Tumor antigens and immunogenic cell death subtypes guided mRNA vaccine development for lower-grade gliomas

Tumor antigens and immunogenic cell death subtypes guided mRNA vaccine development for 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 Tumor antigens and immunogenic cell death subtypes guided mRNA vaccine development for lower-grade gliomas Wen Yin, Dongcheng Xie, Guihua Tang, Caiping Ren, Xingjun Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3505524/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 Background: Accumulating evidence demonstrated the effectiveness of mRNA vaccine against many cancers, however, their development in LGGs is still urgently needed. In addition, increasing evidence demonstrated that Immunogenic cell death (ICD) was associated with antitumor immune response. Thus, the aim of our study was to identify potential LGG tumor antigens for mRNA vaccine development and select suitable patients for vaccination based on ICD subtypes. Methods: Gene expression matrix and matched clinical information of LGG were downloaded from the UCSC Xena website and CGGA databases. Differential expression analysis was conducted by GEPIA, and altered genomes were obtained from cBioPortal. TIMER was used for immune cell infiltration analysis, consensus clustering for typing ICD subtypes, and WGCNA for identifying hub modules and genes related to ICD subtypes. Eighty-two glioma tissue samples were collected and immunohistochemical staining was used to validate the correlation between tumor antigens and co-stimulatory factors. Results: We identified seven potential LGG tumor antigens significantly correlated with poor prognosis and strongly positively correlated with infiltration of antigen-presenting cells, including CREB3L2, DDR2, IRF2, NCSTN, RECQL, REST, and TGFBR1. Furthermore, we identified two ICD subtypes in LGGs with different clinical, cellular, and molecular characteristics. Icds1 is an immunological "hot" and immunosuppression phenotype with a worse prognosis, while Icds2 is an immunological cold phenotype with a better prognosis. Finally, WGCNA identified hub immune-related genes associated with ICD subtypes, which could be potential vaccination biomarkers. Conclusion: In summary, CREB3L2, DDR2, IRF2, NCSTN, RECQL, REST, and TGFBR1 are LGGs’ potential tumor antigens for mRNA vaccine development. The Icds2 subtype is suitable for vaccination. lower-grade gliomas (LGGs) immunogenic cell death (ICD) subtypes mRNA vaccine tumor antigens immunotherapy tumor immune microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Diffuse gliomas, a set of highly heterogeneous infiltrative brain malignancies, account for the majority of primary central nervous system (CNS) cancers[ 1 ]. Gliomas were classified into grades I–IV based on the World Health Organization (WHO) classification[ 2 , 3 ]. Within them, only grade II and III gliomas were considered lower-grade gliomas (LGGs)[ 4 ]. Unlike grade I glioma (considered benign lesions), LGGs are aggressive and cannot be completely removed by neurosurgery, usually evolving into recurrence or directly progressing to higher-grade neoplasms[ 5 ]. Although there has been an explosive innovation in radiotherapy, chemotherapy, and targeted therapies for LGGs over the past decades, unfortunately, the responses to therapies vary widely and the prognosis differs from person to person[ 5 , 6 ]. Therefore, novel strategies, which are better at improving the treatment and making personalized clinical decisions, are an urgent research focus. Recently, therapeutic tumor vaccines, as a hotspot in tumor immunotherapy, reported to be effective against several cancers and have attracted extensive concerns from oncologists and scientists[ 7 , 8 ]. These tumor vaccines specifically attack and further eliminate malignant cells expressing tumor-specific antigens or tumor-associated antigens, and, eventually achieve chronic therapeutic effects on the basis of immune memory[ 9 ]. Tumor vaccines are composed mainly of tumor antigens without or with adjuvants, and they offer the advantages of a broad treatment window, relative nontoxicity, minimal non-specific influence, and a durable immune memory, which could be possible to win the challenge of drug resistance, restricted therapeutic effects, increased costs and potential side effects related to standard immunotherapy and chemotherapy[ 10 ]. According to the form of antigens, tumor vaccines could be classified into five categories: DNA, tumor cell, peptide, dendritic cell, and RNA[ 11 , 12 ]. However, in clinical application, mRNA vaccines have several major advantages[ 7 ] compared to the other four types. Such as easy to design and produce, relatively low cost, stronger and persistent immune responses[ 13 ], a regulatable and short half-life, and safety with few potential gene insertional mutations or risk of infection. More importantly, mRNA vaccines have been demonstrated to be effective in fighting against multiple tumors, such as prostate cancer[ 14 ], melanoma[ 15 ], and colorectal cancer[ 16 ]. However, for patients with LGGs, the application of the mRNA vaccine remains largely uncharacterized and needs to be explored. Furthermore, immunogenic cell death (ICD), a modality of regulated cell death (RCD), is enough to drive an adaptive immune response under specific circumstances followed by the development of long-term immunological memory[ 17 ]. Adjuvanticity and antigenicity are two characteristics necessary for ICD cells to be able to induce an effective immune response[ 18 ]. A key characteristic of triggering ICD is the exposure and release of damage-associated molecular patterns (DAMPs) of dying or dead cells to the microenvironment, which is potent endogenous adjuvants[ 17 ]. These DAMPs include but are not limited to calreticulin (CRT), high mobility group protein B1 (HMGB1) [ 19 ], adenosine triphosphate (ATP), and heat shock proteins (HSPs) 70/90[ 20 , 21 ]. Antigenicity is a process that dendritic cells are attracted by the expression of DAMPs to engulf fragments of dying cells and bind antigenic peptides to the major histocompatibility complexes (MHCs)[ 22 ]. Due to the immune surveillance evasion of tumor cells, ICD has developed into a promising strategy for tumor therapy[ 19 ] because of its precision and antigen-specific clearance. Previous studies have observed that some anti-tumor therapies such as chemotherapy[ 23 ], radiotherapy[ 24 ], photodynamic therapy[ 25 ], photothermal therapy[ 26 ], and immunotherapy[ 27 ] could trigger ICD to enhance the antitumor immune response. Thus, a comprehensive understanding of the molecular features of ICD may be beneficial to elucidate the relationship between ICD and the microenvironment infiltration characteristics of LGGs and its impact on immunotherapy, thereby providing novel insights into the development of mRNA vaccines and identification of appropriate candidate recipients for vaccination. Unfortunately, to our knowledge, there is a lack of such studies integrating LGGs mRNA vaccine development and elucidation of the molecular features of ICDs. In the present study, by screening potential LGGs antigens, we identified seven over-expressed, mutated, and amplified cancer genes as the targets for mRNA vaccine development, which were closely associated with poor prognosis and infiltration of antigen-presenting cells (APCs). Meanwhile, considering the tumor heterogeneity and tumor immune microenvironment (TIME) complexity, we developed two immunogenic cell death subtypes to identify LGG populations more suitable for vaccination and more likely to benefit. Subsequently, we observed that the two ICD subtypes exhibited distinct prognosis, molecular, clinical, and immune microenvironment infiltrating characteristics. These findings were consistent in TCGA and CGGA repositories. In addition, we observed that the subtype Icds1 was highly associated with activated ICD, increased expression of seven tumor antigens and higher tumor mutation burden (TMB), which revealed that the subtype Icds1 were the candidates who were suitable for vaccination against LGGs. These findings provided a novel insight into development of mRNA vaccine and a powerful reference for selecting vaccine beneficiaries. 2. Materials and Methods 2.1 Data source and preprocessing Open LGG RNA sequencing data with complete survival information were retrospectively enrolled in this study. Additionally, patients whose survival time was smaller than 30 days were excluded. Ultimately, a total of two datasets and matching clinical annotations were collected, including the log2(x + 1) normalized TCGA-LGG dataset downloaded from the UCSC Xena website ( https://xena.ucsc.edu/ ), and the CGGA-mRNAseq_693 obtained from the Chinese Glioma Genome Atlas (CGGA) official website (CGGA, http://www.cgga.org.cn/index.jsp ). Moreover, the CGGA datasets were also normalized by log2(x + 1) transformed. In addition, immune-related genes were downloaded and integrated from the InnateDB ( https://www.innatedb.ca/ ) and ImmPort ( https://www.immport.org/shared/home ) databases. 2.2 GEPIA analysis We used the Gene Expression Profiling Interactive Analysis[ 28 ] (GEPIA2, http://gepia2.cancer-pku.cn ) to identify the overexpressed genes in LGGs with the cutoff of |log2FC| values > 1 and q < 0.01. Besides, the differential expression analysis method was set to LIMMA, and the gene expression profiles were from the TCGA and the Genotype-Tissue Expression (GTEx) database. 2.3 cBioPortal analysis By applied the cBioPortal for Cancer Genomics[ 29 ] (cBioPortal, http://www.cbioportal.org ), we extracted gene copy number variation (CNV) and gene mutation data of LGG patients in TCGA database to identify the amplified cancer genes, and visualized the genome alteration status and mutation frequency status. In addition, the disease-free survival (DFS) and disease-specifical survival (DSS) of the LGG patients from TCGA cohort were downloaded from this Portal. 2.4 TIMER analysis The Tumor Immune Estimation Resource (TIMER[ 30 ], https://cistrome.shinyapps.io/timer/ ) is an advanced tool for comprehensive analysis of the immune infiltration in multiple cancer types. To explore the relationship between tumor antigens and the infiltration of APCs, a TIMER analysis was conducted by using Spearman’s correlation with its statistical cutoff p value of 0.05. 2.5 Construction and validation of the immunogenic cell death subtypes A previous study summarized ICD genes by retrieving extensive literature[ 31 ], and we extracted them from this study. Eventually, a total of 18 prognostic ICD genes via univariate Cox analysis were included in this study. TCGA-LGG dataset was used in this step, and patients were clustered into distinct molecular subtypes based on the expression of the 18 ICD genes by consensus clustering[ 32 , 33 ]. In order to ensure stable clustering results, “ConsensusClusterPlus” package was utilized to perform 1000 times repetitions. Subsequently, the T-distributed stochastic neighbor embedding (t-SNE)[ 34 , 35 ] was applied to confirm the accuracy of the clustering assignments. To validate the clustered subtypes, consensus clustering and t-SNE were subsequently conducted by using the expression of those prognostic ICD genes that shared in CGGA database. 2.6 Gene set variation analysis (GSVA) To ascertain the difference in hallmark gene sets among ICD subtypes, GSVA was conducted by using “GSVA” R package[ 36 ]. The immune pathway-related gene sets were extracted from a published study[ 37 ], including cytotoxicity, immune suppression genes, inflammation, antigen presentation and processing, innate immunity, immune cells recruitment, and adaptive immunity. 2.7 Single sample gene set enrichment analysis (ssGSEA) To evaluate the tumor microenvironment (TME) cell infiltrations and immune functions, we used "GSVA" R package to perform ssGSEA algorithm and calculate the normalized enrichment score (NES) of 23 infiltrating cells and 13 immune functions, which represents the relative abundance of estimated terms. The marker genes of each infiltration cell were obtained from the study of Zhang et al[ 32 ], and the gene set of immune functions were retrieved from another online paper[ 38 ]. 2.8 Functional enrichment analysis Tumor antigen-associated immune genes were defined as immune-related genes with a Pearson's correlation coefficient greater than 0.3 with the antigens. To understand the potential biological functions of those antigens, Gene Ontology (GO) annotation was performed by using the “clusterprofiler”[ 39 ] R package, and enrichment terms with p < 0.05 were considered statistically significant. In addition, we performed weighted gene co-expression network analysis[ 40 ] (WGCNA) to identify the key modules and genes closely associated with ICD subtypes. The potential mechanism of these module genes was annotated by using Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis in a interaction-friendly bioinformatic analysis tool: Sangerbox[ 41 ]. 2.9 Evaluation of tumor mutational burden (TMB), somatic mutation and immunomodulatory characteristics associated with immunogenic cell death subtypes To determine the relationships between somatic mutation and immunogenic cell death subtypes, we used the "maftools"[ 42 ] R package to calculate TMB in Icds1 and Icds2 of the LGG samples in TCGA database and to visualize the somatic mutations landscape of the top 10 most frequently mutated genes. Moreover, immunomodulatory molecules such as MHCs, co-stimulators, and co-inhibitors were obtained from another published study[ 43 ] and were compared between the two subtypes. 2.10 Immunohistochemistry (IHC) This research was approved by the Ethics Committee of Xiangya Hospital, Central South University. Eighty-two glioma tissue samples were donated by patients suffered from primary glioma at the department of neurosurgery, Xiangya Hospital. These glioma tissue samples were manufactured using a tissue microarray. The following antibodies were used for IHC: TGFBR1 (Proteintech, 30117-1-AP), CD40 (Proteintech, 28158-1-AP), and CD86 (Proteintech, 13395-1-AP). The grading rules as follows: The intensity was categorized as follows: 0 (negative), 1 (weak brown), 2 (moderate brown), and 3 (strong brown). The quantity of positive cells was defined as follows: 0 (≤ 10%), 1 (11–25%), 2 (26–50%), 3 (51–75%), and 4 (> 75%). The final IHC score was determined by multiplying the intensity scores with the quantity scores (values, 0–12). Images of the microarray were scored by two independent pathologists blinded to the clinical information. 2.11 Statistical analysis Data processing was performed with R software (version 4.1.1). Kaplan-Meier (K-M) survival curves was drawn with the “survival” R package and tested by log-rank tests. Pearson’s or Spearman’s analysis was applied to conduct correlation tests. The difference between two groups was compared by the Wilcoxon test. Statistical difference of gene mutation between immunogenic cell death subtypes was compared by Chi-square test. 3. Results 3.1 Screening of potential tumor antigens of LGGs To identify potential tumor antigens in LGGs for mRNA vaccine development, 3982 overexpressed genes (Fig. 1 A) were first screened in LGGs samples through GEPIA2. Subsequently, the amplified and mutated cancer genes were filtered by assessing the genome fraction alteration and mutation frequency in individual samples, which showed that low incidence of genome fraction variation and mutation counts (Fig. 1 B, C) were involved in most LGGs patients. These results revealed a low immunogenicity of LGG patients. Figure 1 D, E showed the top 10 highest alteration frequency genes in genome fraction variation and mutation count groups, respectively. Notably, IDH1, TP53, ATRX were the highest alteration genes in both the groups. Eventually, the overexpressed, amplified, and mutated cancer genes were selected for further analysis. 