enriched in epithelial-mesenchymal transition, mismatch repair and cytokine-related pathway (Fig. 2 F). After that, we used the ESTIMATE algorithm to assess the tumor purity levels which was significantly higher in Cluster C (Fig. 2 G). CIBERSORT was also used to evaluate the fraction of TME cells among the m 6 A clusters (Fig. 2 H). In addition, LGGs with wild type IDH were mainly clustered into Cluster B&C, while IDH mutant samples were closely relevant to Cluster A (Fig. 2 I). Finally, we determined 215 overlapping m 6 A phenotype-related DEGs as the m 6 A gene signature in LGGs (Fig. 2 J). To further investigate the clinical traits and potential biological behavior of the m 6 A modification signature, unsupervised clustering analyses was performed to identify three typical subtypes termed ‘Gene cluster’ (Fig. 3 A). Overall survival analysis and progression free survival analysis revealed that patients in gene cluster B experienced the outcomes of poor prognosis (Fig. 3 B-C). 24 m 6 A regulators involved in this study showed significant expression perturbations among the three gene clusters (Fig. 3 D). A significant higher infraction level of TME cells and elevated expression level of immune-checkpoints were indicated in the gene cluster B (Fig. 3 E-F). In addition, we also evaluated the enrichment of mismatch repair and stromal relevant signatures which were remarkably difference among the three gene clusters (Fig. 3 G). Furthermore, functional annotations on the genes in the m 6 A gene signature were carried out. The results showed that the m6A-related genes were widely involved in the maintenance of tumor stemness, radiotherapy, proliferation and metabolism and immunity (Fig. 3 H). Quantifying the m6A methylation modification in an independent individual To quantifying the m 6 A methylation modification in an independent individual, we generated a scoring scheme termed “m 6 Ascore” based on the m 6 A signature via the PCA algorithm. The attribute changes of LGG patients were illustrated by an alluvial diagram (Fig. 4 A). m 6 Ascore was positively correlated with the expression levels of CD8 + T effectors and immune checkpoints (Fig. 4 B), which suggested the pre-existence of immune response in the tumors with high m 6 Ascore and then followed by T-cell exhaustion. Moreover, the m 6 Ascore was markedly positively correlated with EMT signatures but negatively correlated with stromal relevant signatures. The results of GSEA revealed the differentially enriched pathways between the m 6 Ascore groups, such as “MAPK cascade”, “ERK1/2 cascades” and “JAK/STAT signaling” (Fig. 4 C). After that, we analyzed the somatic mutation differences between high- and low-m 6 Ascore groups in TCGA-LGG cohort (Fig. 4 D). The genes with significantly different distributions among the genes with TOP10 mutant frequency in the TCGA-LGG patients were shown in Figure S5. Then, we analyzed the clinical and molecular features between the LGG-m 6 Ascore groups. The significant distinct distributions of primary location, grade, age and molecular subtypes were observed between the m 6 Ascore groups. Patients with high m 6 score were remarkably associated with worse prognosis (Fig. 4 F-H). What’s more, an increasing number of studies have revealed the significant roles of TME in the development of LGGs 40 and the feasibility of immunotherapy in brain tumors 41 , 42 . We assessed the benefit of immune checkpoint blockage (ICB) therapy. Those patients with high m 6 Ascore and worse prognosis also predicted to benefit less from ICB therapy (Fig. 4 I-J). Thus, it is urgent for LGG patients to identify ICB sensitization strategies. Identification of the key m 6 A relevant signaling pathway for immunotherapy enhancement Instead of merely analyzing gene expression perturbation, we expected to obtain the sensitive drug compounds applied in high-m 6 Ascore LGGs for TME remodeling as well as their associated pathways which might contribute to ICB sensitization. We first identified drug candidates with greater sensitivity to high LGG-m 6 Ascore patients 43 . The analyses were based on the drug response data of CTRP 44 – 46 and PRISM, including 1,639 compounds. Totally 16 compounds were yielded with higher sensitivity by the analysis, while the most widespread chemotherapy for glioma patients, temozolomide, was detected to had lower sensitivity in high m 6 Ascore patients (Fig. 5 A-C). Afterwards, correlation analysis was adopted to select TAK-733, clofarabine, dasatinib and birinapant as the optimal therapeutic agents to those LGGs the immune suppressed microenvironment (Fig. 5 D-E). It was found that the sensitivities of LGGs to these four candidates were all positively correlated with the enrichment of IL-6/JAK/STAT3 signaling (Fig. 5 F). Together, our data revealed that IL-6/JAK/STAT3 might be a potential therapeutic targeting to trigger anti-LGG immunity to avoid ineffective overtreatment. Inhibition of IL-6/JAK/STAT3 signaling sensitized LGGs with high m 6 Ascore to immune checkpoint blockade We first evaluated the relationship between IL-6/JAK/STAT3 signaling activation and prognosis. Higher enrichment of IL-6/JAK/STAT3 signaling was associated with the worse prognosis (Fig. 6 A-B). Assessing the ICB treatment benefits, however, we did not observe less enrichment of IL-6/JAK/STAT3 signaling in the ICB-benefit group compared with non-benefit group in overall patients (Fig. 6 C). Therefore, we stratified TCGA-LGG patients. It was found that IL-6/JAK/STAT3 signaling was lower enriched in the predicted ICB-beneficial patients with the high LGG-m 6 Ascore, while this pathway was significantly enriched in ICB-beneficial patients with median and low m 6 Ascore (Fig. 6 D). Similarly, highly enriched IL-6/JAK/STAT3 signaling associated with poor outcomes (Fig. 6 E) was observed in ICB non-benefit LGGs with high m 6 Ascore, while there was no significant difference in the overall CGGA-LGG patients (Fig. 6 F-G). Tumor-associated Mϕs have a pivotal role in cancer immunosuppression and therapy resistance 24 , 47 , 48 . We first evaluated the infiltration level and polarization of Mϕs in different m 6 Ascore groups. We found that high-m 6 Ascore group had an equal level of M2 macrophages and higher level of M0 and M1 macrophages compared with the other groups, albeit, the expression level of IL-6 which contributed to the macrophage infiltration and polarization was higher in high-m 6 Ascore group ( Fig. 6 H-I ) . IL-6 neutralization has been proved to moderately enhance infiltration of CD3 + CD8 + T cells into the tumors but do not activate these T cells, and reduced recruitment of CD45 + CD11b + F4/80 + Mϕs 49 . We therefore speculated that IL-6 neutralization might lead to insufficient activation of T cells by Mϕs, and perhaps there are certain mechanisms in protecting IL-6 downstream from IL-6 inhibition in the patients with high m 6 Ascore. RNA-seq data revealed that IL-6 could stimulate the expression of the members of tumor necrosis factor (TNF) superfamilies (cd40, tnfsf4, tnfsf9, tnfsf14, tnfsf15) in mouse bone marrow-derived Mϕs ( Fig. 6 J-L ) . We ranked TCGA and CGGA samples according to their IL-6 expression levels from low to high, selected the LGGs with the lowest IL-6 expression, and ensured that there was no significant difference in IL-6 expression levels between the m 6 Ascore groups (Fig. 6 M). We were surprised to find that CD40 and TNFSF9 expression were dramatically higher in high-m 6 Ascore group than in other groups (Fig. 6 N-R). In conclusion, our study built a m 6 A scoring scheme, based on which synergistic immunotherapy of IL-6 neutralization and ICB might be considered as an effective therapeutic strategy for LGGs with high m 6 Ascore, for the stimulation of CD40 or TNFSF9 might reverse IL-6 inhibition induced insufficient activation of T cells by Mϕs. Discussion Tumor associated T cell actication by ICB is one of the most successful approaches for cancer immunotherapy and expected to improve the prognosis of LGG patients. However, it is poorly studied in LGGs. Recently, growing efforts have focused on the m 6 A RNA methylation and it’s impacts on cancer immune editing 50 . Here, we developed the m 6 Ascore to quantify the m 6 A methylation modification patterns in individuals and a dual-targeted strategy including anti-IL-6 and ICB to reverse T cell exhaustion for those patients with high-m 6 Ascore LGGs. Based on the existing evidence of m 6 A methylation modification network, 24 m 6 A regulators were involved in the study and stratified the the tumor samples. DEGs among the distinct m 6 A phenotypes were identified and considered as a m 6 A relevant signature in LGG, which was a biomarker designed to classify m 6 A modification patterns. The expressions of m 6 A regulators in gene cluster B were significantly higher than in the other two clusters, and the expressions of immune-checkpoints and gatheration of suppressive cells in gene cluster A&C were remarkably decreased. Patients in gene cluster B experienced the worst clinical outcomes.Then, we constructed a scoring scheme termed ‘m 6 Ascore’ to quantify the m 6 A methylation modification patterns which can act as an indipendent prognostic biomarker for LGG patients. In addition, the results of our analyses showed the correlation between m 6 Ascore with the genomic aberrations, especialy with IDH1, EGFR and TP53 mutation status. Then, we determined whether m 6 Ascore can predict immunotherapy benefit in LGG. It is observed that there were less predicted ICB benefits in m 6 Ascore-high group compared with m 6 Ascore-low group. Thus, we expected to explore potential strategies to make those non-benefit patients benefit from immunotherapy. IL-6, a pleiotropic cytokine, was recognized as a key cytokine to promote inflammation at first 51 . Recent studies has suggested that IL-6/JAK/STAT3 inhibited functional maturation of DC and suppressed effector T cells which blocked anti-tumor immunity 52 . IL-6 promoted M2 Mϕs polarization 53 , 54 and tumors with high IL-6 expression with infiltration of PD1 + CD8 + T cells and M2 Mϕs predicted poor prognosis 55 . In our study, we used cell line expression profiles and their drug sensitivity