3.2 Identification of tumor antigens associated with LGG prognosis and antigen-presenting cells To identify tumor antigens that play a key role in LGGs progression as the best candidates for mRNA vaccine development. We further performed univariate Cox regression analysis with the statistical p < 0.01 to screen genes closely associated with poorer overall survival (OS), DSS, and PFS of LGGs in TCGA dataset. A total of 42 target genes (Fig. 2 A) were selected to further validate. Next, 14 overlapping genes (Fig. 2 B) significantly related to LGGs prognosis were selected after their prognostic value was confirmed in TCGA dataset by K-M analysis and validated in CGGA dataset through both univariate Cox regression analysis and K-M analysis. As validated and visualized in CGGA dataset (Fig. 2 C-P), patients with high expression of CHEK1, CREB3L2, DDR2, EZH2, GTSE1, IRF2, MKI67, NCSTN, NUF2, RECQL, REST, SMO, SS18, and TGFBR1 were significantly associated with inferior prognosis of LGGs. Overall, these 14 candidate genes were considered to be critical for the development and progression of LGGs. Given the important role of APCs in capturing, processing, and presenting allergens to cognate T cells in the protective immunity[ 44 ], we further identify the antigens closely related to APCs including B cell, macrophage, dendritic cell to determine the optimal targets to develop mRNA vaccine, which was performed by using TIMER comprehensive analysis with its cutoff Spearman correlation coefficient of 0.3. Finally, as shown in Fig. 3 A-G, 7 genes expression including CREB3L2, DDR2, IRF2, NCSTN, RECQL, REST, and TGFBR1 were closely positively correlated with the infiltration of APCs, revealing their potential immune-activating effect and could be directly processed and presented by the APCs. In addition, considering the reinforcing effect of certain co-stimulatory factors on the immune response, we further analyzed the association between the expression of these 7 genes and CD40, CD80, and CD86[ 45 ]. The significantly positive Spearman correlations between these genes and co-stimulatory factors were observed in both TCGA (Fig. 4 A-C) and CGGA ( Figure S1 A-C ) cohorts. Taken together, the 7 identified tumor antigens are likely to trigger a stronger immune response and therefore serve as promising targets for developing mRNA vaccine against LGGs. Subsequently, we selected one well-known gene (TGFBR1) for further experimental validation. Considering the expression of CD80 was relatively lower than CD 40 and CD86 in glioma sample, we only analyzed the correlation between the expression of TGFBR1 and the expression of CD40 or CD86. IHC results of the microarrays displayed that expression of TGFBR1 was positively correlated with the expression of CD40 and CD86 (Fig. 5 A-B). We found that TGFBR1 was the highly expressed in glioma tissues than the peritumoral brain tissues ( Fig. 5 C-D ) . Moreover, the expression of TGFBR1 was also closely correlated the age, IDH status, and 1p/19q status of glioma patients ( Fig. 5 E-F ) . The statistical analysis of the correlation between the expression of TGFBR1 and the expression of CD40 or CD86 were showed in Figure H-I . 3.3 Identification of ICD subtypes of LGGs with distinct expression of ICD modulators and prognosis To fully understand the immunogenic cell death subtypes of LGGs, 481 patients from TCGA cohort were involved in this analytical process. 18 ICD genes with prognostic values were identified by univariate Cox regression, and a network map depicted their Pearson correlation and impact on the prognosis of LGG patients (Fig. 6 A). Next, according to the expression profile of the above 18 ICD genes, consensus clustering analysis was performed to classify patients into two subtypes (Fig. 6 B), designated Icds1, and Icds2. The same results were clustered in the CGGA database (Fig. 6 C). Subsequently, the result of t-SNE in both TCGA (Fig. 6 D) and CGGA (Fig. 6 E) sets confirmed that patients in different subtypes can be completely distinguished. Furthermore, we compared the differences in ICD transcriptional profiles between ICD subtypes and found their obvious higher expression in Icds1. This result kept consistent in TCGA (Fig. 6 F) and CGGA (Fig. 6 G), indicating that ICD subtypes can distinguish the ICD modulators expressional status. Besides, we explored the relationship between ICD subtypes and the six pan-cancer immune subtypes reported in a previous study[ 46 ]. As shown in Fig. 6 H, LGGs was mostly classified into C4 (Lymphocyte Depleted) and C5 (Immunologically Quiet), and Icds1 mainly clustered into C4, while Icds2 mainly involved in C5, illustrating that LGGs differ from other tumors. More importantly, K-M curves revealed that the immunogenic cell death subtypes were prognostically relevant and patients with subtype Icds1 had a significant shortened survival. These results were consistent in both TCGA (Fig. 6 I) and CGGA (Fig. 6 J) cohorts, indicating that the reproducibility and stability of immunogenic cell death subtypes. Therefore, immunogenic cell death typing can be a promising prognostic biomarker and may provide valuable guideline for LGG immunotherapy. 3.4 Cellular and molecular characteristics of the ICD subtypes To better understand the immune status changes of tumor microenvironment underlying distinct ICD subtypes, ssGSEA was first performed to score 23 immune cells and 13 immune functions. We found that immune cell components and immune functions were obviously distinct between ICD subtypes. As shown in Fig. 7 A, subtype Icds1 had much higher scores in both immune-activated cells (such as activated B cell, activated CD8 + T cell, natural killer cell) and immunosuppressive cells (such as myeloid-derived suppressor cells: MDSC, plasmacytoid dendritic cell, regulatory T cell). Similar trends were observed in immune function (Fig. 7 B) including immune promoting and immune inhibiting effects. Moreover, these findings were further confirmed by GSVA scores of immune-related pathways (Fig. 7 C). More importantly, matching significant results were observed in the CGGA cohort (Fig. 7 D-F). Furthermore, consistent with the antigen processing and presentation pathway, MHC molecules are observed highly expressed in the Icds1 subtype in both TCGA (Fig. 8 A) and CGGA (Fig. 8 B) cohorts. Besides, we further compared the expression levels of immunomodulatory molecules (co-stimulators and co-inhibitors) between the ICD subtypes. Corresponding to immune function, the Icds1 subtype showed a higher abundance of these immunomodulators (Fig. 8 C-D), indicating immune activation accompanied by immunosuppression. Therefore, the Icds1 is a phenotype characterized by immunological “hot” and immunosuppression while the Icds2 is a phenotype characterized by immunological “cold”. These results keep consistent with the pan-cancer immune subtyping that Icds1 were mainly enriched in C4 (Lymphocyte Depleted) and Icds2 were mainly enriched in C5 (Immunologically Quiet), illustrating that our ICD typing is reliability. Taken together, our ICD subtyping could reflect the immune status of LGG patients and select suitable candidates for immunotherapy. Icds2 patients, with an immunologically cold phenotype, might be potential candidates for mRNA vaccination, while Icds1, with an immunologically hot and immunosuppressive phenotype, might be suitable for immune checkpoint inhibitors (ICIs) therapy. 3.5 The association between tumor mutational burden (TMB) and ICD Subtypes Since TMB is associated with immune status[ 47 ], it may affect the immunogenicity[ 48 ] of the tumor and the response to immunotherapy[ 49 ]. Hence, in TCGA dataset, we compared the distribution of the ten most frequently mutated genes in the two subtypes. As shown in Fig. 9 A, there were five genes (IDH1, ATRX, CIC, TTN, and EGFR) mutated significantly differently between subtypes. Next, we assessed the TMB and mutation counts between Icds1 and Icds2. Our results showed that TMB (Fig. 9 B) and mutation counts (Fig. 9 C) were both significantly higher in Icds1 than that in Icds2. In addition, we found that the 7 identified tumor antigens including CREB3L2 (Fig. 9 D), DDR2 (Fig. 9 E), IRF2 (Fig. 9 F), NCSTN (Fig. 9 G), RECQL (Fig. 9 H), REST (Fig. 9 I), and TGFBR1 (Fig. 9 J) expressed significantly higher in Icds1 than in Icds2. The same expression trends of those antigens ( Figure S2 A-G ) were observed between ICD subtypes in the CGGA dataset. These findings indicated that the Icds2 had lower immunogenicity and the patients in Icds2 may be more suitable for mRNA vaccination to reinforce the immune response. 3.6 The potential biological mechanisms of the identified tumor antigens in LGGs To explore the potential biological mechanisms and identify the underlying immune-associated pathways of the tumor antigens, we performed Pearson correlation analysis to screen for genes strongly positively associated with identified tumor antigens from 2909 immune-related genes. The cut-off correlation coefficient r is set to be greater than 0.3. As shown in Fig. 10 A-G and Figure S3 A-G , the immune-related biological process (BP) enrichment items of the 7 identified tumor antigens were all involved in immune response-regulation signaling pathway and/or T cell activation. In addition, cytokines act as key messengers for immune cell communication and they can drive many biological processes including inflammatory and immune responses[ 50 ]. And the enrichment items of the 7 identified tumor antigens were all involved in cytokine production and cytokine-mediated signaling pathway in TCGA and CGGA cohorts. Moreover, chemotactic cytokines, signaling molecules, etc. act as critical mediators of inflammation, participate in intercellular communication, condition the immune system's proper response and maintain the normal functioning of the immune system[ 51 ]. According to the GO enrichment analysis, the most enriched molecular function (MF) of CREB3L2 and DDR2 both included cytokine binding, cytokine receptor binding and cytokine receptor activity in the TCGA (Fig. 10 A, B) and CGGA ( Figure S3 A, B ) cohorts. And the enrichment results of IRF2 were both involved in cytokine binding and cytokine receptor binding in those two cohorts (Fig. 10 C, Figure S3 C ). Besides, as shown in Fig. 10 D-G and Figure S3 D-G , the pathway enriched for NCSTN, RECQL, REST and TGFBR1 involved immune receptor activity, cytokine binding, cytokine activity, cytokine receptor binding, cytokine receptor activity or growth factor receptor binding. These results revealed that the 7 identified tumor antigens in LGGs were closely related to immunologic biological processes, which provided a theoretical basis for the development of mRNA vaccines. 3.7 Identification of immune- and ICD subtype-associated hub modules of LGG We performed WGCNA to construct an immune gene co-expression network with the optimal soft threshold of 3 ( Figure S4 A, B ) in the scale-free network. We used dynamic shear tree to cluster and identify gene co-expression modules with the minimum module size of 30 and the height of 0.25 ( Figure S4 C ). As shown in Figure S4 D , 6 gene modules including blue, brown, green, red, turquoise, and yellow were clustered except for the genes in the grey module. Next, we analyzed the module eigengenes in the two ICD subtypes and found that Icds1 showed significantly (p < 0.01) higher eigengenes in the yellow, brown, and turquoise modules ( Figure S4 E ), which were consistent with the findings that Icds1 corresponded to immunologically hot and Icds2 to immunologically cold. Meanwhile, we also observed that the yellow, brown, and turquoise modules were closely associated with overall survival (Fig. 11 A) and ICD subtypes (Fig. 11 B-D). Further biological functions analysis of these module genes showed that the brown module genes were mainly involved in cytokine-cytokine receptor interaction, T cell receptor signaling pathway, natural killer cell mediated cytotoxicity, Th17 cell differentiation, and etc. (Fig. 11 E), while turquoise module genes were enriched in cytokine-cytokine receptor interaction, hematopoietic cell lineage, NF-kappa B signaling pathway (Fig. 11 F). And the yellow module was associated with cytokine-cytokine receptor interaction, natural killer cell mediated cytotoxicity, and several cancer-related pathways such as Jak-STAT signaling pathway (Fig. 11 G). Subsequently, in the prognostic analysis of the modules, we founded that the module eigengenes of the yellow, brown, and turquoise modules were all significantly related to the prognosis of LGGs and higher expression those module genes correlated with shortened survival (Fig. 11 H-J), which is consistent with above findings. Therefore, patients with higher expression of yellow, brown, and turquoise module genes may not be suitable for mRNA vaccines. Finally, we selected 5 hub genes including HLA-DMB, SPI1, CASP1, ITGB2, and LAIR1 from turquoise module with their module membership (MM) > 0.93, which might be potential biomarkers for selecting optimal mRNA vaccines recipients. 4. Discussion LGG, as common primary intracranial tumor, is one of the most aggressive malignant tumors. Due to its biological heterogeneity and immunosuppressive properties, conventional treatments such as surgical resection, radiotherapy, and chemotherapy are not sufficient to combat their progression[ 52 ]. Immunotherapy is a novel and explosively growing field and the mRNA vaccines are a promising immunotherapy against tumors[ 53 ]. Tumor eradication through vaccine-induced active immunity is the ultimate goal of tumor immunotherapy[ 54 ]. The initial success of a peptide vaccine targeting IDH1 mutants in fighting IDH1 mutant gliomas[ 55 ], has greatly encouraged the development of mRNA vaccines. However, the clinical application of the mRNA vaccine in LGGs is still needed to be explored, thus, we hope our research could provide a valuable reference for the development and application of mRNA vaccine. In the current study, we identified seven potential tumor antigens for developing mRNA vaccine by using the aberrantly expressed profile and exploring the mutational landscape of LGGs. These tumor antigens, including CREB3L2, DDR2, IRF2, NCSTN, RECQL, REST, and TGFBR1, are overexpressed, mutated, and amplified in LGGs and are promising targets for developing mRNA vaccine. Their high expression was not only correlated with shortened survival but also significantly correlated with the high infiltration of APCs, demonstrating their key role in the invasion and progression of LGGs, and the useful value as vaccine candidates. Although further experimental verification and clinical evaluation are still urgently needed, their potential value as mRNA vaccine candidates were supported in available studies. CREB3L2, a member of the CREB3 transcription factor family, is characterized in cancer by the formation of the chimeric gene FUS-CREB3L2, which is responsible for low-grade fibromyxoid sarcoma [ 56 ]. While, in malignant glioma, CREB3L2 binds directly to the ATF5 promoter, leading to ATF5 transcription and thus exerting an anti-apoptotic effect[ 57 ]. DDR2, a member of tyrosine kinase receptors (TKRs) families, is probably a potential molecular biomarker for multiple types of cancer[ 58 ]. It has been demonstrated to promote a variety of tumor cells proliferation, such as lung cancer[ 59 ], lymphoma[ 60 ], and oral squamous cell carcinoma[ 61 ] among others. In addition, its overexpression was positively correlated with poor prognosis of patients[ 62 ]. Besides, the mutation of DDR2 was also closely related to the formation in some tumors[ 58 ]. It has been shown that there is a significant positive correlation between the expression level of IRF2 and the grade of glioma, and that its overexpression enhances the invasion and migration of glioma cells, and vice versa[ 63 ]. Nicastrin (NCSTN) is one of a core subunit of γ-Secretase, which was found to be closely associated with the tumor progression of a variety of tumors[ 64 , 65 ]. RECQL, namely RECQ1 or RECQL1, played critical role in ensuring chromosomal stability[ 66 ]. However, it was high expressed in transformed or a variety of cancer cells, for instance, multiple myeloma[ 67 ], hematological cancers[ 68 ], glioblastoma[ 69 ], and so on, showing that RECQL may be involved in tumorigenesis. Repressor element silencing transcription factor (REST), a well-known transcription repressor, has been reported to be an oncogene and be related to poor prognosis in glioma[ 70 ]. In addition, its upregulation was associated with larger tumor size, higher grade and worse therapeutic efficacy in glioma[ 71 ]. TGF-β receptor 1 (TGFBR1), a member of the TGF-β signaling pathway, has been increasingly shown to promote epithelial-mesenchymal transition (EMT) and the proliferation and migration of tumor cells[ 72 ]. And it was also found to be high expressed in a variety of tumors and involved in the progression and metastasis of tumor[ 73 , 74 ]. More importantly, IHC staining showed that TGFBR1 is higher expressed