results to predict the sensitive drugs of TCGA-LGG with high m 6 Ascore by machine learning, and found the key pathway, IL6/JAK/STAT3, related to TME modulation. However, anti-IL-6 therapy showed the modest efficacy to synergize with ICB out of the patients with high ower m 6 Ascore LGGs, suggesting that there are certain mechanisms to protect the downstreams of IL-6 from anti-IL-6 therapy. Multiple studies have revealed the significant impacts of TNF superfamily on macrophage activation and their co-stimulation of T cells to promote anti-tumor immunity 56 , 57 . Thus, we analyzed the expression profile of Mϕs treated with IL-6, and found that IL-6 significantly up-regulated the expression of cd40, tnfsf4, tnfsf9, tnfsf14 and tnfsf15. Then, we found that the expression level of CD40 and TNFSF9 was significantly higher in m6Ascore-high group than the other groups when the expression of IL-6 remained same low level at different m 6 Ascore groups. These results illustrate a phynomenon that anti-IL-6 monotherapy might not fully reverse macrophage-mediated immunosuppression. Dual-targeting of CD40/TNFSF9 and IL-6 might cooperate immunotherapy of LGGs. In summary, our study established the m 6 Ascore to quantifying the m 6 A methylation modification in individuals. For LGG patients with high m 6 Ascores, we found some therapeutic drugs to avoid overtreatment and chemotherapy resistance. In the meanwhile, our findings suggested that dual-targeted stratergy including anti-IL-6 and ICB might trigger anti-tumor immunity for the patients with high-m 6 Ascore LGGs which offered opportunities for facilitating T-cell based immunotherapy against LGGs. Declarations Ethical Approval and Consent to participate The patient data involved in this study were acquired from publicly available datasets with the patients’ informed consent. Consent for publication All authors have read and approved the final submitted manuscript. Availability of supporting data Public attainable expression profiles and the corresponding clinical annotation can be obtained from the Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) and the Chinese Glioma Genome Atlas (CGGA) database (http://cgga.org.cn/). Methods used for the analyses and all generated results in this study are described in the supplementary data. Competing interests No potential conflicts of interest were disclosed . Authors' contributions Q. H. designed the study. Q. H. and HM. H. collated the data, carried out data analyses and produced the initial draft of the manuscript. Q. H. contributed to drafting the manuscript. Acknowledgments We would like to appreciate Dr. Zewei Tu for the crucial advice on this study. References Cancer Genome Atlas Research, N, Brat, DJ, Verhaak, RG, Aldape, KD, Yung, WK, Salama, SR, et al. Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas.N Engl J Med.2015;372:2481–2498 Zhou, H, Vallieres, M, Bai, HX, Su, C, Tang, H, Oldridge, D, et al. MRI features predict survival and molecular markers in diffuse lower-grade gliomas.Neuro Oncol.2017;19:862–870 Morshed, RA, Young, JS, Hervey-Jumper, SL, and Berger, MS.The management of low-grade gliomas in adults.J Neurosurg Sci.2019;63:450–457 Jaeckle, KA, Decker, PA, Ballman, KV, Flynn, PJ, Giannini, C, Scheithauer, BW, et al. Transformation of low grade glioma and correlation with outcome: an NCCTG database analysis.J Neurooncol.2011;104:253–259 Meyer, KD, and Jaffrey, SR.The dynamic epitranscriptome: N6-methyladenosine and gene expression control.Nat Rev Mol Cell Biol.2014;15:313–326 Zaccara, S, Ries, RJ, and Jaffrey, SR.Reading, writing and erasing mRNA methylation.Nat Rev Mol Cell Biol.2019;20:608–624 Sun, T, Wu, R, and Ming, L.The role of m6A RNA methylation in cancer.Biomed Pharmacother.2019;112:108613 Wang, T, Kong, S, Tao, M, and Ju, S.The potential role of RNA N6-methyladenosine in Cancer progression.Mol Cancer.2020;19:88 Chen, XY, Zhang, J, and Zhu, JS.The role of m(6)A RNA methylation in human cancer.Mol Cancer.2019;18:103 Zhang, S, Zhao, BS, Zhou, A, Lin, K, Zheng, S, Lu, Z, et al. m(6)A Demethylase ALKBH5 Maintains Tumorigenicity of Glioblastoma Stem-like Cells by Sustaining FOXM1 Expression and Cell Proliferation Program.Cancer Cell.2017;31:591–606 e596 Miranda-Goncalves, V, Lobo, J, Guimaraes-Teixeira, C, Barros-Silva, D, Guimaraes, R, Cantante, M, et al. The component of the m(6)A writer complex VIRMA is implicated in aggressive tumor phenotype, DNA damage response and cisplatin resistance in germ cell tumors.J Exp Clin Cancer Res.2021;40:268 Chang, G, Shi, L, Ye, Y, Shi, H, Zeng, L, Tiwary, S, et al. YTHDF3 Induces the Translation of m(6)A-Enriched Gene Transcripts to Promote Breast Cancer Brain Metastasis.Cancer Cell.2020;38:857–871 e857 Lan, Q, Liu, PY, Bell, JL, Wang, JY, Huttelmaier, S, Zhang, XD, et al. The Emerging Roles of RNA m(6)A Methylation and Demethylation as Critical Regulators of Tumorigenesis, Drug Sensitivity, and Resistance.Cancer Res.2021;81:3431–3440 Schreiber, RD, Old, LJ, and Smyth, MJ.Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion.Science.2011;331:1565–1570 Chowell, D, Morris, LGT, Grigg, CM, Weber, JK, Samstein, RM, Makarov, V, et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy.Science.2018;359:582–587 Gettinger, S, Choi, J, Hastings, K, Truini, A, Datar, I, Sowell, R, et al. Impaired HLA Class I Antigen Processing and Presentation as a Mechanism of Acquired Resistance to Immune Checkpoint Inhibitors in Lung Cancer.Cancer Discov.2017;7:1420–1435 Restifo, NP, Marincola, FM, Kawakami, Y, Taubenberger, J, Yannelli, JR, and Rosenberg, SA.Loss of functional beta 2-microglobulin in metastatic melanomas from five patients receiving immunotherapy.J Natl Cancer Inst.1996;88:100–108 Sade-Feldman, M, Jiao, YJ, Chen, JH, Rooney, MS, Barzily-Rokni, M, Eliane, JP, et al. Resistance to checkpoint blockade therapy through inactivation of antigen presentation.Nat Commun.2017;8:1136 Zaretsky, JM, Garcia-Diaz, A, Shin, DS, Escuin-Ordinas, H, Hugo, W, Hu-Lieskovan, S, et al. Mutations Associated with Acquired Resistance to PD-1 Blockade in Melanoma.N Engl J Med.2016;375:819–829 Nakamura, K, and Smyth, MJ.Myeloid immunosuppression and immune checkpoints in the tumor microenvironment.Cell Mol Immunol.2020;17:1–12 Hofer, F, Di Sario, G, Musiu, C, Sartoris, S, De Sanctis, F, and Ugel, S.A Complex Metabolic Network Confers Immunosuppressive Functions to Myeloid-Derived Suppressor Cells (MDSCs) within the Tumour Microenvironment.Cells.2021;10: Wherry, EJ.T cell exhaustion.Nat Immunol.2011;12:492–499 Wu, F, Wang, ZL, Wang, KY, Li, GZ, Chai, RC, Liu, YQ, et al. Classification of diffuse lower-grade glioma based on immunological profiling.Mol Oncol.2020;14:2081–2095 Grabowski, MM, Sankey, EW, Ryan, KJ, Chongsathidkiet, P, Lorrey, SJ, Wilkinson, DS, et al. Immune suppression in gliomas.J Neurooncol.2021;151:3–12 Haddad, AF, Young, JS, Oh, JY, Okada, H, and Aghi, MK.The immunology of low-grade gliomas.Neurosurg Focus.2022;52:E2 Li, HB, Tong, J, Zhu, S, Batista, PJ, Duffy, EE, Zhao, J, et al. m(6)A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways.Nature.2017;548:338–342 Li, M, Zha, X, and Wang, S.The role of N6-methyladenosine mRNA in the tumor microenvironment.Biochim Biophys Acta Rev Cancer.2021;1875:188522 Song, H, Song, J, Cheng, M, Zheng, M, Wang, T, Tian, S, et al .METTL3-mediated m(6)A RNA methylation promotes the anti-tumour immunity of natural killer cells.Nat Commun.2021;12:5522 Wilkerson, MD, and Hayes, DN.ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking.Bioinformatics.2010;26:1572–1573 Hanzelmann, S, Castelo, R, and Guinney, J.GSVA: gene set variation analysis for microarray and RNA-seq data.BMC Bioinformatics.2013;14:7 Yu, G, Wang, LG, Han, Y, and He, QY.clusterProfiler: an R package for comparing biological themes among gene clusters.OMICS.2012;16:284–287 Subramanian, A, Tamayo, P, Mootha, VK, Mukherjee, S, Ebert, BL, Gillette, MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc Natl Acad Sci U S A.2005;102:15545–15550 Love, MI, Huber, W, and Anders, S.Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.Genome Biol.2014;15:550 Charoentong, P, Finotello, F, Angelova, M, Mayer, C, Efremova, M, Rieder, D, et al. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.Cell Rep.2017;18:248–262 Newman, AM, Liu, CL, Green, MR, Gentles, AJ, Feng, W, Xu, Y, et al. Robust enumeration of cell subsets from tissue expression profiles.Nat Methods.2015;12:453–457 Yoshihara, K, Shahmoradgoli, M, Martinez, E, Vegesna, R, Kim, H, Torres-Garcia, W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data.Nat Commun.2013;4:2612 Jiang, P, Gu, S, Pan, D, Fu, J, Sahu, A, Hu, X, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response.Nat Med.2018;24:1550–1558 Zeng, D, Li, M, Zhou, R, Zhang, J, Sun, H, Shi, M, et al. Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures.Cancer Immunol Res.2019;7:737–750 Chong, W, Shang, L, Liu, J, Fang, Z, Du, F, Wu, H, et al. m(6)A regulator-based methylation modification patterns characterized by distinct tumor microenvironment immune profiles in colon cancer.Theranostics.2021;11:2201–2217 Klemm, F, Maas, RR, Bowman, RL, Kornete, M, Soukup, K, Nassiri, S, et al. Interrogation of the Microenvironmental Landscape in Brain Tumors Reveals Disease-Specific Alterations of Immune Cells.Cell.2020;181:1643–1660 e1617 Jin, L, Ge, H, Long, Y, Yang, C, Chang, YE, Mu, L, et al. CD70, a novel target of CAR T-cell therapy for gliomas.Neuro Oncol.2018;20:55–65 Gromeier, M, Brown, MC, Zhang, G, Lin, X, Chen, Y, Wei, Z, et al. Very low mutation burden is a feature of inflamed recurrent glioblastomas responsive to cancer immunotherapy.Nat Commun.2021;12:352 Yang, C, Huang, X, Li, Y, Chen, J, Lv, Y, and Dai, S.Prognosis and personalized treatment prediction in TP53-mutant hepatocellular carcinoma: an in silico strategy towards precision oncology.Brief Bioinform.2021;22: Rees, MG, Seashore-Ludlow, B, Cheah, JH, Adams, DJ, Price, EV, Gill, S, et al. Correlating chemical sensitivity and basal gene expression reveals mechanism of action.Nat Chem Biol.2016;12:109–116 Seashore-Ludlow, B, Rees, MG, Cheah, JH, Cokol, M, Price, EV, Coletti, ME, et al. Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset.Cancer Discov.2015;5:1210–1223 Basu, A, Bodycombe, NE, Cheah, JH, Price, EV, Liu, K, Schaefer, GI, et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules.Cell.2013;154:1151–1161 Quail, DF, and Joyce, JA.Microenvironmental regulation of tumor progression and metastasis.Nat Med.2013;19:1423–1437 Noy, R, and Pollard, JW.Tumor-associated macrophages: from mechanisms to therapy.Immunity.2014;41:49–61 Yang, F, He, Z, Duan, H, Zhang, D, Li, J, Yang, H, et al. Synergistic immunotherapy of glioblastoma by dual targeting of IL-6 and CD40.Nat Commun.2021;12:3424 Li, X, Ma, S, Deng, Y, Yi, P, and Yu, J.Targeting the RNA m(6)A modification for cancer immunotherapy.Mol Cancer.2022;21:76 Hunter, CA, and Jones, SA.IL-6 as a keystone cytokine in health and disease.Nat Immunol.2015;16:448–457 Kitamura, H, Ohno, Y, Toyoshima, Y, Ohtake, J, Homma, S, Kawamura, H, et al. Interleukin-6/STAT3 signaling as a promising target to improve the efficacy of cancer immunotherapy.Cancer Sci.2017;108:1947–1952 Weng, YS, Tseng, HY, Chen, YA, Shen, PC, Al Haq, AT, Chen, LM, et al. MCT-1/miR-34a/IL-6/IL-6R signaling axis promotes EMT progression, cancer stemness and M2 macrophage polarization in triple-negative breast cancer.Mol Cancer.2019;18:42 Yin, Z, Ma, T, Lin, Y, Lu, X, Zhang, C, Chen, S, et al. IL-6/STAT3 pathway intermediates M1/M2 macrophage polarization during the development of hepatocellular carcinoma.J Cell Biochem.2018;119:9419–9432 Kuo, IY, Yang, YE, Yang, PS, Tsai, YJ, Tzeng, HT, Cheng, HC, et al. Converged Rab37/IL-6 trafficking and STAT3/PD-1 transcription axes elicit an immunosuppressive lung tumor microenvironment.Theranostics.2021;11:7029–7044 So, T, and Ishii, N.The TNF-TNFR Family of Co-signal Molecules.Adv Exp Med Biol.2019;1189:53–84 Croft, M.The role of TNF superfamily members in T-cell function and diseases.Nat Rev Immunol.2009;9:271–285 Supplementary Files Supplementarytables.xlsx SupportingFigs.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1519458","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":95739250,"identity":"de3cbbff-a807-41f1-aadf-cadaacd1d29f","order_by":0,"name":"Qing Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACAxCRwMAgB6IPPICKShCjxRisJYFoLUCQ2ADVS1iLOfvZgzce7qhN7xc7/BBoyx15gwPMB2/zMNjl4dJi2ZOXbJF45njuzNlpBkAtzww3HGBLtuZhSC7G6bADOWYSiW3HcjfcTgBpOQwkecykeRgOQJyKTcv5N2At6fa30z9AtfB/w6/lBtiWmgQD6Ry4LWwEtLwxtkhsO2A443ZOwYEEg8OGMw+zGVvOMUjG47Acw5s/2+rk+Wenb/7woeKwPN/x5oc33lTY4dQCAsBYOAwzAYiZYQz8WurwqxgFo2AUjIKRDQDmqlzPKXf7nQAAAABJRU5ErkJggg==","orcid":"","institution":"Nanchang University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Han","suffix":""},{"id":95739251,"identity":"c7f8e990-1858-46ff-a252-b5a616e4298d","order_by":1,"name":"Huimin He","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huimin","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2022-04-03 17:15:11","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-1519458/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1519458/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":19946031,"identity":"c1fc99ad-e525-49f2-92ba-928c4c9e8f7f","added_by":"auto","created_at":"2022-04-04 18:03:35","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93029,"visible":true,"origin":"","legend":"\u003cp\u003eThe structural framework of this study.\u003c/p\u003e","description":"","filename":"f1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1519458/v1/b836e1ccfdb33420a615fbd7.jpg"},{"id":19946035,"identity":"4c8fe3d9-81c6-4d6c-8288-5113c402d8ee","added_by":"auto","created_at":"2022-04-04 18:03:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":585562,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of m\u003csup\u003e6\u003c/sup\u003eA methylation signatures in LGGs. (A) Interactions between m\u003csup\u003e6\u003c/sup\u003eA regulators in LGG. The size of circles represented the prognosis effect calculated by Log-rank test of the regulators. Green dot in each circles represented to be protective factors for overall survival, while black dot represented to be risk factors for overall survival. The lines linked with each other showed their interactions (blue for positivity; red for negativity), and thickness represented the correlation strength. (B) Heatmap of immune cell infractions in TCGA-LGG patients. Tumor purity, ESTIMATE score, immune score, stromal score, molecular subtype, age, gender and m\u003csup\u003e6\u003c/sup\u003eA cluster were shown as annotations. (C) Overall survival analyses for the TCGA-LGG patients among the three m\u003csup\u003e6\u003c/sup\u003eA-methylation modification patterns. (D) Progression free survival analyses for the TCGA-LGG patients among the three m\u003csup\u003e6\u003c/sup\u003eA methylation modification patterns. (E) m\u003csup\u003e6\u003c/sup\u003eA gene clusters were distinguished by different signatures. (F) GSVA enrichment analysis showed the difference on the biological behaviors among the m\u003csup\u003e6\u003c/sup\u003eA methylation modification phenotypes. (G) Different levels of tumor purity in the distinct m\u003csup\u003e6\u003c/sup\u003eA clusters. (H) TME cell fractions in the m\u003csup\u003e6\u003c/sup\u003eA clusters calculated via the CIBERSORT algorithm. (I) Proportion of the LGG-molecular subtypes in the three m\u003csup\u003e6\u003c/sup\u003eA methylation modification patterns. (J) 215 m\u003csup\u003e6\u003c/sup\u003eA phenotype-related DEGs among three m\u003csup\u003e6\u003c/sup\u003eA clusters were shown by Venn diagram. Statistical significance was corresponded to \u003cem\u003ep\u003c/em\u003e values: ns\u0026gt;0.05, * \u0026lt;0.05, ** \u0026lt;0.01, *** \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"f2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1519458/v1/7333aa3262432b3b45bada4e.jpg"},{"id":19946032,"identity":"4c673e52-f17c-42b8-8617-6e605a0a4ea6","added_by":"auto","created_at":"2022-04-04 18:03:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":560001,"visible":true,"origin":"","legend":"\u003cp\u003eThe functional annotation of the m\u003csup\u003e6\u003c/sup\u003eA methylation signatures in LGG. (A) Unsupervised clustering of the m6A methylation modification relevant DEGs to stratify LGG patients into different genomic subtypes. (B) Overall survival analyses for the TCGA-LGG patients among the three m6A gene clusters. (C) Progression free survival analyses for the TCGA-LGG patients among the three m6A gene clusters. (D) Different expression levels of m\u003csup\u003e6\u003c/sup\u003eA regulators among the m\u003csup\u003e6\u003c/sup\u003eA gene clusters. (E) TME cell fractions in the m\u003csup\u003e6\u003c/sup\u003eA clusters calculated via the CIBERSORT algorithm. (F) Different expression levels of immune-checkpoints among the m\u003csup\u003e6\u003c/sup\u003eA gene clusters. (G) The m6A gene clusters were distinguished by different signatures. (H)\u0026nbsp;Functional annotation for m6A phenotype relevant genes, adjusted \u003cem\u003ep\u003c/em\u003e value \u0026lt;0.05. Statistical significance was corresponded to \u003cem\u003ep\u003c/em\u003e values: ns\u0026gt;0.05, * \u0026lt;0.05, ** \u0026lt;0.01, *** \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"f3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1519458/v1/aa99837abedec3919c45854b.jpg"},{"id":19946033,"identity":"526231cf-6e74-44b6-b36f-698aea587f9a","added_by":"auto","created_at":"2022-04-04 18:03:35","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":501820,"visible":true,"origin":"","legend":"\u003cp\u003eQuantifying the m\u003csup\u003e6\u003c/sup\u003eA methylation modification in an independent individual. (A) Alluvial diagram showing the attribute changes of LGG patients in m\u003csup\u003e6\u003c/sup\u003eA clusters, molecular subtypes, m\u003csup\u003e6\u003c/sup\u003eA gene clusters and LGG-m\u003csup\u003e6\u003c/sup\u003eAscore. (B) Spearman analysis revealed the correlations between known signatures with LGG-m\u003csup\u003e6\u003c/sup\u003eAscore. (C) GSEA results of GO (biological process) gene sets between m\u003csup\u003e6\u003c/sup\u003eAscore-high and -low groups, adjusted \u003cem\u003ep\u003c/em\u003e value \u0026lt;0.05. (D) Mutational landscape of TCGA-LGG patients with high m\u003csup\u003e6\u003c/sup\u003eAscore on the left and low m\u003csup\u003e6\u003c/sup\u003eAscore on the right. The top bar plot indicates tumor mutation burden, while molecular subtype, age, gender and grade were shown as annotations on the bottom. (E) Clinical and molecular features between high- and low-m\u003csup\u003e6\u003c/sup\u003eAscore groups. (F) Overall survival analyses for LGG patients between low- and high-m\u003csup\u003e6\u003c/sup\u003eAscore groups in the TCGA cohort via Kaplan-Meier curves. (G) Overall survival analyses for LGG patients between low- and high-m\u003csup\u003e6\u003c/sup\u003eAscore groups in the meta-CGGA cohort via Kaplan-Meier curves. (H) The prognostic predictive effects of LGG-m\u003csup\u003e6\u003c/sup\u003eAscore in different cohorts were measured by ROC curves. (I) Predicted immunotherapy benefits via TIDE between high- and low-m\u003csup\u003e6\u003c/sup\u003eAscore groups in the TCGA cohort. (J) Predicted immunotherapy benefits via TIDE between high- and low-m\u003csup\u003e6\u003c/sup\u003eAscore groups in the meta-CGGA cohort.