in glioma samples and positively correlated with the co-stimulatory factors, which indicated that TGFBR1 may serve as an important tumor antigen for vaccine development. Given the critical role of ICDs in antitumor immunotherapy[ 75 , 76 ] and the fact that mRNA vaccines are beneficial for only a small subset of cancer patients. Thus, we classified LGG patients into two ICD subtypes based on the ICD gene profile to select optimal populations for mRNA vaccine. To our knowledge, this is the first article on ICD-based typing to screen for optimal vaccine recipients with LGGs. The two ICD subtypes characterized by a distinct ICD transcription profiles expression, clinical prognosis, molecular and cellular features. Compared to Icds1, Icds2 had a prolonged survival, indicating that ICD typing could be a potential prognostic biomarker for LGGs. More importantly, different molecular, and cellular features were observed between ICD subtypes, indicating that distinct mechanisms regulated the tumor immune environment between the subtypes and distinct therapeutic strategies are required for different ICD subtype patients. We analyzed the TMB and gene mutation count differences between ICD subtypes and found a significantly higher TMB, gene mutated counts, TP53 mutation rate and a lower IDH mutation rate were shown in the Icds1 subtype. As shown in the publications[ 77 , 78 ], LGGs with wild-type IDH and TP53 mutation has a poor prognosis, which were consistent with our findings. In addition, high TMB has the potential to generate immunogenic neoantigens[ 79 ] and further drive APCs and immune effector cells infiltration. However, Icds1 exhibited significantly higher ICD modulators, infiltration of APCs, and immune effector cells such as natural killer cell, activated B cell, and activated CD8 + T cell, it had obviously poorer prognosis. These results indicated that immunosuppression may play a dominant role in the regulation of the tumor microenvironment in this subtype. Consistent with this hypothesis, immunosuppressive cells (for instance MDSC, and regulatory T cell), immune inhibiting functions, and immunosuppression signaling pathways were all enriched in Icds1 subtype. The immunosuppressive tumor microenvironment of Icds1 may inhibit the effective immune response elicited by mRNA vaccine. Thus, the immunologically "hot" and immunosuppressive phenotype of Icds1, which is not suitable for mRNA vaccine alone but more suitable for immune checkpoint blockade (ICB), or Icds1 were likely to benefit from the strategy with mRNA vaccine combination with ICB. Indeed, TMB is becoming a potential biomarker for predicting response to ICB[ 80 ]. In contrast, Icds2 was immunologically "cold" phenotype with lower level of immune infiltration cells, immune function, and immune-related signaling pathway. Since mRNA vaccine could elicit an effective immune response, it might be more suitable for patients with low infiltrated immune cells. Hence, Icds2 but not Icds1 were the optimal candidates for mRNA vaccine in LGGs. To validate the reliability of our ICD subtypes, we further explored the association between ICD subtypes and six reported pan-cancer immune subtypes and found consistent results that C4 (lymphocyte depleted) was mainly enriched in Icds1, while C5 (immunologically quiet) was mainly clustered in Icds2. Taken together, our ICD subtypes could not only predict survival of LGG patients, but also select optimal candidates for mRNA vaccine. Specifically, Icds2 is an immunological “cold” phenotype and is suitable for mRNA vaccine. Considering that ICD subtype may be not stable among different patient populations and the biomarkers of ICD subtypes are the key of linkage candidate population screening, typing specificity, and mechanism research[ 54 ]. Hence, we constructed immune gene co-expression network by using WGCNA and identified six ICD subtype-associated gene modules. Among them, the yellow, brown, and turquoise modules were significantly related to overall survival. Finally, based on the threshold MM value of larger than 0.93, we identified 5 hub genes including HLA-DMB, SPI1, CASP1, ITGB2, and LAIR1 from turquoise module, which might be potential biomarkers for predicting response to mRNA vaccine. 5. Conclusion Taken together, CREB3L2, DDR2, IRF2, NCSTN, RECQL, REST, and TGFBR1 are candidate tumor antigens for mRNA vaccine development in LGGs, and patients with Icds2 subtype of LGGs are the population more likely to benefit from mRNA vaccination. These finding in our study provide a theoretical framework for future developing mRNA vaccine against LGGs, predicting prognosis and responses to mRNA vaccination, selecting suitable recipients and designing better individualized immunotherapeutic strategy. Declarations Declaration of interest statement The authors declare no conflicts of interest in this manuscript. Data Availability Statement The data source of the manuscript is from the public database. The details were displayed in the materials and methods section. Other data could contact with the corresponding authors. Funding Statement This study was supported by the National Natural Science Foundation of China (81472355), and Provincial Natural Science Foundation of Hunan (2022JJ30931). Author Contributions GT, CR and XJ conceived the study. WY collected and analyzed data. DX visualized figures. GT and WYwrote the manuscript. All authors reviewed and approved the submitted manuscript. Acknowledgments We sincerely thank the CGGA and TCGA databases for freely providing the transcriptomic information of LGG samples. References Bao Z, Wang Y, Wang Q, Fang S, Shan X, Wang J, Jiang T: Intratumor heterogeneity, microenvironment, and mechanisms of drug resistance in glioma recurrence and evolution . Front Med 2021, 15 (4):551-561.doi.org/10.1007/s11684-020-0760-2. Horbinski C, Berger T, Packer RJ, Wen PY: Clinical implications of the 2021 edition of the WHO classification of central nervous system tumours . Nat Rev Neurol 2022, 18 (9):515-529.doi.org/10.1038/s41582-022-00679-w. Johnson DR, Giannini C, Vaubel RA, Morris JM, Eckel LJ, Kaufmann TJ, Guerin JB: A Radiologist's Guide to the 2021 WHO Central Nervous System Tumor Classification: Part I-Key Concepts and the Spectrum of Diffuse Gliomas . Radiology 2022, 304 (3):494-508.doi.org/10.1148/radiol.213063. Youssef G, Miller JJ: Lower Grade Gliomas . Curr Neurol Neurosci Rep 2020, 20 (7):21.doi.org/10.1007/s11910-020-01040-8. Aoki K, Nakamura H, Suzuki H, Matsuo K, Kataoka K, Shimamura T, Motomura K, Ohka F, Shiina S, Yamamoto T et al : Prognostic relevance of genetic alterations in diffuse lower-grade gliomas . Neuro Oncol 2018, 20 (1):66-77.doi.org/10.1093/neuonc/nox132. Berzero G, Di Stefano AL, Ronchi S, Bielle F, Villa C, Guillerm E, Capelle L, Mathon B, Laurenge A, Giry M et al : IDH-wildtype lower-grade diffuse gliomas: the importance of histological grade and molecular assessment for prognostic stratification . Neuro-oncology 2021, 23 (6):955-966.doi.org/10.1093/neuonc/noaa258. Pardi N, Hogan MJ, Porter FW, Weissman D: mRNA vaccines - a new era in vaccinology . Nat Rev Drug Discov 2018, 17 (4):261-279.doi.org/10.1038/nrd.2017.243. Olin MR, Ampudia-Mesias E, Pennell CA, Sarver A, Chen CC, Moertel CL, Hunt MA, Pluhar GE: Treatment Combining CD200 Immune Checkpoint Inhibitor and Tumor-Lysate Vaccination after Surgery for Pet Dogs with High-Grade Glioma . Cancers (Basel) 2019, 11 (2).doi.org/10.3390/cancers11020137. DeMaria PJ, Bilusic M: Cancer Vaccines . Hematol Oncol Clin North Am 2019, 33 (2):199-214.doi.org/10.1016/j.hoc.2018.12.001. Zirlik KM, Zahrieh D, Neuberg D, Gribben JG: Cytotoxic T cells generated against heteroclitic peptides kill primary tumor cells independent of the binding affinity of the native tumor antigen peptide . Blood 2006, 108 (12):3865-3870.doi.org/10.1182/blood-2006-04-014415. Bouzid R, Peppelenbosch M, Buschow SI: Opportunities for Conventional and in Situ Cancer Vaccine Strategies and Combination with Immunotherapy for Gastrointestinal Cancers, A Review . Cancers (Basel) 2020, 12 (5).doi.org/10.3390/cancers12051121. Fan Q, Ma Q, Bai J, Xu J, Fei Z, Dong Z, Maruyama A, Leong KW, Liu Z, Wang C: An implantable blood clot-based immune niche for enhanced cancer vaccination . Sci Adv 2020, 6 (39).doi.org/10.1126/sciadv.abb4639. Nguyen QT, Kwak C, Lee WS, Kim J, Jeong J, Sung MH, Yang J, Poo H: Poly-γ-Glutamic Acid Complexed With Alum Induces Cross-Protective Immunity of Pandemic H1N1 Vaccine . Front Immunol 2019, 10 :1604.doi.org/10.3389/fimmu.2019.01604. Rausch S, Schwentner C, Stenzl A, Bedke J: mRNA vaccine CV9103 and CV9104 for the treatment of prostate cancer . Hum Vaccin Immunother 2014, 10 (11):3146-3152.doi.org/10.4161/hv.29553. Wang Y, Zhang L, Xu Z, Miao L, Huang L: mRNA Vaccine with Antigen-Specific Checkpoint Blockade Induces an Enhanced Immune Response against Established Melanoma . Mol Ther 2018, 26 (2):420-434.doi.org/10.1016/j.ymthe.2017.11.009. Shahnazari M, Samadi P, Pourjafar M, Jalali A: Therapeutic vaccines for colorectal cancer: The progress and future prospect . Int Immunopharmacol 2020, 88 :106944.doi.org/10.1016/j.intimp.2020.106944. Galluzzi L, Vitale I, Warren S, Adjemian S, Agostinis P, Martinez AB, Chan TA, Coukos G, Demaria S, Deutsch E et al : Consensus guidelines for the definition, detection and interpretation of immunogenic cell death . J Immunother Cancer 2020, 8 (1).doi.org/10.1136/jitc-2019-000337. Rodrigues MC, Morais JAV, Ganassin R, Oliveira GRT, Costa FC, Morais AAC, Silveira AP, Silva VCM, Longo JPF, Muehlmann LA: An Overview on Immunogenic Cell Death in Cancer Biology and Therapy . Pharmaceutics 2022, 14 (8).doi.org/10.3390/pharmaceutics14081564. Li Q, Chen C, Kong J, Li L, Li J, Huang Y: Stimuli-responsive nano vehicle enhances cancer immunotherapy by coordinating mitochondria-targeted immunogenic cell death and PD-L1 blockade . Acta Pharm Sin B 2022, 12 (5):2533-2549.doi.org/10.1016/j.apsb.2021.11.005. Kim R, Kin T: Current and Future Therapies for Immunogenic Cell Death and Related Molecules to Potentially Cure Primary Breast Cancer . Cancers (Basel) 2021, 13 (19).doi.org/10.3390/cancers13194756. Birmpilis AI, Paschalis A, Mourkakis A, Christodoulou P, Kostopoulos IV, Antimissari E, Terzoudi G, Georgakilas AG, Armpilia C, Papageorgis P et al : Immunogenic Cell Death, DAMPs and Prothymosin α as a Putative Anticancer Immune Response Biomarker . Cells 2022, 11 (9).doi.org/10.3390/cells11091415. Iulianna T, Kuldeep N, Eric F: The Achilles' heel of cancer: targeting tumors via lysosome-induced immunogenic cell death . Cell Death Dis 2022, 13 (5):509.doi.org/10.1038/s41419-022-04912-8. Du B, Waxman DJ: Medium dose intermittent cyclophosphamide induces immunogenic cell death and cancer cell autonomous type I interferon production in glioma models . Cancer Lett 2020, 470 :170-180.doi.org/10.1016/j.canlet.2019.11.025. Zhou H, Tu C, Yang P, Li J, Kepp O, Li H, Zhang L, Zhang L, Zhao Y, Zhang T et al : Carbon ion radiotherapy triggers immunogenic cell death and sensitizes melanoma to anti-PD-1 therapy in mice . Oncoimmunology 2022, 11 (1):2057892.doi.org/10.1080/2162402x.2022.2057892. Song H, Cai Z, Li J, Xiao H, Qi R, Zheng M: Light triggered release of a triple action porphyrin-cisplatin conjugate evokes stronger immunogenic cell death for chemotherapy, photodynamic therapy and cancer immunotherapy . J Nanobiotechnology 2022, 20 (1):329.doi.org/10.1186/s12951-022-01531-5. Xiang Q, Yang C, Luo Y, Liu F, Zheng J, Liu W, Ran H, Sun Y, Ren J, Wang Z: Near-Infrared II Nanoadjuvant-Mediated Chemodynamic, Photodynamic, and Photothermal Therapy Combines Immunogenic Cell Death with PD-L1 Blockade to Enhance Antitumor Immunity . Small 2022, 18 (13):e2107809.doi.org/10.1002/smll.202107809. Xie L, Ding Y, Zhang X, Zhang Z, Zeng S, Wang L, Yang Z, Liu Q, Hu ZW: A Cascade-Targeted Enzyme-Instructed Peptide Self-Assembly Strategy for Cancer Immunotherapy through Boosting Immunogenic Cell Death . Small Methods 2023:e2201416.doi.org/10.1002/smtd.202201416. Tang Z, Kang B, Li C, Chen T, Zhang Z: GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis . Nucleic Acids Res 2019, 47 (W1):W556-w560.doi.org/10.1093/nar/gkz430. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E et al : Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal . Sci Signal 2013, 6 (269):pl1.doi.org/10.1126/scisignal.2004088. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS: TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells . Cancer Res 2017, 77 (21):e108-e110.doi.org/10.1158/0008-5472.Can-17-0307. Garg AD, De Ruysscher D, Agostinis P: Immunological metagene signatures derived from immunogenic cancer cell death associate with improved survival of patients with lung, breast or ovarian malignancies: A large-scale meta-analysis . Oncoimmunology 2016, 5 (2):e1069938.doi.org/10.1080/2162402x.2015.1069938. Zhang B, Wu Q, Li B, Wang D, Wang L, Zhou YL: m(6)A regulator-mediated methylation modification patterns and tumor microenvironment infiltration characterization in gastric cancer . Mol Cancer 2020, 19 (1):53.doi.org/10.1186/s12943-020-01170-0. Tang G, Tan L, Yuan H, Yin W: Glycosylation modification patterns reveal distinct tumor metabolism and immune microenvironment landscape in lower-grade gliomas . Front Cell Dev Biol 2022, 10 :886989.doi.org/10.3389/fcell.2022.886989. Zhou H, Wang F, Tao P: t-Distributed Stochastic Neighbor Embedding Method with the Least Information Loss for Macromolecular Simulations . J Chem Theory Comput 2018, 14 (11):5499-5510.doi.org/10.1021/acs.jctc.8b00652. Mariet C, Castel D, Grill J, Saffroy R, Dangouloff-Ros V, Boddaert N, Llamas-Guttierrez F, Chappé C, Puget S, Hasty L et al : Posterior fossa ependymoma H3 K27-mutant: an integrated radiological and histomolecular tumor analysis . Acta Neuropathol Commun 2022, 10 (1):137.doi.org/10.1186/s40478-022-01442-4. Hänzelmann S, Castelo R, Guinney J: GSVA: gene set variation analysis for microarray and RNA-seq data . BMC Bioinformatics 2013, 14 :7.doi.org/10.1186/1471-2105-14-7. Nguyen CT, Caruso S, Maille P, Beaufrère A, Augustin J, Favre L, Pujals A, Boulagnon-Rombi C, Rhaiem R, Amaddeo G et al : Immune Profiling of Combined Hepatocellular- Cholangiocarcinoma Reveals Distinct Subtypes and Activation of Gene Signatures Predictive of Response to Immunotherapy . Clin Cancer Res 2022, 28 (3):540-551.doi.org/10.1158/1078-0432.Ccr-21-1219. Bindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, Angell H, Fredriksen T, Lafontaine L, Berger A et al : Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer . Immunity 2013, 39 (4):782-795.doi.org/10.1016/j.immuni.2013.10.003. Yu G, Wang LG, Han Y, He QY: clusterProfiler: an R package for comparing biological themes among gene clusters . Omics 2012, 16 (5):284-287.doi.org/10.1089/omi.2011.0118. Tang G, Peng J, Huo L, Yin W: An N6-methyladenosine regulation- and mRNAsi-related prognostic index reveals the distinct immune microenvironment and immunotherapy responses in lower-grade glioma . BMC Bioinformatics 2023, 24 (1):225.doi.org/10.1186/s12859-023-05328-7. Shen W, Song Z, Zhong X, Huang M, Shen D, Gao P, Qian X, Wang M, He X, Wang T et al : Sangerbox: A comprehensive, interaction-friendly clinical bioinformatics analysis platform . iMeta 2022, 1 (3):e36.doi.org/https://doi.org/10.1002/imt2.36. Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP: Maftools: efficient and comprehensive analysis of somatic variants in cancer . Genome Res 2018, 28 (11):1747-1756.doi.org/10.1101/gr.239244.118. Lin Y, Pan X, Zhao L, Yang C, Zhang Z, Wang B, Gao Z, Jiang K, Ye Y, Wang S et al : Immune cell infiltration signatures identified molecular subtypes and underlying mechanisms in gastric cancer . NPJ Genom Med 2021, 6 (1):83.doi.org/10.1038/s41525-021-00249-x. Wculek SK, Cueto FJ, Mujal AM, Melero I, Krummel MF, Sancho D: Dendritic cells in cancer immunology and immunotherapy . Nat Rev Immunol 2020, 20 (1):7-24.doi.org/10.1038/s41577-019-0210-z. Sugiura D, Maruhashi T, Okazaki IM, Shimizu K, Maeda TK, Takemoto T, Okazaki T: Restriction of PD-1 function by cis-PD-L1/CD80 interactions is required for optimal T cell responses . Science 2019, 364 (6440):558-566.doi.org/10.1126/science.aav7062. Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, Porta-Pardo E, Gao GF, Plaisier CL, Eddy JA et al : The Immune Landscape of Cancer . Immunity 2018, 48 (4):812-830.e814.doi.org/10.1016/j.immuni.2018.03.023. Wang H, Xu T, Huang Q, Jin W, Chen J: Immunotherapy for Malignant Glioma: Current Status and Future Directions . Trends Pharmacol Sci 2020, 41 (2):123-138.doi.org/10.1016/j.tips.2019.12.003. Hodges TR, Ott M, Xiu J, Gatalica Z, Swensen J, Zhou S, Huse JT, de Groot J, Li S, Overwijk WW et al : Mutational burden, immune checkpoint expression, and mismatch repair in glioma: implications for immune checkpoint immunotherapy . Neuro Oncol 2017, 19 (8):1047-1057.doi.org/10.1093/neuonc/nox026. Sha D, Jin Z, Budczies J, Kluck K, Stenzinger A, Sinicrope FA: Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors . Cancer Discov 2020, 10 (12):1808-1825.doi.org/10.1158/2159-8290.Cd-20-0522. Zipp F, Bittner S, Schafer DP: Cytokines as emerging regulators of central nervous system synapses . Immunity 2023, 56 (5):914-925.doi.org/10.1016/j.immuni.2023.04.011. Piekarska K, Bonowicz K, Grzanka A, Jaworski Ł M, Reiter RJ, Slominski AT, Steinbrink K, Kleszczyński K, Gagat M: Melatonin and TGF-β-Mediated Release of Extracellular Vesicles . Metabolites 2023, 13 (4).doi.org/10.3390/metabo13040575. Xu S, Tang L, Li X, Fan F, Liu Z: Immunotherapy for glioma: Current management and future application . Cancer Lett 2020, 476 :1-12.doi.org/10.1016/j.canlet.2020.02.002. Song YC, Huang HC, Chang CY, Lee HJ, Liu CT, Lo HY, Ho TY, Lin WC, Yen HR: A Potential Herbal Adjuvant Combined With a Peptide-Based Vaccine Acts Against HPV-Related Tumors Through Enhancing Effector and Memory T-Cell Immune Responses . Front Immunol 2020, 11 :62.doi.org/10.3389/fimmu.2020.00062. Ye L, Wang L, Yang J, Hu P, Zhang C, Tong S, Liu Z, Tian D: Identification of tumor antigens and immune subtypes in lower grade gliomas for mRNA vaccine development . J Transl Med 2021, 19 (1):352.doi.org/10.1186/s12967-021-03014-x. Platten M, Bunse L, Wick A, Bunse T, Le Cornet L, Harting I, Sahm F, Sanghvi K, Tan CL, Poschke I et al : A vaccine targeting mutant IDH1 in newly diagnosed glioma . Nature 2021, 592 (7854):463-468.doi.org/10.1038/s41586-021-03363-z. Lee AF, Yip S, Smith AC, Hayes MM, Nielsen TO, O'Connell JX: Low-grade fibromyxoid sarcoma of the perineum with heterotopic ossification: case report and review of the literature . Hum Pathol 2011, 42 (11):1804-1809.doi.org/10.1016/j.humpath.2011.01.023. Sampieri L, Di Giusto P, Alvarez C: CREB3 Transcription Factors: ER-Golgi Stress Transducers as Hubs for Cellular Homeostasis . Front Cell Dev Biol 2019, 7 :123.doi.org/10.3389/fcell.2019.00123. Elkamhawy A, Lu Q, Nada H, Woo J, Quan G, Lee K: The Journey of DDR1 and DDR2 Kinase Inhibitors as Rising Stars in the Fight Against Cancer . Int J Mol Sci 2021, 22 (12).doi.org/10.3390/ijms22126535. Kim D, You E, Jeong J, Ko P, Kim JW, Rhee S: DDR2 controls the epithelial-mesenchymal-transition-related gene expression via c-Myb acetylation upon matrix stiffening . Sci Rep 2017, 7 (1):6847.doi.org/10.1038/s41598-017-07126-7. Deng L, Liu G, Zheng C, Zhang L, Kang Y, Yang F: Circ-LAMP1 promotes T-cell lymphoblastic lymphoma progression via acting as a ceRNA for miR-615-5p to regulate DDR2 expression . Gene 2019, 701 :146-151.doi.org/10.1016/j.gene.2019.03.052. Sun M, Shen Z: Knockdown of Long Non-Coding RNA (lncRNA) Colon Cancer-Associated Transcript-1 (CCAT1) Suppresses Oral Squamous Cell Carcinoma Proliferation, Invasion, and Migration by Inhibiting the Discoidin Domain Receptor 2 (DDR2)/ERK/AKT Axis . Med Sci Monit 2020, 26 :e920020.doi.org/10.12659/msm.920020. Gao Y, Zhou J, Li J: Discoidin domain receptors orchestrate cancer progression: A focus on cancer therapies . Cancer Sci 2021, 112 (3):962-969.doi.org/10.1111/cas.14789. Tong S, Ye L, Xu Y, Sun Q, Gao L, Cai J, Ye Z, Tian D, Chen Q: IRF2-ferroptosis related gene is associated with prognosis and EMT in gliomas . Transl Oncol 2022, 26 :101544.doi.org/10.1016/j.tranon.2022.101544. Li H, Lan T, Xu L, Liu H, Wang J, Li J, Chen X, Huang J, Li X, Yuan K et al : NCSTN promotes hepatocellular carcinoma cell growth and metastasis via β-catenin activation in a Notch1/AKT dependent manner . J Exp Clin Cancer Res 2020, 39 (1):128.doi.org/10.1186/s13046-020-01638-3. Filipović A, Lombardo Y, Faronato M, Abrahams J, Aboagye E, Nguyen QD, d'Aqua BB, Ridley A, Green A, Rahka E et al : Anti-nicastrin monoclonal antibodies elicit pleiotropic anti-tumour pharmacological effects in invasive breast cancer cells . Breast Cancer Res Treat 2014, 148 (2):455-462.doi.org/10.1007/s10549-014-3119-z. Debnath S, Sharma S: RECQ1 Helicase in Genomic Stability and Cancer . Genes (Basel) 2020, 11 (6).doi.org/10.3390/genes11060622. Viziteu E, Klein B, Basbous J, Lin YL, Hirtz C, Gourzones C, Tiers L, Bruyer A, Vincent L, Grandmougin C et al : RECQ1 helicase is involved in replication stress survival and drug resistance in multiple myeloma . Leukemia 2017, 31 (10):2104-2113.doi.org/10.1038/leu.2017.54. Viziteu E, Kassambara A, Pasero P, Klein B, Moreaux J: RECQ helicases are deregulated in hematological malignancies in association with a prognostic value . Biomark Res 2016, 4 :3.doi.org/10.1186/s40364-016-0057-4. Mendoza-Maldonado R, Faoro V, Bajpai S, Berti M, Odreman F, Vindigni M, Ius T, Ghasemian A, Bonin S, Skrap M et al : The human RECQ1 helicase is highly expressed in glioblastoma and plays an important role in tumor cell proliferation . Mol Cancer 2011, 10 :83.doi.org/10.1186/1476-4598-10-83. Wang G, Yang X, Qi M, Li M, Dong M, Xu R, Zhang C: Systematic analysis identifies REST as an oncogenic and immunological biomarker in glioma . Sci Rep 2023, 13 (1):3023.doi.org/10.1038/s41598-023-30248-0. Li C, Zou H, Wang Z, Tang X, Fan X, Zhang K, Liu J, Li Z: REST, not REST4, is a risk factor associated with radiotherapy plus chemotherapy efficacy in glioma . Drug Des Devel Ther 2018, 12 :1363-1371.doi.org/10.2147/dddt.S161602. Wang J, Xiang H, Lu Y, Wu T: Role and clinical significance of TGF ‑β1 and TGF ‑βR1 in malignant tumors (Review) . Int J Mol Med 2021, 47 (4).doi.org/10.3892/ijmm.2021.4888. Jiang F, Yu Q, Chu Y, Zhu X, Lu W, Liu Q, Wang Q: MicroRNA-98-5p inhibits proliferation and metastasis in non-small cell lung cancer by targeting TGFBR1 . Int J Oncol 2019, 54 (1):128-138.doi.org/10.3892/ijo.2018.4610. Zhang L, Song X, Chen X, Wang Q, Zheng X, Wu C, Jiang J: Circular RNA CircCACTIN Promotes Gastric Cancer Progression by Sponging MiR-331-3p and Regulating TGFBR1 Expression . Int J Biol Sci 2019, 15 (5):1091-1103.doi.org/10.7150/ijbs.31533. Yu X, Han N, Dong Z, Dang Y, Zhang Q, Hu W, Wang C, Du S, Lu Y: Combined Chemo-Immuno-Photothermal Therapy for Effective Cancer Treatment via an All-in-One and One-for-All Nanoplatform . ACS Appl Mater Interfaces 2022, 14 (38):42988-43009.doi.org/10.1021/acsami.2c12969. Zhao Y, Liu X, Liu X, Yu J, Bai X, Wu X, Guo X, Liu Z, Liu X: Combination of phototherapy with immune checkpoint blockade: Theory and practice in cancer . Front Immunol 2022, 13 :955920.doi.org/10.3389/fimmu.2022.955920. Fujimoto K, Arita H, Satomi K, Yamasaki K, Matsushita Y, Nakamura T, Miyakita Y, Umehara T, Kobayashi K, Tamura K et al : TERT promoter mutation status is necessary and sufficient to diagnose IDH-wildtype diffuse astrocytic glioma with molecular features of glioblastoma . Acta Neuropathol 2021, 142 (2):323-338.doi.org/10.1007/s00401-021-02337-9. Li J, Lv F, Jin T: Structuring and validating a prognostic model for low-grade gliomas based on the genes for plasma membrane tension . Front Neurol 2022, 13 :1024869.doi.org/10.3389/fneur.2022.1024869. Gubin MM, Artyomov MN, Mardis ER, Schreiber RD: Tumor neoantigens: building a framework for personalized cancer immunotherapy . J Clin Invest 2015, 125 (9):3413-3421.doi.org/10.1172/jci80008. Chan TA, Yarchoan M, Jaffee E, Swanton C, Quezada SA, Stenzinger A, Peters S: Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic . Ann Oncol 2019, 30 (1):44-56.doi.org/10.1093/annonc/mdy495. Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif Figure S1Spearman correlation between the expression levels of identified seven antigens (CREB3L2, DDR2, IRF2, NCSTN, RECQL, REST, and TGFBR1) and those of APC markers in CGGA-LGG cohort. Spearman correlation between the expression levels of the seven tumor antigens and those of (A) CD40, (B) CD80, and (C) CD86. FigureS2.tif Figure S2The differential expression of seven tumor antigens between ICD subtypes in CGGA cohort including (A) CREB3L2, (B) DDR2, (C) IRF2, (D) NCSTN, (E) RECQL, (F) REST, and (G) TGFBR1. FigureS3.tif Figure S3Functional enrichment analysis of seven tumor antigens in CGGA cohort. Gene ontology (GO) enrichment analysis for immune-related genes positively associated with (A) CREB3L2, (B) DDR2, (C) IRF2, (D) NCSTN, (E) RECQL, (F) REST, and (G) TGFBR1. FigureS4.tif Figure S4 Identification of immune gene co-expression modules of LGGs. Scale-free fit index (A), and mean connectivity (B) for various soft thresholding powers. (C) Dendrogram of immune genes clustered on the basis of a dissimilarity measure. (D) Gene counts in each module. (E) The feature vectors distribution of each module between ICD subtypes in LGGs. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3505524","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":243771074,"identity":"d8dac344-f48c-4107-9e1a-235146177691","order_by":0,"name":"Wen Yin","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Yin","suffix":""},{"id":243771075,"identity":"17d31bfc-0f46-4328-ae07-fd151a0ea2a3","order_by":1,"name":"Dongcheng Xie","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dongcheng","middleName":"","lastName":"Xie","suffix":""},{"id":243771076,"identity":"250ec4a6-342a-42d9-a161-2ced60e54156","order_by":2,"name":"Guihua Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYFACxjYYo/HBx382PPz8DURpMQBi5sOGM9jSZCRnHCBoDRtUC1uaNA/bYRuDhgT86uUjktse89T8kTfnX2NswMNznseA4QDjh485uLUY3khsN+Y5ZmC4c8YbwwcSErd5zJkbmCVnbsOjZUZiG9A9BowbbpwxNjAwuM1j2XCAjZmXoJZ/BvZALWYSCQnneAwOJODXIi8B1MLbZpC44XxbmsSBAwcIazHgedgmObfPOHnDDWAgNzYk80jOONiM1y/y7enPJN58k7PdcP5g4+O/DXb2/PzNBz98xGfLARhLIgHGYmzArR5kC1ya/wBuVaNgFIyCUTCyAQC2b1ZdpLIPzgAAAABJRU5ErkJggg==","orcid":"","institution":"Hunan Provincial People's Hospital (The first affiliated hospital of Hunan Normal University, The College of Clinical Medicine of Human Normal University)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Guihua","middleName":"","lastName":"Tang","suffix":""},{"id":243771077,"identity":"f98cfab6-9e5a-4006-ba2d-adb6b1f2abc0","order_by":3,"name":"Caiping Ren","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Caiping","middleName":"","lastName":"Ren","suffix":""},{"id":243771078,"identity":"dcae3df3-6f9d-4b4b-8fc8-9811575f6620","order_by":4,"name":"Xingjun Jiang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xingjun","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2023-10-29 03:44:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3505524/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3505524/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":45588998,"identity":"5cee0581-ab76-433c-bb4c-8ad78fd347d7","added_by":"auto","created_at":"2023-10-31 20:39:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1955056,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of potential tumor antigens in LGGs. (\u003cstrong\u003eA\u003c/strong\u003e) Identifying potential tumor-associated antigens of LGGs. Chromosomal distribution of over-expressed genes in LGGs. (\u003cstrong\u003eB-E\u003c/strong\u003e) Identifying potential tumor-specific antigens of LGGs. Overlapping mutated genes showed in (\u003cstrong\u003eB\u003c/strong\u003e) altered genome fraction and (\u003cstrong\u003eC\u003c/strong\u003e) mutation count groups. Top ten the highest frequency genes in (\u003cstrong\u003eD\u003c/strong\u003e) altered genome fraction and (\u003cstrong\u003eE\u003c/strong\u003e) mutation count groups.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3505524/v1/7c79b6f45b24faffb44334e2.jpg"},{"id":45591316,"identity":"155bcc0c-7f96-4075-9ee5-78086ff1c50f","added_by":"auto","created_at":"2023-10-31 20:47:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1780762,"visible":true,"origin":"","legend":"\u003cp\u003eScreening for tumor antigens associated with LGGs prognosis. (\u003cstrong\u003eA\u003c/strong\u003e) Identification of potential tumor antigens with amplification, mutation and over-expression, which were significantly associated with OS, DSS, and PFS (42 candidates), in TCGA cohort. (\u003cstrong\u003eB\u003c/strong\u003e) Further validating the prognosis of the 42 candidates by K-M analysis in TCGA and through both univariate Cox regression analysis and K-M analysis in CGGA dataset (total 14 intersections). Visualization of Kaplan-Meier curves comparing OS for LGG patients with different expression of (\u003cstrong\u003eC\u003c/strong\u003e) CHEK1, (\u003cstrong\u003eD\u003c/strong\u003e) CREB3L2, (\u003cstrong\u003eE\u003c/strong\u003e) DDR2, (\u003cstrong\u003eF\u003c/strong\u003e) EZH2, (\u003cstrong\u003eG\u003c/strong\u003e) GTSE1, (\u003cstrong\u003eH\u003c/strong\u003e) IRF2, (\u003cstrong\u003eI\u003c/strong\u003e) MKI67, (\u003cstrong\u003eJ\u003c/strong\u003e) NCSTN, (\u003cstrong\u003eK\u003c/strong\u003e) NUF2, (\u003cstrong\u003eL\u003c/strong\u003e) RECQL, (\u003cstrong\u003eM\u003c/strong\u003e) REST, (\u003cstrong\u003eN\u003c/strong\u003e) SMO, (\u003cstrong\u003eO\u003c/strong\u003e) SS18, and (\u003cstrong\u003eP\u003c/strong\u003e) TGFBR1 in CGGA cohort.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3505524/v1/38666c1e6b5c03ac580ca472.jpg"},{"id":45591324,"identity":"03772196-1d6c-40a3-af90-c5c267207bc2","added_by":"auto","created_at":"2023-10-31 20:47:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2482412,"visible":true,"origin":"","legend":"\u003cp\u003eIdentifying tumor antigens strongly associated with APCs (correlation coefficient \u0026gt; 0.3). Spearman correlation between the expression levels of (\u003cstrong\u003eA\u003c/strong\u003e) CREB3L2, (\u003cstrong\u003eB\u003c/strong\u003e) DDR2, (\u003cstrong\u003eC\u003c/strong\u003e) IRF2, (\u003cstrong\u003eD\u003c/strong\u003e) NCSTN, (\u003cstrong\u003eE\u003c/strong\u003e) RECQL, (\u003cstrong\u003eF\u003c/strong\u003e) REST, (\u003cstrong\u003eG\u003c/strong\u003e) TGFBR1 and the infiltration of B cells, macrophages, and dendritic cells in LGGs.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3505524/v1/4b7fbbcf352d9fdfded3258a.jpg"},{"id":45592868,"identity":"47f78a32-506d-4be2-9dff-37b7b3d36926","added_by":"auto","created_at":"2023-10-31 20:55:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1942111,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman correlation between the expression levels of identified seven antigens (CREB3L2, DDR2, IRF2, NCSTN, RECQL, REST, and TGFBR1) and those of APC markers in TCGA-LGG cohort. Spearman correlation between the expression levels of the seven tumor antigens and those of (\u003cstrong\u003eA\u003c/strong\u003e) CD40, (\u003cstrong\u003eB\u003c/strong\u003e) CD80, and (\u003cstrong\u003eC\u003c/strong\u003e) CD86.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3505524/v1/01d7f235fe290b87c1098a54.jpg"},{"id":45589003,"identity":"d44ad22c-d316-486c-9851-69ea9879bba4","added_by":"auto","created_at":"2023-10-31 20:39:31","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3111474,"visible":true,"origin":"","legend":"\u003cp\u003eImmunohistochemical results of tissue microarrays. \u003cstrong\u003e(A)\u003c/strong\u003eIHC staining of TGFBR1 and CD40. \u003cstrong\u003e(B)\u003c/strong\u003eIHC staining of TGFBR1 and CD86.\u003cstrong\u003e (C)\u003c/strong\u003eThe IHC scores of TGFBR1 in different grades of glioma samples.