\u003c/p\u003e","description":"","filename":"f4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1519458/v1/eedff223ce11eebbc6b4e8ac.jpg"},{"id":19946037,"identity":"f3ba54e1-a21a-44f5-a7a9-246fd6ee0d7e","added_by":"auto","created_at":"2022-04-04 18:03:35","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":494329,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of the key m\u003csup\u003e6\u003c/sup\u003eA relevant signaling pathway for immunotherapy enhancement. (A) The sensitivity of LGG patients to TMZ treatment between m\u003csup\u003e6\u003c/sup\u003eAscore-high and -low groups was calculated based on PRISM and CRTP database. (B-C) Spearman’s correlation and drug response of CTRP-derived compounds on the top and PRISM-derived compounds on the bottom. Compounds with lower AUC value in m\u003csup\u003e6\u003c/sup\u003eAscore-high group and negative closely correlated with m\u003csup\u003e6\u003c/sup\u003eAscore were selected as potential therapeutic drugs. Lower values of AUC indicated greater drug sensitivity. (D) Correlation between drug sensitivities of potential therapeutic agents and immune cell infiltrations. (E) Correlation between drug sensitivities of potential therapeutic agents and expression levels of immune checkpoints. (F) Correlation between drug sensitivities of potential therapeutic agents and enrichment of signaling pathways. Statistical significance was corresponded to \u003cem\u003ep\u003c/em\u003e values: ns\u0026gt;0.05, * \u0026lt;0.05, ** \u0026lt;0.01, *** \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"f5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1519458/v1/ec464a19d19d5236e9cdbfdb.jpg"},{"id":19946103,"identity":"2834c3ab-88de-4174-840b-5f058f4532a2","added_by":"auto","created_at":"2022-04-04 18:08:35","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":443615,"visible":true,"origin":"","legend":"\u003cp\u003eInhibition of IL-6/JAK/STAT3 signaling sensitized lower grade gliomas to immune checkpoint blockade. Overall survival analyses (A) and progression free survival analyses (B) for the TCGA-LGG patients between high- and low- enrichment of IL-6/JAK/STAT3 signaling via Kaplan-Meier curves. (C) Relative distribution of IL-6/JAK/STAT3 signaling enrichment score between TURE and FALSE response of TIDE in the overall TCGA-LLG patients. (D) Relative distribution of IL-6/JAK/STAT3 signaling enrichment score between TURE and FALSE response at different LGG-m\u003csup\u003e6\u003c/sup\u003eAscore levels. (E) Overall survival analyses for the CGGA-LGG patients between high- and low- enrichment of IL-6/JAK/STAT3 signaling via Kaplan-Meier curves. (F) Relative distribution of IL-6/JAK/STAT3 signaling enrichment score between TURE and FALSE response in overall CGGA-LGG patients. (G) Relative distribution of IL-6/JAK/STAT3 signaling enrichment score between TURE and FALSE response at different LGG-m\u003csup\u003e6\u003c/sup\u003eAscore levels in the CGGA patients. Infiltration levels of Mϕs in different m\u003csup\u003e6\u003c/sup\u003eAscore group in TCGA cohort (H) and CGGA cohort (I). (J) Different expressed genes between mouse bone marrow-derived Mϕs treated with IL-6 and control group. (K) GSEA results of GO (biological process) gene sets between mouse bone marrow-derived Mϕs treated with IL-6 and control group, adjusted \u003cem\u003ep\u003c/em\u003e value \u0026lt;0.05. (L) Heatmap of cd40, tnfsf4, tnfsf9, tnfsf14 and tnfsf15. (M-R) The boxplots show the expression levels of IL-6, CD40, TNFSF4, TNFSF9, TNFSF14 and TNFSF15 between high m\u003csup\u003e6\u003c/sup\u003eAscore groups and the other groups. The former are based on the TCGA dataset and the latter are based on the CGGA dataset. Statistical significance was corresponded to \u003cem\u003ep\u003c/em\u003e values: ns\u0026gt;0.05, * \u0026lt;0.05, ** \u0026lt;0.01, *** \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"f6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1519458/v1/bbe525e86481d077729d4e0f.jpg"},{"id":19946104,"identity":"714d846e-e5a7-45b9-89d1-a054f74754c3","added_by":"auto","created_at":"2022-04-04 18:08:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":999252,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1519458/v1/b029a5dd-8100-40f4-907e-6c324f96200a.pdf"},{"id":19946038,"identity":"59211fb3-a654-4ba4-93c1-5b913f885c75","added_by":"auto","created_at":"2022-04-04 18:03:35","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":676930,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1519458/v1/928814e8c96b0b2f06f03afb.xlsx"},{"id":19946102,"identity":"8fc35474-2923-455c-8eba-ce6c985d0822","added_by":"auto","created_at":"2022-04-04 18:08:35","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1020731,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingFigs.docx","url":"https://assets-eu.researchsquare.com/files/rs-1519458/v1/9bcc532712d3ea4c718bb816.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eIdentification of synergistic strategies for m\u003csup\u003e6\u003c/sup\u003eA methylation-involved immunotherapy enhancement in lower grade gliomas\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLower grade gliomas (LGGs), a sort of primary brain and CNS tumor, are taken to denote both World Health Organization grades II and III gliomas \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. While some advances have been made in imaging \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and treatment \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e of LGGs, the outcomes of the patients still remain unsatisfactory and a considerable proportion of them would experience the recurrence and transformation of malignancy to higher grade \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eN6-methyladenosine, namely m\u003csup\u003e6\u003c/sup\u003eA, is a modified base which has been known to present in ribosomal RNA, noncoding RNAs, polyadenylated RNA and mammalian mRNA \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. To form m\u003csup\u003e6\u003c/sup\u003eA methylation in mRNA accounts is considered to be the most abundant and vital mRNA internal modification which has emerged as an extensively gene expression-modulation mechanism in diverse physiological processes \u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. m\u003csup\u003e6\u003c/sup\u003eA can be installed by methyltransferases termed \"writers\", removed by demethylases termed \"erasers\", and recognized by specific RNA-binding proteins termed \"readers\" \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Increasing evidences have demonstrated the crucial impacts of m\u003csup\u003e6\u003c/sup\u003eA methylation modification on the maintenance of tumor cell stemness \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, DNA damage response \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, metastasis \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and drug resistance \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eet al\u003c/em\u003e. Nevertheless, the roles of m\u003csup\u003e6\u003c/sup\u003eA RNA methylation in LGGs still remain unexplored.\u003c/p\u003e \u003cp\u003eCircumvent immune recognition is a hallmark of tumor \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. It can occur through various mechanisms including tumor cell alterations that decrease immune recognition \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and increase resistance to immunity cytotoxic effects \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e as well as the establishment of the immunosuppressive microenvironment \u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The significance of non-neoplastic stromal cells in the development of LGGs has been gradually recognized \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Considering its extraordinary immunosuppressive microenvironment, LGGs seem to be refractory to immunotherapies based on T cells including checkpoint inhibition and chimeric antigen receptor T cell transfer \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. This highlights the urgent need to identify novel combination strategies to trigger anti-LGG immunity.\u003c/p\u003e \u003cp\u003ePresent studies have revealed the effects of m\u003csup\u003e6\u003c/sup\u003eA RNA methylation on TME modification \u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Here, we systematically correlated the m\u003csup\u003e6\u003c/sup\u003eA modification phenotypes with genomic and clinical characteristics of LGGs and established a novel scoring scheme to quantify the m\u003csup\u003e6\u003c/sup\u003eA methylation modification patterns for individual LGG patients. Based on the scoring scheme, potential therapeutic drugs were prepared for TME remodeling related pathway analysis. We found that IL-6/JAK/STAT3 signaling inhibition had the potential to sensitize those patients with high m\u003csup\u003e6\u003c/sup\u003eAscore to immune checkpoint blockade (ICB), and suggested the stimulation of CD40 or TNFSF9 might reverse IL-6 inhibition induced insufficient activation of T cells by macrophages (Mϕs). Overall, our research provides possible strategies for overcoming the resistance to chemotherapy and immunotherapy under the specific m\u003csup\u003e6\u003c/sup\u003eA modification pattern for LGG patients.\u003c/p\u003e"},{"header":"Material And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition and processing of low grade glioma datasets\u003c/h2\u003e \u003cp\u003ePublic attainable expression profiles and the corresponding clinical annotation were obtained from the Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancergenome.nih.gov/\u003c/span\u003e\u003cspan address=\"https://cancergenome.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the Chinese Glioma Genome Atlas (CGGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cgga.org.cn/\u003c/span\u003e\u003cspan address=\"http://cgga.org.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Gene Expression Omnibus database (GSE151213). A total of 1040 LGG patients and mouse bone marrow-derived Mϕs treated with IL-6 were enrolled in the study, including those from TCGA-LGG cohort (n\u0026thinsp;=\u0026thinsp;457), CCGA_mRNA_seq325 (n\u0026thinsp;=\u0026thinsp;182), CGGA_mRNA_seq693 (n\u0026thinsp;=\u0026thinsp;401) and GSE151213. Batch effects of raw data from CGGA were processed by the \u0026ldquo;sva\u0026rdquo; package. Then, FPKM values in data from TCGA, CGGA and GEO were transformed into transcripts per kilobase million (TPM) format for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePhenotype Classification\u003c/h2\u003e \u003cp\u003eUnsupervised clustering analysis was performed to identify m\u003csup\u003e6\u003c/sup\u003eA methylation modification phenotypes based on the expression of 24 m\u003csup\u003e6\u003c/sup\u003eA regulators and m\u003csup\u003e6\u003c/sup\u003eA-modulated phenotypes based on the expression of perturbation genes through \u0026ldquo;ConsensuClusterPlus\u0026rdquo; package \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional annotation\u003c/h2\u003e \u003cp\u003eTo investigate the contrasts on biological process among m\u003csup\u003e6\u003c/sup\u003eA methylation modification phenotypes, GSVA enrichment analysis \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e was utilized using \u0026ldquo;GSVA\u0026rdquo; packages. The hallmark gene sets were obtained from MSigDB database. Gene Ontology (GO) analysis on m\u003csup\u003e6\u003c/sup\u003eA signature genes was performed via \u0026ldquo;clusterProfiler\u0026rdquo; package \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, which was also used to carry out \u0026ldquo;GSEA\u0026rdquo; analysis \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e to determine pathways up- and downregulated between LGG-m\u003csup\u003e6\u003c/sup\u003eAscore high and low groups. Adjusted \u003cem\u003ep\u003c/em\u003e values less than 0.05 were held to be statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eExploration of gene expression perturbation among m6A phenotypes\u003c/h2\u003e \u003cp\u003eTo identify genes correlated with m\u003csup\u003e6\u003c/sup\u003eA modification patterns, LGG patients were grouped into m\u003csup\u003e6\u003c/sup\u003eA clusters. Differently expression genes (DEGs) among three clusters were determined using the \u0026ldquo;DEseq2\u0026rdquo; package \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The counts data of TCGA-LGG were downloaded from UCSC XENA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The significance criteria for gene filtering were set as log2FC\u0026thinsp;\u0026gt;\u0026thinsp;1 while adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTumor microenvironment estimation and immune response prediction\u003c/h2\u003e \u003cp\u003eThe infiltration abundance of each cell in the tumor microenvironment (TME) was quantified by ssGSEA algorithm. The gene sets marked immune cell types were achieved from the research of Charoentong\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. CIBERSORT, a deconvolution algorithm was performed to infer cell type proportions in bulk tumor samples \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In addition, we used the ESTIMATE algorithm to predict immune and stromal cells infiltration and further infer tumor purity \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Finally, TIDE algorithm was utilized to quantify the tumor immune evasion patterns in tumor samples and predict immunotherapy benefits of each patient \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGeneration of m\u003csup\u003e6\u003c/sup\u003eA methylation modification signature\u003c/h2\u003e \u003cp\u003eTo quantify the m\u003csup\u003e6\u003c/sup\u003eA modification patterns in individual samples, a scoring system termed m6Ascore was contrasted as following procedures:\u003c/p\u003e \u003cp\u003eThe DEGs among different m\u003csup\u003e6\u003c/sup\u003eA clusters were identified for the overlap gene extract. Then, the LGG patients were stratified for deeper analysis by the overlap DEGs using unsupervised clustering method. After that, principal component analysis (PCA) was constructed to generate m\u003csup\u003e6\u003c/sup\u003eA modification signature. Both principal component 1 and component 2 were considered to serve as signature scores. The advantage of this method focuses the score on the largest block of well-correlated/ inverse-correlated genes in the set, while the contributions of genes which little track with other members were weighted down. Finally, we adopted a former formula to define the m6Ascore of each LGG patient \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e: m6Ascore= \u0026sum;(PC1i\u0026thinsp;+\u0026thinsp;PC2i), where i is the gene expression in the signature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition and processing of drug sensitivity data\u003c/h2\u003e \u003cp\u003eExpression profile data from human cancer cell lines were obtained from the Broad Institute Cancer Cell Line Encyclopedia (CCLE) project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portals.broadinstitute.org/ccle/\u003c/span\u003e\u003cspan address=\"https://portals.broadinstitute.org/ccle/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for working as the training set. Drug sensitivity data was achieved from the Cancer Therapeutics Response Portal (CTRP) and Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset. Both two datasets provide AUC (the area under the curve) values as a drug sensitivity measure. Compounds with more than one fifth of missing data were removed, while K nearest neighbor (k-NN) imputation was used for imputing the missing values. We identified the candidate compounds with different AUC values between LGG-m\u003csup\u003e6\u003c/sup\u003eAscore groups (log2FC\u0026thinsp;\u0026gt;\u0026thinsp;0.1). Spearman correlation analysis was adopted to estimate the relationship between AUC value and m\u003csup\u003e6\u003c/sup\u003eAscore (Spearman\u0026rsquo;s r\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.30).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eR-4.0.2 was employed for statistical analyses. For quantified data, statistical significance between normally distributed variables was calculated by Student\u0026rsquo;s t-tests, while that between non-normally distributed variables was checked by the Wilcoxon test. Chi-square test or Fisher exact test was used to exam correlation between two categorical variables. For survival analysis, Kaplan-Meier curves and the Cox proportional hazards models were performed by \u0026lsquo;Survminer\u0026rsquo; package. The receiver operating characteristic (ROC) curves was utilized to verify the prognostic prediction performance via \u0026lsquo;timeROC\u0026rsquo; package. Statistical significance was corresponded to \u003cem\u003ep\u003c/em\u003e values: ns\u0026thinsp;\u0026gt;\u0026thinsp;0.05, * \u0026lt;0.05, ** \u0026lt;0.01, *** \u0026lt;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of m6A methylation signatures in LGGs\u003c/h2\u003e \u003cp\u003eThe structural framework of this study was depicted \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003eTo systematically investigate the m\u003csup\u003e6\u003c/sup\u003eA modification patterns, a total of 24 m\u003csup\u003e6\u003c/sup\u003eA regulators (the landscape of genetic alterations and prognostic analysis were shown in Figure S1) were enrolled for stratifying TCGA-LGG patients, among which IGF2BPs were significantly associated to the patients\u0026rsquo; prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cb\u003eFigure S2A-G\u003c/b\u003e). Those patients with high immune infiltration were observed to have poor prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-D). Then, we tested known signatures to better describe the function of m\u003csup\u003e6\u003c/sup\u003eA methylation modification in LGG, and confirmed its important regulatory roles in immune, stromal as well as mismatch repair (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). To learn more about the biological behaviors among the distinct m\u003csup\u003e6\u003c/sup\u003eA modification phenotypes, GSVA analysis was performed. The phenotypes with poor prognosis related to pathways enriched in epithelial-mesenchymal transition, mismatch repair and cytokine-related pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). After that, we used the ESTIMATE algorithm to assess the tumor purity levels which was significantly higher in Cluster C (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). CIBERSORT was also used to evaluate the fraction of TME cells among the m\u003csup\u003e6\u003c/sup\u003eA clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). In addition, LGGs with wild type IDH were mainly clustered into Cluster B\u0026amp;C, while IDH mutant samples were closely relevant to Cluster A (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). Finally, we determined 215 overlapping m\u003csup\u003e6\u003c/sup\u003eA phenotype-related DEGs as the m\u003csup\u003e6\u003c/sup\u003eA gene signature in LGGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further investigate the clinical traits and potential biological behavior of the m\u003csup\u003e6\u003c/sup\u003eA modification signature, unsupervised clustering analyses was performed to identify three typical subtypes termed \u0026lsquo;Gene cluster\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Overall survival analysis and progression free survival analysis revealed that patients in gene cluster B experienced the outcomes of poor prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C). 24 m\u003csup\u003e6\u003c/sup\u003eA regulators involved in this study showed significant expression perturbations among the three gene clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). A significant higher infraction level of TME cells and elevated expression level of immune-checkpoints were indicated in the gene cluster B (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F). In addition, we also evaluated the enrichment of mismatch repair and stromal relevant signatures which were remarkably difference among the three gene clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Furthermore, functional annotations on the genes in the m\u003csup\u003e6\u003c/sup\u003eA gene signature were carried out. The results showed that the m6A-related genes were widely involved in the maintenance of tumor stemness, radiotherapy, proliferation and metabolism and immunity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eQuantifying the m6A methylation modification in an independent individual\u003c/h2\u003e \u003cp\u003eTo quantifying the m\u003csup\u003e6\u003c/sup\u003eA methylation modification in an independent individual, we generated a scoring scheme termed \u0026ldquo;m\u003csup\u003e6\u003c/sup\u003eAscore\u0026rdquo; based on the m\u003csup\u003e6\u003c/sup\u003eA signature via the PCA algorithm. The attribute changes of LGG patients were illustrated by an alluvial diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). m\u003csup\u003e6\u003c/sup\u003eAscore was positively correlated with the expression levels of CD8\u003csup\u003e+\u003c/sup\u003e T effectors and immune checkpoints (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), which suggested the pre-existence of immune response in the tumors with high m\u003csup\u003e6\u003c/sup\u003eAscore and then followed by T-cell exhaustion. Moreover, the m\u003csup\u003e6\u003c/sup\u003eAscore was markedly positively correlated with EMT signatures but negatively correlated with stromal relevant signatures. The results of GSEA revealed the differentially enriched pathways between the m\u003csup\u003e6\u003c/sup\u003eAscore groups, such as \u0026ldquo;MAPK cascade\u0026rdquo;, \u0026ldquo;ERK1/2 cascades\u0026rdquo; and \u0026ldquo;JAK/STAT signaling\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). After that, we analyzed the somatic mutation differences between high- and low-m\u003csup\u003e6\u003c/sup\u003eAscore groups in TCGA-LGG cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The genes with significantly different distributions among the genes with TOP10 mutant frequency in the TCGA-LGG patients were shown in Figure S5. Then, we analyzed the clinical and molecular features between the LGG-m\u003csup\u003e6\u003c/sup\u003eAscore groups. The significant distinct distributions of primary location, grade, age and molecular subtypes were observed between the m\u003csup\u003e6\u003c/sup\u003eAscore groups. Patients with high m\u003csup\u003e6\u003c/sup\u003escore were remarkably associated with worse prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF-H). What\u0026rsquo;s more, an increasing number of studies have revealed the significant roles of TME in the development of LGGs \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and the feasibility of immunotherapy in brain tumors \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. We assessed the benefit of immune checkpoint blockage (ICB) therapy. Those patients with high m\u003csup\u003e6\u003c/sup\u003eAscore and worse prognosis also predicted to benefit less from ICB therapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI-J). Thus, it is urgent for LGG patients to identify ICB sensitization strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of the key m\u003csup\u003e6\u003c/sup\u003eA relevant signaling pathway for immunotherapy enhancement\u003c/h2\u003e \u003cp\u003eInstead of merely analyzing gene expression perturbation, we expected to obtain the sensitive drug compounds applied in high-m\u003csup\u003e6\u003c/sup\u003eAscore LGGs for TME remodeling as well as their associated pathways which might contribute to ICB sensitization. We first identified drug candidates with greater sensitivity to high LGG-m\u003csup\u003e6\u003c/sup\u003eAscore patients \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The analyses were based on the drug response data of CTRP \u003csup\u003e\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e and PRISM, including 1,639 compounds. Totally 16 compounds were yielded with higher sensitivity by the analysis, while the most widespread chemotherapy for glioma patients, temozolomide, was detected to had lower sensitivity in high m\u003csup\u003e6\u003c/sup\u003eAscore patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). Afterwards, correlation analysis was adopted to select TAK-733, clofarabine, dasatinib and birinapant as the optimal therapeutic agents to those LGGs the immune suppressed microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-E). It was found that the sensitivities of LGGs to these four candidates were all positively correlated with the enrichment of IL-6/JAK/STAT3 signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Together, our data revealed that IL-6/JAK/STAT3 might be a potential therapeutic targeting to trigger anti-LGG immunity to avoid ineffective overtreatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eInhibition of IL-6/JAK/STAT3 signaling sensitized LGGs with high m\u003csup\u003e6\u003c/sup\u003eAscore to immune checkpoint blockade\u003c/h2\u003e \u003cp\u003eWe first evaluated the relationship between IL-6/JAK/STAT3 signaling activation and prognosis. Higher enrichment of IL-6/JAK/STAT3 signaling was associated with the worse prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). Assessing the ICB treatment benefits, however, we did not observe less enrichment of IL-6/JAK/STAT3 signaling in the ICB-benefit group compared with non-benefit group in overall patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Therefore, we stratified TCGA-LGG patients. It was found that IL-6/JAK/STAT3 signaling was lower enriched in the predicted ICB-beneficial patients with the high LGG-m\u003csup\u003e6\u003c/sup\u003eAscore, while this pathway was significantly enriched in ICB-beneficial patients with median and low m\u003csup\u003e6\u003c/sup\u003eAscore (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Similarly, highly enriched IL-6/JAK/STAT3 signaling associated with poor outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE) was observed in ICB non-benefit LGGs with high m\u003csup\u003e6\u003c/sup\u003eAscore, while there was no significant difference in the overall CGGA-LGG patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF-G). Tumor-associated Mϕs have a pivotal role in cancer immunosuppression and therapy resistance \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. We first evaluated the infiltration level and polarization of Mϕs in different m\u003csup\u003e6\u003c/sup\u003eAscore groups. We found that high-m\u003csup\u003e6\u003c/sup\u003eAscore group had an equal level of M2 macrophages and higher level of M0 and M1 macrophages compared with the other groups, albeit, the expression level of IL-6 which contributed to the macrophage infiltration and polarization was higher in high-m\u003csup\u003e6\u003c/sup\u003eAscore group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH-I\u003cb\u003e)\u003c/b\u003e. IL-6 neutralization has been proved to moderately enhance infiltration of CD3\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003eT cells into the tumors but do not activate these T cells, and reduced recruitment of CD45\u003csup\u003e+\u003c/sup\u003eCD11b\u003csup\u003e+\u003c/sup\u003eF4/80\u003csup\u003e+\u003c/sup\u003e Mϕs \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. We therefore speculated that IL-6 neutralization might lead to insufficient activation of T cells by Mϕs, and perhaps there are certain mechanisms in protecting IL-6 downstream from IL-6 inhibition in the patients with high m\u003csup\u003e6\u003c/sup\u003eAscore.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRNA-seq data revealed that IL-6 could stimulate the expression of the members of tumor necrosis factor (TNF) superfamilies (cd40, tnfsf4, tnfsf9, tnfsf14, tnfsf15) in mouse bone marrow-derived Mϕs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ-L\u003cb\u003e)\u003c/b\u003e. We ranked TCGA and CGGA samples according to their IL-6 expression levels from low to high, selected the LGGs with the lowest IL-6 expression, and ensured that there was no significant difference in IL-6 expression levels between the m\u003csup\u003e6\u003c/sup\u003eAscore groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eM). We were surprised to find that CD40 and TNFSF9 expression were dramatically higher in high-m\u003csup\u003e6\u003c/sup\u003eAscore group than in other groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eN-R).\u003c/p\u003e \u003cp\u003eIn conclusion, our study built a m\u003csup\u003e6\u003c/sup\u003eA scoring scheme, based on which synergistic immunotherapy of IL-6 neutralization and ICB might be considered as an effective therapeutic strategy for LGGs with high m\u003csup\u003e6\u003c/sup\u003eAscore, for the stimulation of CD40 or TNFSF9 might reverse IL-6 inhibition induced insufficient activation of T cells by Mϕs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTumor associated T cell actication by ICB is one of the most successful approaches for cancer immunotherapy and expected to improve the prognosis of LGG patients. However, it is poorly studied in LGGs. Recently, growing efforts have focused on the m\u003csup\u003e6\u003c/sup\u003eA RNA methylation and it\u0026rsquo;s impacts on cancer immune editing \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Here, we developed the m\u003csup\u003e6\u003c/sup\u003eAscore to quantify the m\u003csup\u003e6\u003c/sup\u003eA methylation modification patterns in individuals and a dual-targeted strategy including anti-IL-6 and ICB to reverse T cell exhaustion for those patients with high-m\u003csup\u003e6\u003c/sup\u003eAscore LGGs.\u003c/p\u003e \u003cp\u003eBased on the existing evidence of m\u003csup\u003e6\u003c/sup\u003eA methylation modification network, 24 m\u003csup\u003e6\u003c/sup\u003eA regulators were involved in the study and stratified the the tumor samples. DEGs among the distinct m\u003csup\u003e6\u003c/sup\u003eA phenotypes were identified and considered as a m\u003csup\u003e6\u003c/sup\u003eA relevant signature in LGG, which was a biomarker designed to classify m\u003csup\u003e6\u003c/sup\u003eA modification patterns. The expressions of m\u003csup\u003e6\u003c/sup\u003eA regulators in gene cluster B were significantly higher than in the other two clusters, and the expressions of immune-checkpoints and gatheration of suppressive cells in gene cluster A\u0026amp;C were remarkably decreased. Patients in gene cluster B experienced the worst clinical outcomes.Then, we constructed a scoring scheme termed \u0026lsquo;m\u003csup\u003e6\u003c/sup\u003eAscore\u0026rsquo; to quantify the m\u003csup\u003e6\u003c/sup\u003eA methylation modification patterns which can act as an indipendent prognostic biomarker for LGG patients. In addition, the results of our analyses showed the correlation between m\u003csup\u003e6\u003c/sup\u003eAscore with the genomic aberrations, especialy with IDH1, EGFR and TP53 mutation status. Then, we determined whether m\u003csup\u003e6\u003c/sup\u003eAscore can predict immunotherapy benefit in LGG. It is observed that there were less predicted ICB benefits in m\u003csup\u003e6\u003c/sup\u003eAscore-high group compared with m\u003csup\u003e6\u003c/sup\u003eAscore-low group. Thus, we expected to explore potential strategies to make those non-benefit patients benefit from immunotherapy.