\u003cstrong\u003e (D) \u003c/strong\u003eThe IHC scores of TGFBR1 in paired glioma samples. Associations between TGFBR1 expression and different clinical characteristics: age \u003cstrong\u003e(E)\u003c/strong\u003e, IDH status \u003cstrong\u003e(F)\u003c/strong\u003e, 1p/19q codeletion \u003cstrong\u003e(G)\u003c/strong\u003e. The statistical analysis of the correlation between the expression of TGFBR1 and the expression of CD40 \u003cstrong\u003e(H)\u003c/strong\u003e or CD86 \u003cstrong\u003e(I)\u003c/strong\u003e. *p \u0026lt; 0.05, **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3505524/v1/00fc3547be9195783661fdd1.jpg"},{"id":45591320,"identity":"a95c288f-9a6a-410b-b60e-9d35222d05a9","added_by":"auto","created_at":"2023-10-31 20:47:31","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1802352,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of ICD subtypes in LGGs. (\u003cstrong\u003eA\u003c/strong\u003e) Interactions of prognostic ICD genes in LGGs, the lines connecting genes represent their interactions. Red represents positive correlation, while gray represents negative correlation. The size of the circle represents the significance of univariate Cox regression. Red and green circles represent risk factors and favorable factors, respectively. Consensus clustering analysis of prognostic ICD genes in (\u003cstrong\u003eB\u003c/strong\u003e) TCGA and (\u003cstrong\u003eC\u003c/strong\u003e) CGGA cohorts identified two distinct ICD subtypes. t-SNE confirmed the distribution of patients in different subtypes in both (\u003cstrong\u003eD\u003c/strong\u003e) TCGA and (\u003cstrong\u003eE\u003c/strong\u003e) CGGA cohorts. The distribution of ICD genes expression between the two distinct subtypes in (\u003cstrong\u003eF\u003c/strong\u003e) TCGA and (\u003cstrong\u003eG\u003c/strong\u003e) CGGA cohorts. (\u003cstrong\u003eH\u003c/strong\u003e) Distribution of six recognized pan-cancer immune subtypes in the two TCGA cohort ICD subtypes. K-M analysis showed the significant survival differences between two ICD subtypes in both (\u003cstrong\u003eI\u003c/strong\u003e) TCGA and (\u003cstrong\u003eJ\u003c/strong\u003e) CGGA cohorts.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3505524/v1/e194c43a6cc6f6fc79907173.jpg"},{"id":45588999,"identity":"20c6ddfa-4043-439f-bf79-ec21d1fb4817","added_by":"auto","created_at":"2023-10-31 20:39:30","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1660430,"visible":true,"origin":"","legend":"\u003cp\u003eCellular and molecular characteristics of the ICD subtypes in LGGs. Differences in enrichment scores of (\u003cstrong\u003eA\u003c/strong\u003e) infiltrating immune cells, (\u003cstrong\u003eB\u003c/strong\u003e) immune functions and (\u003cstrong\u003eC\u003c/strong\u003e) immune-related pathways between distinct ICD subtypes in TCGA cohort. Differences in enrichment scores of (\u003cstrong\u003eD\u003c/strong\u003e) infiltrating immune cells, (\u003cstrong\u003eE\u003c/strong\u003e) immune functions and (\u003cstrong\u003eF\u003c/strong\u003e) immune-related pathways between distinct ICD subtypes in CGGA cohort. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3505524/v1/e56fc22f5e4563390e01b4bd.jpg"},{"id":45592869,"identity":"f9bdfb36-ce63-40bf-aaf4-a67d20e0944b","added_by":"auto","created_at":"2023-10-31 20:55:31","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1339537,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of the expression of immunomodulatory molecules between ICD subtypes. Differences in the expression of MHC molecules between the LGGs ICD subtypes in (\u003cstrong\u003eA\u003c/strong\u003e) TCGA and (B) CGGA cohorts. Differences in the expression of co-stimulators and co-inhibitors between the LGGs ICD subtypes in (\u003cstrong\u003eC\u003c/strong\u003e) TCGA and (\u003cstrong\u003eD\u003c/strong\u003e) CGGA cohorts. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3505524/v1/4f4f8d42782bb61451e32c0e.jpg"},{"id":45589009,"identity":"b667eddc-b52d-4558-8f4e-82bfdde294c4","added_by":"auto","created_at":"2023-10-31 20:39:31","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2014676,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between ICD subtypes and gene mutation, TMB, and tumor antigens. (\u003cstrong\u003eA\u003c/strong\u003e) Waterfall plot showing the landscape of the top 10 most frequent genomic alteration genes of LGGs. The distribution of (\u003cstrong\u003eB\u003c/strong\u003e) TMB and (\u003cstrong\u003eC\u003c/strong\u003e) mutation counts were with significant differences between ICD subtypes. The differential expression of seven tumor antigens between ICD subtypes in TCGA cohort including (\u003cstrong\u003eD\u003c/strong\u003e) CREB3L2, (\u003cstrong\u003eE\u003c/strong\u003e) DDR2, (\u003cstrong\u003eF\u003c/strong\u003e) IRF2, (\u003cstrong\u003eG\u003c/strong\u003e) NCSTN, (\u003cstrong\u003eH\u003c/strong\u003e) RECQL, (\u003cstrong\u003eI\u003c/strong\u003e) REST, and (\u003cstrong\u003eJ\u003c/strong\u003e) TGFBR1.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3505524/v1/7390223502fac7ebae17f3c8.jpg"},{"id":45591318,"identity":"45319ee7-c9ac-4b41-922c-c0dbf1fc505a","added_by":"auto","created_at":"2023-10-31 20:47:31","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1255560,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of seven tumor antigens in TCGA cohort. Gene ontology (GO) enrichment analysis for immune-related genes positively associated with (\u003cstrong\u003eA\u003c/strong\u003e) CREB3L2, (\u003cstrong\u003eB\u003c/strong\u003e) DDR2, (\u003cstrong\u003eC\u003c/strong\u003e) IRF2, (\u003cstrong\u003eD\u003c/strong\u003e) NCSTN, (\u003cstrong\u003eE\u003c/strong\u003e) RECQL, (\u003cstrong\u003eF\u003c/strong\u003e) REST, and (\u003cstrong\u003eG\u003c/strong\u003e) TGFBR1.\u003c/p\u003e","description":"","filename":"Figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3505524/v1/12ae0798af790e3f8487ad5c.jpg"},{"id":45589006,"identity":"c32593d3-1654-4aa0-92c4-ff0ea131ee8a","added_by":"auto","created_at":"2023-10-31 20:39:31","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1463285,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of ICD subtype-associated modules and genes. (\u003cstrong\u003eA\u003c/strong\u003e) Forest maps visualized the univariate Cox analysis of six modules of LGGs. Correlation between the gene significance and module membership of (\u003cstrong\u003eB\u003c/strong\u003e) brown, (\u003cstrong\u003eC\u003c/strong\u003e) turquoise, and (\u003cstrong\u003eD\u003c/strong\u003e) yellow modules. Dot plots visualized the top 10 enriched KEGG items in the (\u003cstrong\u003eE\u003c/strong\u003e) brown, (\u003cstrong\u003eF\u003c/strong\u003e) turquoise, and (\u003cstrong\u003eG\u003c/strong\u003e) yellow modules. The dot color and size represent the enrichment degree and gene counts respectively. K-M survival analysis showed the significant survival differences between high and low mean in the (\u003cstrong\u003eH\u003c/strong\u003e) brown, (\u003cstrong\u003eI\u003c/strong\u003e) turquoise, and (\u003cstrong\u003eJ\u003c/strong\u003e) yellow modules.\u003c/p\u003e","description":"","filename":"Figure11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3505524/v1/b5557c20784edeef4c84c103.jpg"},{"id":50647700,"identity":"30662bb3-ebf0-4e09-aa0e-00e68dc1699e","added_by":"auto","created_at":"2024-02-05 08:09:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4281604,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3505524/v1/15e636b2-3470-485e-b736-0d49829f53fe.pdf"},{"id":45589010,"identity":"22619ec0-912e-42b5-b638-9820ad3ba269","added_by":"auto","created_at":"2023-10-31 20:39:31","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2409744,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1\u003c/strong\u003eSpearman correlation between the expression levels of identified seven antigens (CREB3L2, DDR2, IRF2, NCSTN, RECQL, REST, and TGFBR1) and those of APC markers in CGGA-LGG cohort. Spearman correlation between the expression levels of the seven tumor antigens and those of (\u003cstrong\u003eA\u003c/strong\u003e) CD40, (\u003cstrong\u003eB\u003c/strong\u003e) CD80, and (\u003cstrong\u003eC\u003c/strong\u003e) CD86.\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-3505524/v1/bffbd1fb3c4fb1f4b7264c66.tif"},{"id":45589011,"identity":"f83ea33f-2ceb-473e-8f2c-21504774289e","added_by":"auto","created_at":"2023-10-31 20:39:31","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1096536,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2\u003c/strong\u003eThe differential expression of seven tumor antigens between ICD subtypes in CGGA cohort including (\u003cstrong\u003eA\u003c/strong\u003e) CREB3L2, (\u003cstrong\u003eB\u003c/strong\u003e) DDR2, (\u003cstrong\u003eC\u003c/strong\u003e) IRF2, (\u003cstrong\u003eD\u003c/strong\u003e) NCSTN, (\u003cstrong\u003eE\u003c/strong\u003e) RECQL, (\u003cstrong\u003eF\u003c/strong\u003e) REST, and (\u003cstrong\u003eG\u003c/strong\u003e) TGFBR1.\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-3505524/v1/8f5d98170c84e1291982d2ed.tif"},{"id":45589002,"identity":"8b4513de-df83-4007-83da-d3108128789c","added_by":"auto","created_at":"2023-10-31 20:39:31","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1491952,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S3\u003c/strong\u003eFunctional enrichment analysis of seven tumor antigens in CGGA cohort. Gene ontology (GO) enrichment analysis for immune-related genes positively associated with (\u003cstrong\u003eA\u003c/strong\u003e) CREB3L2, (\u003cstrong\u003eB\u003c/strong\u003e) DDR2, (\u003cstrong\u003eC\u003c/strong\u003e) IRF2, (\u003cstrong\u003eD\u003c/strong\u003e) NCSTN, (\u003cstrong\u003eE\u003c/strong\u003e) RECQL, (\u003cstrong\u003eF\u003c/strong\u003e) REST, and (\u003cstrong\u003eG\u003c/strong\u003e) TGFBR1.\u003c/p\u003e","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-3505524/v1/582aa73253725b8894ed2c93.tif"},{"id":45589005,"identity":"d9c21d6e-c341-4ce1-9022-1ef7efe84e10","added_by":"auto","created_at":"2023-10-31 20:39:31","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1160768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S4 \u003c/strong\u003eIdentification of immune gene co-expression modules of LGGs. Scale-free fit index (\u003cstrong\u003eA\u003c/strong\u003e), and mean connectivity (\u003cstrong\u003eB\u003c/strong\u003e) for various soft thresholding powers. (\u003cstrong\u003eC\u003c/strong\u003e) Dendrogram of immune genes clustered on the basis of a dissimilarity measure. (\u003cstrong\u003eD\u003c/strong\u003e) Gene counts in each module. (\u003cstrong\u003eE\u003c/strong\u003e) The feature vectors distribution of each module between ICD subtypes in LGGs. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-3505524/v1/d9b48c2d4b589637c35359d2.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Tumor antigens and immunogenic cell death subtypes guided mRNA vaccine development for lower-grade gliomas","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDiffuse gliomas, a set of highly heterogeneous infiltrative brain malignancies, account for the majority of primary central nervous system (CNS) cancers[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Gliomas were classified into grades I\u0026ndash;IV based on the World Health Organization (WHO) classification[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Within them, only grade II and III gliomas were considered lower-grade gliomas (LGGs)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Unlike grade I glioma (considered benign lesions), LGGs are aggressive and cannot be completely removed by neurosurgery, usually evolving into recurrence or directly progressing to higher-grade neoplasms[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although there has been an explosive innovation in radiotherapy, chemotherapy, and targeted therapies for LGGs over the past decades, unfortunately, the responses to therapies vary widely and the prognosis differs from person to person[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, novel strategies, which are better at improving the treatment and making personalized clinical decisions, are an urgent research focus.\u003c/p\u003e \u003cp\u003eRecently, therapeutic tumor vaccines, as a hotspot in tumor immunotherapy, reported to be effective against several cancers and have attracted extensive concerns from oncologists and scientists[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These tumor vaccines specifically attack and further eliminate malignant cells expressing tumor-specific antigens or tumor-associated antigens, and, eventually achieve chronic therapeutic effects on the basis of immune memory[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Tumor vaccines are composed mainly of tumor antigens without or with adjuvants, and they offer the advantages of a broad treatment window, relative nontoxicity, minimal non-specific influence, and a durable immune memory, which could be possible to win the challenge of drug resistance, restricted therapeutic effects, increased costs and potential side effects related to standard immunotherapy and chemotherapy[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. According to the form of antigens, tumor vaccines could be classified into five categories: DNA, tumor cell, peptide, dendritic cell, and RNA[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, in clinical application, mRNA vaccines have several major advantages[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] compared to the other four types. Such as easy to design and produce, relatively low cost, stronger and persistent immune responses[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], a regulatable and short half-life, and safety with few potential gene insertional mutations or risk of infection. More importantly, mRNA vaccines have been demonstrated to be effective in fighting against multiple tumors, such as prostate cancer[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], melanoma[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and colorectal cancer[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, for patients with LGGs, the application of the mRNA vaccine remains largely uncharacterized and needs to be explored.\u003c/p\u003e \u003cp\u003eFurthermore, immunogenic cell death (ICD), a modality of regulated cell death (RCD), is enough to drive an adaptive immune response under specific circumstances followed by the development of long-term immunological memory[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Adjuvanticity and antigenicity are two characteristics necessary for ICD cells to be able to induce an effective immune response[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A key characteristic of triggering ICD is the exposure and release of damage-associated molecular patterns (DAMPs) of dying or dead cells to the microenvironment, which is potent endogenous adjuvants[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These DAMPs include but are not limited to calreticulin (CRT), high mobility group protein B1 (HMGB1) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], adenosine triphosphate (ATP), and heat shock proteins (HSPs) 70/90[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Antigenicity is a process that dendritic cells are attracted by the expression of DAMPs to engulf fragments of dying cells and bind antigenic peptides to the major histocompatibility complexes (MHCs)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Due to the immune surveillance evasion of tumor cells, ICD has developed into a promising strategy for tumor therapy[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] because of its precision and antigen-specific clearance. Previous studies have observed that some anti-tumor therapies such as chemotherapy[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], radiotherapy[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], photodynamic therapy[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], photothermal therapy[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and immunotherapy[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] could trigger ICD to enhance the antitumor immune response. Thus, a comprehensive understanding of the molecular features of ICD may be beneficial to elucidate the relationship between ICD and the microenvironment infiltration characteristics of LGGs and its impact on immunotherapy, thereby providing novel insights into the development of mRNA vaccines and identification of appropriate candidate recipients for vaccination. Unfortunately, to our knowledge, there is a lack of such studies integrating LGGs mRNA vaccine development and elucidation of the molecular features of ICDs.\u003c/p\u003e \u003cp\u003eIn the present study, by screening potential LGGs antigens, we identified seven over-expressed, mutated, and amplified cancer genes as the targets for mRNA vaccine development, which were closely associated with poor prognosis and infiltration of antigen-presenting cells (APCs). Meanwhile, considering the tumor heterogeneity and tumor immune microenvironment (TIME) complexity, we developed two immunogenic cell death subtypes to identify LGG populations more suitable for vaccination and more likely to benefit. Subsequently, we observed that the two ICD subtypes exhibited distinct prognosis, molecular, clinical, and immune microenvironment infiltrating characteristics. These findings were consistent in TCGA and CGGA repositories. In addition, we observed that the subtype Icds1 was highly associated with activated ICD, increased expression of seven tumor antigens and higher tumor mutation burden (TMB), which revealed that the subtype Icds1 were the candidates who were suitable for vaccination against LGGs. These findings provided a novel insight into development of mRNA vaccine and a powerful reference for selecting vaccine beneficiaries.