\u003c/p\u003e \u003cp\u003eIL-6, a pleiotropic cytokine, was recognized as a key cytokine to promote inflammation at first \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Recent studies has suggested that IL-6/JAK/STAT3 inhibited functional maturation of DC and suppressed effector T cells which blocked anti-tumor immunity \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. IL-6 promoted M2 Mϕs polarization \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e and tumors with high IL-6 expression with infiltration of PD1\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells and M2 Mϕs predicted poor prognosis \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. In our study, we used cell line expression profiles and their drug sensitivity results to predict the sensitive drugs of TCGA-LGG with high m\u003csup\u003e6\u003c/sup\u003eAscore by machine learning, and found the key pathway, IL6/JAK/STAT3, related to TME modulation. However, anti-IL-6 therapy showed the modest efficacy to synergize with ICB out of the patients with high ower m\u003csup\u003e6\u003c/sup\u003eAscore LGGs, suggesting that there are certain mechanisms to protect the downstreams of IL-6 from anti-IL-6 therapy.\u003c/p\u003e \u003cp\u003eMultiple studies have revealed the significant impacts of TNF superfamily on macrophage activation and their co-stimulation of T cells to promote anti-tumor immunity \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Thus, we analyzed the expression profile of Mϕs treated with IL-6, and found that IL-6 significantly up-regulated the expression of cd40, tnfsf4, tnfsf9, tnfsf14 and tnfsf15. Then, we found that the expression level of CD40 and TNFSF9 was significantly higher in m6Ascore-high group than the other groups when the expression of IL-6 remained same low level at different m\u003csup\u003e6\u003c/sup\u003eAscore groups. These results illustrate a phynomenon that anti-IL-6 monotherapy might not fully reverse macrophage-mediated immunosuppression. Dual-targeting of CD40/TNFSF9 and IL-6 might cooperate immunotherapy of LGGs.\u003c/p\u003e \u003cp\u003eIn summary, our study established the m\u003csup\u003e6\u003c/sup\u003eAscore to quantifying the m\u003csup\u003e6\u003c/sup\u003eA methylation modification in individuals. For LGG patients with high m\u003csup\u003e6\u003c/sup\u003eAscores, we found some therapeutic drugs to avoid overtreatment and chemotherapy resistance. In the meanwhile, our findings suggested that dual-targeted stratergy including anti-IL-6 and ICB might trigger anti-tumor immunity for the patients with high-m\u003csup\u003e6\u003c/sup\u003eAscore LGGs which offered opportunities for facilitating T-cell based immunotherapy against LGGs.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthical Approval and Consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe patient data involved in this study were acquired from publicly available datasets with the patients\u0026rsquo; informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final submitted manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of supporting data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePublic attainable expression profiles and the corresponding clinical annotation can be obtained from the Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) and the Chinese Glioma Genome Atlas (CGGA) database (http://cgga.org.cn/). Methods used for the analyses and all generated results in this study are described in the supplementary data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflicts of interest were disclosed\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eQ. H. designed the study. Q. H. and HM. H. collated the data, carried out data analyses and produced the initial draft of the manuscript. Q. H. contributed to drafting the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to appreciate Dr. Zewei Tu for the crucial advice on this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCancer Genome Atlas Research, N, Brat, DJ, Verhaak, RG, Aldape, KD, Yung, WK, Salama, SR, \u003cem\u003eet al.\u003c/em\u003eComprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas.N Engl J Med.2015;372:2481\u0026ndash;2498\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, H, Vallieres, M, Bai, HX, Su, C, Tang, H, Oldridge, D, \u003cem\u003eet al.\u003c/em\u003eMRI features predict survival and molecular markers in diffuse lower-grade gliomas.Neuro Oncol.2017;19:862\u0026ndash;870\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorshed, RA, Young, JS, Hervey-Jumper, SL, and Berger, MS.The management of low-grade gliomas in adults.J Neurosurg Sci.2019;63:450\u0026ndash;457\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaeckle, KA, Decker, PA, Ballman, KV, Flynn, PJ, Giannini, C, Scheithauer, BW, \u003cem\u003eet al.\u003c/em\u003eTransformation of low grade glioma and correlation with outcome: an NCCTG database analysis.J Neurooncol.2011;104:253\u0026ndash;259\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeyer, KD, and Jaffrey, SR.The dynamic epitranscriptome: N6-methyladenosine and gene expression control.Nat Rev Mol Cell Biol.2014;15:313\u0026ndash;326\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaccara, S, Ries, RJ, and Jaffrey, SR.Reading, writing and erasing mRNA methylation.Nat Rev Mol Cell Biol.2019;20:608\u0026ndash;624\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, T, Wu, R, and Ming, L.The role of m6A RNA methylation in cancer.Biomed Pharmacother.2019;112:108613\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, T, Kong, S, Tao, M, and Ju, S.The potential role of RNA N6-methyladenosine in Cancer progression.Mol Cancer.2020;19:88\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, XY, Zhang, J, and Zhu, JS.The role of m(6)A RNA methylation in human cancer.Mol Cancer.2019;18:103\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, S, Zhao, BS, Zhou, A, Lin, K, Zheng, S, Lu, Z, \u003cem\u003eet al.\u003c/em\u003em(6)A Demethylase ALKBH5 Maintains Tumorigenicity of Glioblastoma Stem-like Cells by Sustaining FOXM1 Expression and Cell Proliferation Program.Cancer Cell.2017;31:591\u0026ndash;606 e596\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiranda-Goncalves, V, Lobo, J, Guimaraes-Teixeira, C, Barros-Silva, D, Guimaraes, R, Cantante, M, \u003cem\u003eet al.\u003c/em\u003eThe component of the m(6)A writer complex VIRMA is implicated in aggressive tumor phenotype, DNA damage response and cisplatin resistance in germ cell tumors.J Exp Clin Cancer Res.2021;40:268\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang, G, Shi, L, Ye, Y, Shi, H, Zeng, L, Tiwary, S, \u003cem\u003eet al.\u003c/em\u003eYTHDF3 Induces the Translation of m(6)A-Enriched Gene Transcripts to Promote Breast Cancer Brain Metastasis.Cancer Cell.2020;38:857\u0026ndash;871 e857\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLan, Q, Liu, PY, Bell, JL, Wang, JY, Huttelmaier, S, Zhang, XD, \u003cem\u003eet al.\u003c/em\u003eThe Emerging Roles of RNA m(6)A Methylation and Demethylation as Critical Regulators of Tumorigenesis, Drug Sensitivity, and Resistance.Cancer Res.2021;81:3431\u0026ndash;3440\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchreiber, RD, Old, LJ, and Smyth, MJ.Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion.Science.2011;331:1565\u0026ndash;1570\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChowell, D, Morris, LGT, Grigg, CM, Weber, JK, Samstein, RM, Makarov, V, \u003cem\u003eet al.\u003c/em\u003ePatient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy.Science.2018;359:582\u0026ndash;587\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGettinger, S, Choi, J, Hastings, K, Truini, A, Datar, I, Sowell, R, \u003cem\u003eet al.\u003c/em\u003eImpaired HLA Class I Antigen Processing and Presentation as a Mechanism of Acquired Resistance to Immune Checkpoint Inhibitors in Lung Cancer.Cancer Discov.2017;7:1420\u0026ndash;1435\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRestifo, NP, Marincola, FM, Kawakami, Y, Taubenberger, J, Yannelli, JR, and Rosenberg, SA.Loss of functional beta 2-microglobulin in metastatic melanomas from five patients receiving immunotherapy.J Natl Cancer Inst.1996;88:100\u0026ndash;108\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSade-Feldman, M, Jiao, YJ, Chen, JH, Rooney, MS, Barzily-Rokni, M, Eliane, JP, \u003cem\u003eet al.\u003c/em\u003eResistance to checkpoint blockade therapy through inactivation of antigen presentation.Nat Commun.2017;8:1136\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaretsky, JM, Garcia-Diaz, A, Shin, DS, Escuin-Ordinas, H, Hugo, W, Hu-Lieskovan, S, \u003cem\u003eet al.\u003c/em\u003eMutations Associated with Acquired Resistance to PD-1 Blockade in Melanoma.N Engl J Med.2016;375:819\u0026ndash;829\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakamura, K, and Smyth, MJ.Myeloid immunosuppression and immune checkpoints in the tumor microenvironment.Cell Mol Immunol.2020;17:1\u0026ndash;12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHofer, F, Di Sario, G, Musiu, C, Sartoris, S, De Sanctis, F, and Ugel, S.A Complex Metabolic Network Confers Immunosuppressive Functions to Myeloid-Derived Suppressor Cells (MDSCs) within the Tumour Microenvironment.Cells.2021;10:\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWherry, EJ.T cell exhaustion.Nat Immunol.2011;12:492\u0026ndash;499\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, F, Wang, ZL, Wang, KY, Li, GZ, Chai, RC, Liu, YQ, \u003cem\u003eet al.\u003c/em\u003eClassification of diffuse lower-grade glioma based on immunological profiling.Mol Oncol.2020;14:2081\u0026ndash;2095\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrabowski, MM, Sankey, EW, Ryan, KJ, Chongsathidkiet, P, Lorrey, SJ, Wilkinson, DS, \u003cem\u003eet al.\u003c/em\u003eImmune suppression in gliomas.J Neurooncol.2021;151:3\u0026ndash;12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaddad, AF, Young, JS, Oh, JY, Okada, H, and Aghi, MK.The immunology of low-grade gliomas.Neurosurg Focus.2022;52:E2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, HB, Tong, J, Zhu, S, Batista, PJ, Duffy, EE, Zhao, J, \u003cem\u003eet al.