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source and preprocessing\u003c/h2\u003e \u003cp\u003eOpen LGG RNA sequencing data with complete survival information were retrospectively enrolled in this study. Additionally, patients whose survival time was smaller than 30 days were excluded. Ultimately, a total of two datasets and matching clinical annotations were collected, including the log2(x\u0026thinsp;+\u0026thinsp;1) normalized TCGA-LGG dataset downloaded from the UCSC Xena website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xena.ucsc.edu/\u003c/span\u003e\u003cspan address=\"https://xena.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the CGGA-mRNAseq_693 obtained from the Chinese Glioma Genome Atlas (CGGA) official website (CGGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cgga.org.cn/index.jsp\u003c/span\u003e\u003cspan address=\"http://www.cgga.org.cn/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Moreover, the CGGA datasets were also normalized by log2(x\u0026thinsp;+\u0026thinsp;1) transformed.\u003c/p\u003e \u003cp\u003eIn addition, immune-related genes were downloaded and integrated from the InnateDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.innatedb.ca/\u003c/span\u003e\u003cspan address=\"https://www.innatedb.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and ImmPort (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.immport.org/shared/home\u003c/span\u003e\u003cspan address=\"https://www.immport.org/shared/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 GEPIA analysis\u003c/h2\u003e \u003cp\u003eWe used the Gene Expression Profiling Interactive Analysis[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] (GEPIA2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to identify the overexpressed genes in LGGs with the cutoff of |log2FC| values\u0026thinsp;\u0026gt;\u0026thinsp;1 and q\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Besides, the differential expression analysis method was set to LIMMA, and the gene expression profiles were from the TCGA and the Genotype-Tissue Expression (GTEx) database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 cBioPortal analysis\u003c/h2\u003e \u003cp\u003eBy applied the cBioPortal for Cancer Genomics[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] (cBioPortal, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbioportal.org\u003c/span\u003e\u003cspan address=\"http://www.cbioportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we extracted gene copy number variation (CNV) and gene mutation data of LGG patients in TCGA database to identify the amplified cancer genes, and visualized the genome alteration status and mutation frequency status. In addition, the disease-free survival (DFS) and disease-specifical survival (DSS) of the LGG patients from TCGA cohort were downloaded from this Portal.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 TIMER analysis\u003c/h2\u003e \u003cp\u003eThe Tumor Immune Estimation Resource (TIMER[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer/\u003c/span\u003e\u003cspan address=\"https://cistrome.shinyapps.io/timer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is an advanced tool for comprehensive analysis of the immune infiltration in multiple cancer types. To explore the relationship between tumor antigens and the infiltration of APCs, a TIMER analysis was conducted by using Spearman\u0026rsquo;s correlation with its statistical cutoff p value of 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Construction and validation of the immunogenic cell death subtypes\u003c/h2\u003e \u003cp\u003eA previous study summarized ICD genes by retrieving extensive literature[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and we extracted them from this study. Eventually, a total of 18 prognostic ICD genes via univariate Cox analysis were included in this study. TCGA-LGG dataset was used in this step, and patients were clustered into distinct molecular subtypes based on the expression of the 18 ICD genes by consensus clustering[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In order to ensure stable clustering results, \u0026ldquo;ConsensusClusterPlus\u0026rdquo; package was utilized to perform 1000 times repetitions. Subsequently, the T-distributed stochastic neighbor embedding (t-SNE)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] was applied to confirm the accuracy of the clustering assignments. To validate the clustered subtypes, consensus clustering and t-SNE were subsequently conducted by using the expression of those prognostic ICD genes that shared in CGGA database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Gene set variation analysis (GSVA)\u003c/h2\u003e \u003cp\u003eTo ascertain the difference in hallmark gene sets among ICD subtypes, GSVA was conducted by using \u0026ldquo;GSVA\u0026rdquo; R package[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The immune pathway-related gene sets were extracted from a published study[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], including cytotoxicity, immune suppression genes, inflammation, antigen presentation and processing, innate immunity, immune cells recruitment, and adaptive immunity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Single sample gene set enrichment analysis (ssGSEA)\u003c/h2\u003e \u003cp\u003eTo evaluate the tumor microenvironment (TME) cell infiltrations and immune functions, we used \"GSVA\" R package to perform ssGSEA algorithm and calculate the normalized enrichment score (NES) of 23 infiltrating cells and 13 immune functions, which represents the relative abundance of estimated terms. The marker genes of each infiltration cell were obtained from the study of Zhang et al[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and the gene set of immune functions were retrieved from another online paper[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Functional enrichment analysis\u003c/h2\u003e \u003cp\u003eTumor antigen-associated immune genes were defined as immune-related genes with a Pearson's correlation coefficient greater than 0.3 with the antigens. To understand the potential biological functions of those antigens, Gene Ontology (GO) annotation was performed by using the \u0026ldquo;clusterprofiler\u0026rdquo;[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] R package, and enrichment terms with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. In addition, we performed weighted gene co-expression network analysis[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] (WGCNA) to identify the key modules and genes closely associated with ICD subtypes. The potential mechanism of these module genes was annotated by using Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis in a interaction-friendly bioinformatic analysis tool: Sangerbox[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.9 Evaluation of tumor mutational burden (TMB), somatic mutation and immunomodulatory characteristics associated with immunogenic cell death subtypes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo determine the relationships between somatic mutation and immunogenic cell death subtypes, we used the \"maftools\"[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] R package to calculate TMB in Icds1 and Icds2 of the LGG samples in TCGA database and to visualize the somatic mutations landscape of the top 10 most frequently mutated genes. Moreover, immunomodulatory molecules such as MHCs, co-stimulators, and co-inhibitors were obtained from another published study[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and were compared between the two subtypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Immunohistochemistry (IHC)\u003c/h2\u003e \u003cp\u003e This research was approved by the Ethics Committee of Xiangya Hospital, Central South University. Eighty-two glioma tissue samples were donated by patients suffered from primary glioma at the department of neurosurgery, Xiangya Hospital. These glioma tissue samples were manufactured using a tissue microarray. The following antibodies were used for IHC: TGFBR1 (Proteintech, 30117-1-AP), CD40 (Proteintech, 28158-1-AP), and CD86 (Proteintech, 13395-1-AP). The grading rules as follows: The intensity was categorized as follows: 0 (negative), 1 (weak brown), 2 (moderate brown), and 3 (strong brown). The quantity of positive cells was defined as follows: 0 (\u0026le;\u0026thinsp;10%), 1 (11\u0026ndash;25%), 2 (26\u0026ndash;50%), 3 (51\u0026ndash;75%), and 4 (\u0026gt;\u0026thinsp;75%). The final IHC score was determined by multiplying the intensity scores with the quantity scores (values, 0\u0026ndash;12). Images of the microarray were scored by two independent pathologists blinded to the clinical information.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Statistical analysis\u003c/h2\u003e \u003cp\u003eData processing was performed with R software (version 4.1.1). Kaplan-Meier (K-M) survival curves was drawn with the \u0026ldquo;survival\u0026rdquo; R package and tested by log-rank tests. Pearson\u0026rsquo;s or Spearman\u0026rsquo;s analysis was applied to conduct correlation tests. The difference between two groups was compared by the Wilcoxon test. Statistical difference of gene mutation between immunogenic cell death subtypes was compared by Chi-square test.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Screening of potential tumor antigens of LGGs\u003c/h2\u003e \u003cp\u003eTo identify potential tumor antigens in LGGs for mRNA vaccine development, 3982 overexpressed genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) were first screened in LGGs samples through GEPIA2. Subsequently, the amplified and mutated cancer genes were filtered by assessing the genome fraction alteration and mutation frequency in individual samples, which showed that low incidence of genome fraction variation and mutation counts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, C) were involved in most LGGs patients. These results revealed a low immunogenicity of LGG patients. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, E showed the top 10 highest alteration frequency genes in genome fraction variation and mutation count groups, respectively. Notably, IDH1, TP53, ATRX were the highest alteration genes in both the groups. Eventually, the overexpressed, amplified, and mutated cancer genes were selected for further analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Identification of tumor antigens associated with LGG prognosis and antigen-presenting cells\u003c/h2\u003e \u003cp\u003eTo identify tumor antigens that play a key role in LGGs progression as the best candidates for mRNA vaccine development. We further performed univariate Cox regression analysis with the statistical p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 to screen genes closely associated with poorer overall survival (OS), DSS, and PFS of LGGs in TCGA dataset. A total of 42 target genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) were selected to further validate. Next, 14 overlapping genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) significantly related to LGGs prognosis were selected after their prognostic value was confirmed in TCGA dataset by K-M analysis and validated in CGGA dataset through both univariate Cox regression analysis and K-M analysis. As validated and visualized in CGGA dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-P), patients with high expression of CHEK1, CREB3L2, DDR2, EZH2, GTSE1, IRF2, MKI67, NCSTN, NUF2, RECQL, REST, SMO, SS18, and TGFBR1 were significantly associated with inferior prognosis of LGGs. Overall, these 14 candidate genes were considered to be critical for the development and progression of LGGs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven the important role of APCs in capturing, processing, and presenting allergens to cognate T cells in the protective immunity[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], we further identify the antigens closely related to APCs including B cell, macrophage, dendritic cell to determine the optimal targets to develop mRNA vaccine, which was performed by using TIMER comprehensive analysis with its cutoff Spearman correlation coefficient of 0.3. Finally, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-G, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003e genes expression including CREB3L2, DDR2, IRF2, NCSTN, RECQL, REST, and TGFBR1 were closely positively correlated with the infiltration of APCs, revealing their potential immune-activating effect and could be directly processed and presented by the APCs. In addition, considering the reinforcing effect of certain co-stimulatory factors on the immune response, we further analyzed the association between the expression of these 7 genes and CD40, CD80, and CD86[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The significantly positive Spearman correlations between these genes and co-stimulatory factors were observed in both TCGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C) and CGGA (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-C\u003c/b\u003e) cohorts. Taken together, the 7 identified tumor antigens are likely to trigger a stronger immune response and therefore serve as promising targets for developing mRNA vaccine against LGGs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, we selected one well-known gene (TGFBR1) for further experimental validation. Considering the expression of CD80 was relatively lower than CD 40 and CD86 in glioma sample, we only analyzed the correlation between the expression of TGFBR1 and the expression of CD40 or CD86. IHC results of the microarrays displayed that expression of TGFBR1 was positively correlated with the expression of CD40 and CD86 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). We found that TGFBR1 was the highly expressed in glioma tissues than the peritumoral brain tissues \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-D\u003cb\u003e)\u003c/b\u003e. Moreover, the expression of TGFBR1 was also closely correlated the age, IDH status, and 1p/19q status of glioma patients \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-F\u003cb\u003e)\u003c/b\u003e. The statistical analysis of the correlation between the expression of TGFBR1 and the expression of CD40 or CD86 were showed in \u003cb\u003eFigure H-I\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Identification of ICD subtypes of LGGs with distinct expression of ICD modulators and prognosis\u003c/h2\u003e \u003cp\u003eTo fully understand the immunogenic cell death subtypes of LGGs, 481 patients from TCGA cohort were involved in this analytical process. 18 ICD genes with prognostic values were identified by univariate Cox regression, and a network map depicted their Pearson correlation and impact on the prognosis of LGG patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Next, according to the expression profile of the above 18 ICD genes, consensus clustering analysis was performed to classify patients into two subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), designated Icds1, and Icds2. The same results were clustered in the CGGA database (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Subsequently, the result of t-SNE in both TCGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eD) and CGGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eE) sets confirmed that patients in different subtypes can be completely distinguished. Furthermore, we compared the differences in ICD transcriptional profiles between ICD subtypes and found their obvious higher expression in Icds1. This result kept consistent in TCGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eF) and CGGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eG), indicating that ICD subtypes can distinguish the ICD modulators expressional status. Besides, we explored the relationship between ICD subtypes and the six pan-cancer immune subtypes reported in a previous study[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eH, LGGs was mostly classified into C4 (Lymphocyte Depleted) and C5 (Immunologically Quiet), and Icds1 mainly clustered into C4, while Icds2 mainly involved in C5, illustrating that LGGs differ from other tumors. More importantly, K-M curves revealed that the immunogenic cell death subtypes were prognostically relevant and patients with subtype Icds1 had a significant shortened survival. These results were consistent in both TCGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eI) and CGGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ) cohorts, indicating that the reproducibility and stability of immunogenic cell death subtypes. Therefore, immunogenic cell death typing can be a promising prognostic biomarker and may provide valuable guideline for LGG immunotherapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Cellular and molecular characteristics of the ICD subtypes\u003c/h2\u003e \u003cp\u003eTo better understand the immune status changes of tumor microenvironment underlying distinct ICD subtypes, ssGSEA was first performed to score 23 immune cells and 13 immune functions. We found that immune cell components and immune functions were obviously distinct between ICD subtypes. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, subtype Icds1 had much higher scores in both immune-activated cells (such as activated B cell, activated CD8\u003csup\u003e+\u003c/sup\u003e T cell, natural killer cell) and immunosuppressive cells (such as myeloid-derived suppressor cells: MDSC, plasmacytoid dendritic cell, regulatory T cell). Similar trends were observed in immune function (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003eB) including immune promoting and immune inhibiting effects. Moreover, these findings were further confirmed by GSVA scores of immune-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). More importantly, matching significant results were observed in the CGGA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-F). Furthermore, consistent with the antigen processing and presentation pathway, MHC molecules are observed highly expressed in the Icds1 subtype in both TCGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eA) and CGGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eB) cohorts. Besides, we further compared the expression levels of immunomodulatory molecules (co-stimulators and co-inhibitors) between the ICD subtypes. Corresponding to immune function, the Icds1 subtype showed a higher abundance of these immunomodulators (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-D), indicating immune activation accompanied by immunosuppression. Therefore, the Icds1 is a phenotype characterized by immunological \u0026ldquo;hot\u0026rdquo; and immunosuppression while the Icds2 is a phenotype characterized by immunological \u0026ldquo;cold\u0026rdquo;. These results keep consistent with the pan-cancer immune subtyping that Icds1 were mainly enriched in C4 (Lymphocyte Depleted) and Icds2 were mainly enriched in C5 (Immunologically Quiet), illustrating that our ICD typing is reliability. Taken together, our ICD subtyping could reflect the immune status of LGG patients and select suitable candidates for immunotherapy. Icds2 patients, with an immunologically cold phenotype, might be potential candidates for mRNA vaccination, while Icds1, with an immunologically hot and immunosuppressive phenotype, might be suitable for immune checkpoint inhibitors (ICIs) therapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 The association between tumor mutational burden (TMB) and ICD Subtypes\u003c/h2\u003e \u003cp\u003eSince TMB is associated with immune status[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], it may affect the immunogenicity[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] of the tumor and the response to immunotherapy[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Hence, in TCGA dataset, we compared the distribution of the ten most frequently mutated genes in the two subtypes. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, there were five genes (IDH1, ATRX, CIC, TTN, and EGFR) mutated significantly differently between subtypes. Next, we assessed the TMB and mutation counts between Icds1 and Icds2. Our results showed that TMB (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003eB) and mutation counts (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003eC) were both significantly higher in Icds1 than that in Icds2. In addition, we found that the 7 identified tumor antigens including CREB3L2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003eD), DDR2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003eE), IRF2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003eF), NCSTN (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003eG), RECQL (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003eH), REST (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003eI), and TGFBR1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003eJ) expressed significantly higher in Icds1 than in Icds2. The same expression trends of those antigens (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA-G\u003c/b\u003e) were observed between ICD subtypes in the CGGA dataset. These findings indicated that the Icds2 had lower immunogenicity and the patients in Icds2 may be more suitable for mRNA vaccination to reinforce the immune response.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 The potential biological mechanisms of the identified tumor antigens in LGGs\u003c/h2\u003e \u003cp\u003eTo explore the potential biological mechanisms and identify the underlying immune-associated pathways of the tumor antigens, we performed Pearson correlation analysis to screen for genes strongly positively associated with identified tumor antigens from 2909 immune-related genes. The cut-off correlation coefficient r is set to be greater than 0.3. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-G and \u003cb\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA-G\u003c/b\u003e, the immune-related biological process (BP) enrichment items of the 7 identified tumor antigens were all involved in immune response-regulation signaling pathway and/or T cell activation. In addition, cytokines act as key messengers for immune cell communication and they can drive many biological processes including inflammatory and immune responses[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. And the enrichment items of the 7 identified tumor antigens were all involved in cytokine production and cytokine-mediated signaling pathway in TCGA and CGGA cohorts. Moreover, chemotactic cytokines, signaling molecules, etc. act as critical mediators of inflammation, participate in intercellular communication, condition the immune system's proper response and maintain the normal functioning of the immune system[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. According to the GO enrichment analysis, the most enriched molecular function (MF) of CREB3L2 and DDR2 both included cytokine binding, cytokine receptor binding and cytokine receptor activity in the TCGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eA, B) and CGGA (\u003cb\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA, B\u003c/b\u003e) cohorts. And the enrichment results of IRF2 were both involved in cytokine binding and cytokine receptor binding in those two cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eC, \u003cb\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eC\u003c/b\u003e). Besides, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eD-G and \u003cb\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eD-G\u003c/b\u003e, the pathway enriched for NCSTN, RECQL, REST and TGFBR1 involved immune receptor activity, cytokine binding, cytokine activity, cytokine receptor binding, cytokine receptor activity or growth factor receptor binding. These results revealed that the 7 identified tumor antigens in LGGs were closely related to immunologic biological processes, which provided a theoretical basis for the development of mRNA vaccines.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Identification of immune- and ICD subtype-associated hub modules of LGG\u003c/h2\u003e \u003cp\u003eWe performed WGCNA to construct an immune gene co-expression network with the optimal soft threshold of 3 (\u003cb\u003eFigure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA, B\u003c/b\u003e) in the scale-free network. We used dynamic shear tree to cluster and identify gene co-expression modules with the minimum module size of 30 and the height of 0.25 (\u003cb\u003eFigure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eC\u003c/b\u003e). As shown in \u003cb\u003eFigure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eD\u003c/b\u003e, 6 gene modules including blue, brown, green, red, turquoise, and yellow were clustered except for the genes in the grey module. Next, we analyzed the module eigengenes in the two ICD subtypes and found that Icds1 showed significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) higher eigengenes in the yellow, brown, and turquoise modules (\u003cb\u003eFigure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eE\u003c/b\u003e), which were consistent with the findings that Icds1 corresponded to immunologically hot and Icds2 to immunologically cold. Meanwhile, we also observed that the yellow, brown, and turquoise modules were closely associated with overall survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e11\u003c/span\u003eA) and ICD subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e11\u003c/span\u003eB-D). Further biological functions analysis of these module genes showed that the brown module genes were mainly involved in cytokine-cytokine receptor interaction, T cell receptor signaling pathway, natural killer cell mediated cytotoxicity, Th17 cell differentiation, and etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e11\u003c/span\u003eE), while turquoise module genes were enriched in cytokine-cytokine receptor interaction, hematopoietic cell lineage, NF-kappa B signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e11\u003c/span\u003eF). And the yellow module was associated with cytokine-cytokine receptor interaction, natural killer cell mediated cytotoxicity, and several cancer-related pathways such as Jak-STAT signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e11\u003c/span\u003eG). Subsequently, in the prognostic analysis of the modules, we founded that the module eigengenes of the yellow, brown, and turquoise modules were all significantly related to the prognosis of LGGs and higher expression those module genes correlated with shortened survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e11\u003c/span\u003eH-J), which is consistent with above findings. Therefore, patients with higher expression of yellow, brown, and turquoise module genes may not be suitable for mRNA vaccines. Finally, we selected 5 hub genes including HLA-DMB, SPI1, CASP1, ITGB2, and LAIR1 from turquoise module with their module membership (MM)\u0026thinsp;\u0026gt;\u0026thinsp;0.93, which might be potential biomarkers for selecting optimal mRNA vaccines recipients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLGG, as common primary intracranial tumor, is one of the most aggressive malignant tumors. Due to its biological heterogeneity and immunosuppressive properties, conventional treatments such as surgical resection, radiotherapy, and chemotherapy are not sufficient to combat their progression[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Immunotherapy is a novel and explosively growing field and the mRNA vaccines are a promising immunotherapy against tumors[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Tumor eradication through vaccine-induced active immunity is the ultimate goal of tumor immunotherapy[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The initial success of a peptide vaccine targeting IDH1 mutants in fighting IDH1 mutant gliomas[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], has greatly encouraged the development of mRNA vaccines. However, the clinical application of the mRNA vaccine in LGGs is still needed to be explored, thus, we hope our research could provide a valuable reference for the development and application of mRNA vaccine.\u003c/p\u003e \u003cp\u003eIn the current study, we identified seven potential tumor antigens for developing mRNA vaccine by using the aberrantly expressed profile and exploring the mutational landscape of LGGs. These tumor antigens, including CREB3L2, DDR2, IRF2, NCSTN, RECQL, REST, and TGFBR1, are overexpressed, mutated, and amplified in LGGs and are promising targets for developing mRNA vaccine. Their high expression was not only correlated with shortened survival but also significantly correlated with the high infiltration of APCs, demonstrating their key role in the invasion and progression of LGGs, and the useful value as vaccine candidates. Although further experimental verification and clinical evaluation are still urgently needed, their potential value as mRNA vaccine candidates were supported in available studies. CREB3L2, a member of the CREB3 transcription factor family, is characterized in cancer by the formation of the chimeric gene FUS-CREB3L2, which is responsible for low-grade fibromyxoid sarcoma [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. While, in malignant glioma, CREB3L2 binds directly to the ATF5 promoter, leading to ATF5 transcription and thus exerting an anti-apoptotic effect[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. DDR2, a member of tyrosine kinase receptors (TKRs) families, is probably a potential molecular biomarker for multiple types of cancer[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. It has been demonstrated to promote a variety of tumor cells proliferation, such as lung cancer[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], lymphoma[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], and oral squamous cell carcinoma[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] among others. In addition, its overexpression was positively correlated with poor prognosis of patients[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Besides, the mutation of DDR2 was also closely related to the formation in some tumors[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. It has been shown that there is a significant positive correlation between the expression level of IRF2 and the grade of glioma, and that its overexpression enhances the invasion and migration of glioma cells, and vice versa[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Nicastrin (NCSTN) is one of a core subunit of γ-Secretase, which was found to be closely associated with the tumor progression of a variety of tumors[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. RECQL, namely RECQ1 or RECQL1, played critical role in ensuring chromosomal stability[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. However, it was high expressed in transformed or a variety of cancer cells, for instance, multiple myeloma[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], hematological cancers[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], glioblastoma[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], and so on, showing that RECQL may be involved in tumorigenesis. Repressor element silencing transcription factor (REST), a well-known transcription repressor, has been reported to be an oncogene and be related to poor prognosis in glioma[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. In addition, its upregulation was associated with larger tumor size, higher grade and worse therapeutic efficacy in glioma[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. TGF-β receptor 1 (TGFBR1), a member of the TGF-β signaling pathway, has been increasingly shown to promote epithelial-mesenchymal transition (EMT) and the proliferation and migration of tumor cells[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. And it was also found to be high expressed in a variety of tumors and involved in the progression and metastasis of tumor[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. More importantly, IHC staining showed that TGFBR1 is higher expressed in glioma samples and positively correlated with the co-stimulatory factors, which indicated that TGFBR1 may serve as an important tumor antigen for vaccine development.