\u003c/em\u003em(6)A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways.Nature.2017;548:338\u0026ndash;342\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, M, Zha, X, and Wang, S.The role of N6-methyladenosine mRNA in the tumor microenvironment.Biochim Biophys Acta Rev Cancer.2021;1875:188522\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong, H, Song, J, Cheng, M, Zheng, M, Wang, T, Tian, S, \u003cem\u003eet al\u003c/em\u003e.METTL3-mediated m(6)A RNA methylation promotes the anti-tumour immunity of natural killer cells.Nat Commun.2021;12:5522\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilkerson, MD, and Hayes, DN.ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking.Bioinformatics.2010;26:1572\u0026ndash;1573\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanzelmann, S, Castelo, R, and Guinney, J.GSVA: gene set variation analysis for microarray and RNA-seq data.BMC Bioinformatics.2013;14:7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu, G, Wang, LG, Han, Y, and He, QY.clusterProfiler: an R package for comparing biological themes among gene clusters.OMICS.2012;16:284\u0026ndash;287\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian, A, Tamayo, P, Mootha, VK, Mukherjee, S, Ebert, BL, Gillette, MA, \u003cem\u003eet al.\u003c/em\u003eGene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc Natl Acad Sci U S A.2005;102:15545\u0026ndash;15550\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLove, MI, Huber, W, and Anders, S.Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.Genome Biol.2014;15:550\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharoentong, P, Finotello, F, Angelova, M, Mayer, C, Efremova, M, Rieder, D, \u003cem\u003eet al.\u003c/em\u003ePan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.Cell Rep.2017;18:248\u0026ndash;262\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewman, AM, Liu, CL, Green, MR, Gentles, AJ, Feng, W, Xu, Y, \u003cem\u003eet al.\u003c/em\u003eRobust enumeration of cell subsets from tissue expression profiles.Nat Methods.2015;12:453\u0026ndash;457\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshihara, K, Shahmoradgoli, M, Martinez, E, Vegesna, R, Kim, H, Torres-Garcia, W, \u003cem\u003eet al.\u003c/em\u003eInferring tumour purity and stromal and immune cell admixture from expression data.Nat Commun.2013;4:2612\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, P, Gu, S, Pan, D, Fu, J, Sahu, A, Hu, X, \u003cem\u003eet al.\u003c/em\u003eSignatures of T cell dysfunction and exclusion predict cancer immunotherapy response.Nat Med.2018;24:1550\u0026ndash;1558\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng, D, Li, M, Zhou, R, Zhang, J, Sun, H, Shi, M, \u003cem\u003eet al.\u003c/em\u003eTumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures.Cancer Immunol Res.2019;7:737\u0026ndash;750\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChong, W, Shang, L, Liu, J, Fang, Z, Du, F, Wu, H, \u003cem\u003eet al.\u003c/em\u003em(6)A regulator-based methylation modification patterns characterized by distinct tumor microenvironment immune profiles in colon cancer.Theranostics.2021;11:2201\u0026ndash;2217\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlemm, F, Maas, RR, Bowman, RL, Kornete, M, Soukup, K, Nassiri, S, \u003cem\u003eet al.\u003c/em\u003eInterrogation of the Microenvironmental Landscape in Brain Tumors Reveals Disease-Specific Alterations of Immune Cells.Cell.2020;181:1643\u0026ndash;1660 e1617\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin, L, Ge, H, Long, Y, Yang, C, Chang, YE, Mu, L, \u003cem\u003eet al.\u003c/em\u003eCD70, a novel target of CAR T-cell therapy for gliomas.Neuro Oncol.2018;20:55\u0026ndash;65\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGromeier, M, Brown, MC, Zhang, G, Lin, X, Chen, Y, Wei, Z, \u003cem\u003eet al.\u003c/em\u003eVery low mutation burden is a feature of inflamed recurrent glioblastomas responsive to cancer immunotherapy.Nat Commun.2021;12:352\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, C, Huang, X, Li, Y, Chen, J, Lv, Y, and Dai, S.Prognosis and personalized treatment prediction in TP53-mutant hepatocellular carcinoma: an in silico strategy towards precision oncology.Brief Bioinform.2021;22:\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRees, MG, Seashore-Ludlow, B, Cheah, JH, Adams, DJ, Price, EV, Gill, S, \u003cem\u003eet al.\u003c/em\u003eCorrelating chemical sensitivity and basal gene expression reveals mechanism of action.Nat Chem Biol.2016;12:109\u0026ndash;116\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeashore-Ludlow, B, Rees, MG, Cheah, JH, Cokol, M, Price, EV, Coletti, ME, \u003cem\u003eet al.\u003c/em\u003eHarnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset.Cancer Discov.2015;5:1210\u0026ndash;1223\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasu, A, Bodycombe, NE, Cheah, JH, Price, EV, Liu, K, Schaefer, GI, \u003cem\u003eet al.\u003c/em\u003eAn interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules.Cell.2013;154:1151\u0026ndash;1161\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuail, DF, and Joyce, JA.Microenvironmental regulation of tumor progression and metastasis.Nat Med.2013;19:1423\u0026ndash;1437\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoy, R, and Pollard, JW.Tumor-associated macrophages: from mechanisms to therapy.Immunity.2014;41:49\u0026ndash;61\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, F, He, Z, Duan, H, Zhang, D, Li, J, Yang, H, \u003cem\u003eet al.\u003c/em\u003eSynergistic immunotherapy of glioblastoma by dual targeting of IL-6 and CD40.Nat Commun.2021;12:3424\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, X, Ma, S, Deng, Y, Yi, P, and Yu, J.Targeting the RNA m(6)A modification for cancer immunotherapy.Mol Cancer.2022;21:76\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunter, CA, and Jones, SA.IL-6 as a keystone cytokine in health and disease.Nat Immunol.2015;16:448\u0026ndash;457\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitamura, H, Ohno, Y, Toyoshima, Y, Ohtake, J, Homma, S, Kawamura, H, \u003cem\u003eet al.\u003c/em\u003eInterleukin-6/STAT3 signaling as a promising target to improve the efficacy of cancer immunotherapy.Cancer Sci.2017;108:1947\u0026ndash;1952\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeng, YS, Tseng, HY, Chen, YA, Shen, PC, Al Haq, AT, Chen, LM, \u003cem\u003eet al.\u003c/em\u003eMCT-1/miR-34a/IL-6/IL-6R signaling axis promotes EMT progression, cancer stemness and M2 macrophage polarization in triple-negative breast cancer.Mol Cancer.2019;18:42\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin, Z, Ma, T, Lin, Y, Lu, X, Zhang, C, Chen, S, \u003cem\u003eet al.\u003c/em\u003eIL-6/STAT3 pathway intermediates M1/M2 macrophage polarization during the development of hepatocellular carcinoma.J Cell Biochem.2018;119:9419\u0026ndash;9432\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuo, IY, Yang, YE, Yang, PS, Tsai, YJ, Tzeng, HT, Cheng, HC, \u003cem\u003eet al.\u003c/em\u003eConverged Rab37/IL-6 trafficking and STAT3/PD-1 transcription axes elicit an immunosuppressive lung tumor microenvironment.Theranostics.2021;11:7029\u0026ndash;7044\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSo, T, and Ishii, N.The TNF-TNFR Family of Co-signal Molecules.Adv Exp Med Biol.2019;1189:53\u0026ndash;84\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCroft, M.The role of TNF superfamily members in T-cell function and diseases.Nat Rev Immunol.2009;9:271\u0026ndash;285\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Nanchang University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lower grade gliomas, m6A, Immunotherapy, IL-6, macrophages","lastPublishedDoi":"10.21203/rs.3.rs-1519458/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1519458/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLow grade gliomas (LGGs) are often noted for their unpredictable recurrence and transformation of malignancy to higher grade. Due to the extraordinary immunosuppressive microenvironment, LGGs seem to be refractory to T-cell based immunotherapies. Targeting RNA N6-methyladenosine (m\u003csup\u003e6\u003c/sup\u003eA) modulated tumor microenvironment (TME) offers opportunities to trigger anti-LGG immunity. In our study, m\u003csup\u003e6\u003c/sup\u003eAscore were used for quantifying the m\u003csup\u003e6\u003c/sup\u003eA modification patterns in LGGs. Potential therapeutic drugs and synergistic immunotherapy approaches which might avoid overtreatment and contribute to the TME remodeling were suggested based on the scoring scheme. We found a link between m\u003csup\u003e6\u003c/sup\u003eAscore and TME diversity. Those patients with high m\u003csup\u003e6\u003c/sup\u003eAscore and worse outcomes were more likely to experience immunotherapeutic failures. Inhibition of IL-6/JAK/STAT3 signaling that closely correlated with the drug sensitivities was indicated to facilitate the response of LGGs with high-m\u003csup\u003e6\u003c/sup\u003eAscore instead of overall LGGs to immune checkpoint blockade (ICB). Mechanically, stimulation of CD40 or TNFSF9 might reverse IL-6 inhibition induced insufficient activation of T cells by macrophages. In conclusion, synergistic immunotherapy of anti-IL-6 and ICB may offer insight into the m\u003csup\u003e6\u003c/sup\u003eA related immunotherapy enhancement for LGG patients.\u003c/p\u003e","manuscriptTitle":"Identification of synergistic strategies for m6A methylation-involved immunotherapy enhancement in lower grade gliomas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-04-04 18:03:33","doi":"10.21203/rs.3.rs-1519458/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c88d9ccf-f61f-4a62-9115-d4a52842fa4c","owner":[],"postedDate":"April 4th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":11570564,"name":"Immunology"}],"tags":[],"updatedAt":"2022-04-04T18:03:33+00:00","versionOfRecord":[],"versionCreatedAt":"2022-04-04 18:03:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1519458","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1519458","identity":"rs-1519458","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}