\u003c/p\u003e \u003cp\u003eGiven the critical role of ICDs in antitumor immunotherapy[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e] and the fact that mRNA vaccines are beneficial for only a small subset of cancer patients. Thus, we classified LGG patients into two ICD subtypes based on the ICD gene profile to select optimal populations for mRNA vaccine. To our knowledge, this is the first article on ICD-based typing to screen for optimal vaccine recipients with LGGs. The two ICD subtypes characterized by a distinct ICD transcription profiles expression, clinical prognosis, molecular and cellular features. Compared to Icds1, Icds2 had a prolonged survival, indicating that ICD typing could be a potential prognostic biomarker for LGGs. More importantly, different molecular, and cellular features were observed between ICD subtypes, indicating that distinct mechanisms regulated the tumor immune environment between the subtypes and distinct therapeutic strategies are required for different ICD subtype patients. We analyzed the TMB and gene mutation count differences between ICD subtypes and found a significantly higher TMB, gene mutated counts, TP53 mutation rate and a lower IDH mutation rate were shown in the Icds1 subtype. As shown in the publications[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e], LGGs with wild-type IDH and TP53 mutation has a poor prognosis, which were consistent with our findings. In addition, high TMB has the potential to generate immunogenic neoantigens[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e] and further drive APCs and immune effector cells infiltration. However, Icds1 exhibited significantly higher ICD modulators, infiltration of APCs, and immune effector cells such as natural killer cell, activated B cell, and activated CD8\u0026thinsp;+\u0026thinsp;T cell, it had obviously poorer prognosis. These results indicated that immunosuppression may play a dominant role in the regulation of the tumor microenvironment in this subtype. Consistent with this hypothesis, immunosuppressive cells (for instance MDSC, and regulatory T cell), immune inhibiting functions, and immunosuppression signaling pathways were all enriched in Icds1 subtype. The immunosuppressive tumor microenvironment of Icds1 may inhibit the effective immune response elicited by mRNA vaccine. Thus, the immunologically \"hot\" and immunosuppressive phenotype of Icds1, which is not suitable for mRNA vaccine alone but more suitable for immune checkpoint blockade (ICB), or Icds1 were likely to benefit from the strategy with mRNA vaccine combination with ICB. Indeed, TMB is becoming a potential biomarker for predicting response to ICB[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. In contrast, Icds2 was immunologically \"cold\" phenotype with lower level of immune infiltration cells, immune function, and immune-related signaling pathway. Since mRNA vaccine could elicit an effective immune response, it might be more suitable for patients with low infiltrated immune cells. Hence, Icds2 but not Icds1 were the optimal candidates for mRNA vaccine in LGGs. To validate the reliability of our ICD subtypes, we further explored the association between ICD subtypes and six reported pan-cancer immune subtypes and found consistent results that C4 (lymphocyte depleted) was mainly enriched in Icds1, while C5 (immunologically quiet) was mainly clustered in Icds2. Taken together, our ICD subtypes could not only predict survival of LGG patients, but also select optimal candidates for mRNA vaccine. Specifically, Icds2 is an immunological \u0026ldquo;cold\u0026rdquo; phenotype and is suitable for mRNA vaccine.\u003c/p\u003e \u003cp\u003eConsidering that ICD subtype may be not stable among different patient populations and the biomarkers of ICD subtypes are the key of linkage candidate population screening, typing specificity, and mechanism research[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Hence, we constructed immune gene co-expression network by using WGCNA and identified six ICD subtype-associated gene modules. Among them, the yellow, brown, and turquoise modules were significantly related to overall survival. Finally, based on the threshold MM value of larger than 0.93, we identified 5 hub genes including HLA-DMB, SPI1, CASP1, ITGB2, and LAIR1 from turquoise module, which might be potential biomarkers for predicting response to mRNA vaccine.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eTaken together, CREB3L2, DDR2, IRF2, NCSTN, RECQL, REST, and TGFBR1 are candidate tumor antigens for mRNA vaccine development in LGGs, and patients with Icds2 subtype of LGGs are the population more likely to benefit from mRNA vaccination. These finding in our study provide a theoretical framework for future developing mRNA vaccine against LGGs, predicting prognosis and responses to mRNA vaccination, selecting suitable recipients and designing better individualized immunotherapeutic strategy.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of interest statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest in this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data source of the manuscript is from the public database. The details were displayed in the materials and methods section. Other data could contact with the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (81472355), and Provincial Natural Science Foundation of Hunan (2022JJ30931). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGT, CR and XJ conceived the study. WY collected and analyzed data. DX visualized figures. GT and WYwrote the manuscript. All authors reviewed and approved the submitted manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the CGGA and TCGA databases for freely providing the transcriptomic information of LGG samples.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBao Z, Wang Y, Wang Q, Fang S, Shan X, Wang J, Jiang T: \u003cstrong\u003eIntratumor heterogeneity, microenvironment, and mechanisms of drug resistance in glioma recurrence and evolution\u003c/strong\u003e. \u003cem\u003eFront Med \u003c/em\u003e2021, \u003cstrong\u003e15\u003c/strong\u003e(4):551-561.doi.org/10.1007/s11684-020-0760-2.\u003c/li\u003e\n\u003cli\u003eHorbinski C, Berger T, Packer RJ, Wen PY: \u003cstrong\u003eClinical implications of the 2021 edition of the WHO classification of central nervous system tumours\u003c/strong\u003e. \u003cem\u003eNat Rev Neurol \u003c/em\u003e2022, \u003cstrong\u003e18\u003c/strong\u003e(9):515-529.doi.org/10.1038/s41582-022-00679-w.\u003c/li\u003e\n\u003cli\u003eJohnson DR, Giannini C, Vaubel RA, Morris JM, Eckel LJ, Kaufmann TJ, Guerin JB: \u003cstrong\u003eA Radiologist\u0026apos;s Guide to the 2021 WHO Central Nervous System Tumor Classification: Part I-Key Concepts and the Spectrum of Diffuse Gliomas\u003c/strong\u003e. \u003cem\u003eRadiology \u003c/em\u003e2022, \u003cstrong\u003e304\u003c/strong\u003e(3):494-508.doi.org/10.1148/radiol.213063.\u003c/li\u003e\n\u003cli\u003eYoussef G, Miller JJ: \u003cstrong\u003eLower Grade Gliomas\u003c/strong\u003e. \u003cem\u003eCurr Neurol Neurosci Rep \u003c/em\u003e2020, \u003cstrong\u003e20\u003c/strong\u003e(7):21.doi.org/10.1007/s11910-020-01040-8.\u003c/li\u003e\n\u003cli\u003eAoki K, Nakamura H, Suzuki H, Matsuo K, Kataoka K, Shimamura T, Motomura K, Ohka F, Shiina S, Yamamoto T\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePrognostic relevance of genetic alterations in diffuse lower-grade gliomas\u003c/strong\u003e. \u003cem\u003eNeuro Oncol \u003c/em\u003e2018, \u003cstrong\u003e20\u003c/strong\u003e(1):66-77.doi.org/10.1093/neuonc/nox132.\u003c/li\u003e\n\u003cli\u003eBerzero G, Di Stefano AL, Ronchi S, Bielle F, Villa C, Guillerm E, Capelle L, Mathon B, Laurenge A, Giry M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eIDH-wildtype lower-grade diffuse gliomas: the importance of histological grade and molecular assessment for prognostic stratification\u003c/strong\u003e. \u003cem\u003eNeuro-oncology \u003c/em\u003e2021, \u003cstrong\u003e23\u003c/strong\u003e(6):955-966.doi.org/10.1093/neuonc/noaa258.\u003c/li\u003e\n\u003cli\u003ePardi N, Hogan MJ, Porter FW, Weissman D: \u003cstrong\u003emRNA vaccines - a new era in vaccinology\u003c/strong\u003e. \u003cem\u003eNat Rev Drug Discov \u003c/em\u003e2018, \u003cstrong\u003e17\u003c/strong\u003e(4):261-279.doi.org/10.1038/nrd.2017.243.\u003c/li\u003e\n\u003cli\u003eOlin MR, Ampudia-Mesias E, Pennell CA, Sarver A, Chen CC, Moertel CL, Hunt MA, Pluhar GE: \u003cstrong\u003eTreatment Combining CD200 Immune Checkpoint Inhibitor and Tumor-Lysate Vaccination after Surgery for Pet Dogs with High-Grade Glioma\u003c/strong\u003e. \u003cem\u003eCancers (Basel) \u003c/em\u003e2019, \u003cstrong\u003e11\u003c/strong\u003e(2).doi.org/10.3390/cancers11020137.\u003c/li\u003e\n\u003cli\u003eDeMaria PJ, Bilusic M: \u003cstrong\u003eCancer Vaccines\u003c/strong\u003e. \u003cem\u003eHematol Oncol Clin North Am \u003c/em\u003e2019, \u003cstrong\u003e33\u003c/strong\u003e(2):199-214.doi.org/10.1016/j.hoc.2018.12.001.\u003c/li\u003e\n\u003cli\u003eZirlik KM, Zahrieh D, Neuberg D, Gribben JG: \u003cstrong\u003eCytotoxic T cells generated against heteroclitic peptides kill primary tumor cells independent of the binding affinity of the native tumor antigen peptide\u003c/strong\u003e. \u003cem\u003eBlood \u003c/em\u003e2006, \u003cstrong\u003e108\u003c/strong\u003e(12):3865-3870.doi.org/10.1182/blood-2006-04-014415.\u003c/li\u003e\n\u003cli\u003eBouzid R, Peppelenbosch M, Buschow SI: \u003cstrong\u003eOpportunities for Conventional and in Situ Cancer Vaccine Strategies and Combination with Immunotherapy for Gastrointestinal Cancers, A Review\u003c/strong\u003e. \u003cem\u003eCancers (Basel) \u003c/em\u003e2020, \u003cstrong\u003e12\u003c/strong\u003e(5).doi.org/10.3390/cancers12051121.\u003c/li\u003e\n\u003cli\u003eFan Q, Ma Q, Bai J, Xu J, Fei Z, Dong Z, Maruyama A, Leong KW, Liu Z, Wang C: \u003cstrong\u003eAn implantable blood clot-based immune niche for enhanced cancer vaccination\u003c/strong\u003e. \u003cem\u003eSci Adv \u003c/em\u003e2020, \u003cstrong\u003e6\u003c/strong\u003e(39).doi.org/10.1126/sciadv.abb4639.\u003c/li\u003e\n\u003cli\u003eNguyen QT, Kwak C, Lee WS, Kim J, Jeong J, Sung MH, Yang J, Poo H: \u003cstrong\u003ePoly-\u0026gamma;-Glutamic Acid Complexed With Alum Induces Cross-Protective Immunity of Pandemic H1N1 Vaccine\u003c/strong\u003e. \u003cem\u003eFront Immunol \u003c/em\u003e2019, \u003cstrong\u003e10\u003c/strong\u003e:1604.doi.org/10.3389/fimmu.2019.01604.\u003c/li\u003e\n\u003cli\u003eRausch S, Schwentner C, Stenzl A, Bedke J: \u003cstrong\u003emRNA vaccine CV9103 and CV9104 for the treatment of prostate cancer\u003c/strong\u003e. \u003cem\u003eHum Vaccin Immunother \u003c/em\u003e2014, \u003cstrong\u003e10\u003c/strong\u003e(11):3146-3152.doi.org/10.4161/hv.29553.\u003c/li\u003e\n\u003cli\u003eWang Y, Zhang L, Xu Z, Miao L, Huang L: \u003cstrong\u003emRNA Vaccine with Antigen-Specific Checkpoint Blockade Induces an Enhanced Immune Response against Established Melanoma\u003c/strong\u003e. \u003cem\u003eMol Ther \u003c/em\u003e2018, \u003cstrong\u003e26\u003c/strong\u003e(2):420-434.doi.org/10.1016/j.ymthe.2017.11.009.\u003c/li\u003e\n\u003cli\u003eShahnazari M, Samadi P, Pourjafar M, Jalali A: \u003cstrong\u003eTherapeutic vaccines for colorectal cancer: The progress and future prospect\u003c/strong\u003e. \u003cem\u003eInt Immunopharmacol \u003c/em\u003e2020, \u003cstrong\u003e88\u003c/strong\u003e:106944.doi.org/10.1016/j.intimp.2020.106944.\u003c/li\u003e\n\u003cli\u003eGalluzzi L, Vitale I, Warren S, Adjemian S, Agostinis P, Martinez AB, Chan TA, Coukos G, Demaria S, Deutsch E\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eConsensus guidelines for the definition, detection and interpretation of immunogenic cell death\u003c/strong\u003e. \u003cem\u003eJ Immunother Cancer \u003c/em\u003e2020, \u003cstrong\u003e8\u003c/strong\u003e(1).doi.org/10.1136/jitc-2019-000337.\u003c/li\u003e\n\u003cli\u003eRodrigues MC, Morais JAV, Ganassin R, Oliveira GRT, Costa FC, Morais AAC, Silveira AP, Silva VCM, Longo JPF, Muehlmann LA: \u003cstrong\u003eAn Overview on Immunogenic Cell Death in Cancer Biology and Therapy\u003c/strong\u003e. \u003cem\u003ePharmaceutics \u003c/em\u003e2022, \u003cstrong\u003e14\u003c/strong\u003e(8).doi.org/10.3390/pharmaceutics14081564.\u003c/li\u003e\n\u003cli\u003eLi Q, Chen C, Kong J, Li L, Li J, Huang Y: \u003cstrong\u003eStimuli-responsive nano vehicle enhances cancer immunotherapy by coordinating mitochondria-targeted immunogenic cell death and PD-L1 blockade\u003c/strong\u003e. \u003cem\u003eActa Pharm Sin B \u003c/em\u003e2022, \u003cstrong\u003e12\u003c/strong\u003e(5):2533-2549.doi.org/10.1016/j.apsb.2021.11.005.\u003c/li\u003e\n\u003cli\u003eKim R, Kin T: \u003cstrong\u003eCurrent and Future Therapies for Immunogenic Cell Death and Related Molecules to Potentially Cure Primary Breast Cancer\u003c/strong\u003e. \u003cem\u003eCancers (Basel) \u003c/em\u003e2021, \u003cstrong\u003e13\u003c/strong\u003e(19).doi.org/10.3390/cancers13194756.\u003c/li\u003e\n\u003cli\u003eBirmpilis AI, Paschalis A, Mourkakis A, Christodoulou P, Kostopoulos IV, Antimissari E, Terzoudi G, Georgakilas AG, Armpilia C, Papageorgis P\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eImmunogenic Cell Death, DAMPs and Prothymosin \u0026alpha; as a Putative Anticancer Immune Response Biomarker\u003c/strong\u003e. \u003cem\u003eCells \u003c/em\u003e2022, \u003cstrong\u003e11\u003c/strong\u003e(9).doi.org/10.3390/cells11091415.\u003c/li\u003e\n\u003cli\u003eIulianna T, Kuldeep N, Eric F: \u003cstrong\u003eThe Achilles\u0026apos; heel of cancer: targeting tumors via lysosome-induced immunogenic cell death\u003c/strong\u003e. \u003cem\u003eCell Death Dis \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e(5):509.doi.org/10.1038/s41419-022-04912-8.\u003c/li\u003e\n\u003cli\u003eDu B, Waxman DJ: \u003cstrong\u003eMedium dose intermittent cyclophosphamide induces immunogenic cell death and cancer cell autonomous type I interferon production in glioma models\u003c/strong\u003e. \u003cem\u003eCancer Lett \u003c/em\u003e2020, \u003cstrong\u003e470\u003c/strong\u003e:170-180.doi.org/10.1016/j.canlet.2019.11.025.\u003c/li\u003e\n\u003cli\u003eZhou H, Tu C, Yang P, Li J, Kepp O, Li H, Zhang L, Zhang L, Zhao Y, Zhang T\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eCarbon ion radiotherapy triggers immunogenic cell death and sensitizes melanoma to anti-PD-1 therapy in mice\u003c/strong\u003e. \u003cem\u003eOncoimmunology \u003c/em\u003e2022, \u003cstrong\u003e11\u003c/strong\u003e(1):2057892.doi.org/10.1080/2162402x.2022.2057892.\u003c/li\u003e\n\u003cli\u003eSong H, Cai Z, Li J, Xiao H, Qi R, Zheng M: \u003cstrong\u003eLight triggered release of a triple action porphyrin-cisplatin conjugate evokes stronger immunogenic cell death for chemotherapy, photodynamic therapy and cancer immunotherapy\u003c/strong\u003e. \u003cem\u003eJ Nanobiotechnology \u003c/em\u003e2022, \u003cstrong\u003e20\u003c/strong\u003e(1):329.doi.org/10.1186/s12951-022-01531-5.\u003c/li\u003e\n\u003cli\u003eXiang Q, Yang C, Luo Y, Liu F, Zheng J, Liu W, Ran H, Sun Y, Ren J, Wang Z: \u003cstrong\u003eNear-Infrared II Nanoadjuvant-Mediated Chemodynamic, Photodynamic, and Photothermal Therapy Combines Immunogenic Cell Death with PD-L1 Blockade to Enhance Antitumor Immunity\u003c/strong\u003e. \u003cem\u003eSmall \u003c/em\u003e2022, \u003cstrong\u003e18\u003c/strong\u003e(13):e2107809.doi.org/10.1002/smll.202107809.\u003c/li\u003e\n\u003cli\u003eXie L, Ding Y, Zhang X, Zhang Z, Zeng S, Wang L, Yang Z, Liu Q, Hu ZW: \u003cstrong\u003eA Cascade-Targeted Enzyme-Instructed Peptide Self-Assembly Strategy for Cancer Immunotherapy through Boosting Immunogenic Cell Death\u003c/strong\u003e. \u003cem\u003eSmall Methods \u003c/em\u003e2023:e2201416.doi.org/10.1002/smtd.202201416.\u003c/li\u003e\n\u003cli\u003eTang Z, Kang B, Li C, Chen T, Zhang Z: \u003cstrong\u003eGEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis\u003c/strong\u003e. \u003cem\u003eNucleic Acids Res \u003c/em\u003e2019, \u003cstrong\u003e47\u003c/strong\u003e(W1):W556-w560.doi.org/10.1093/nar/gkz430.\u003c/li\u003e\n\u003cli\u003eGao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eIntegrative analysis of complex cancer genomics and clinical profiles using the cBioPortal\u003c/strong\u003e. \u003cem\u003eSci Signal \u003c/em\u003e2013, \u003cstrong\u003e6\u003c/strong\u003e(269):pl1.doi.org/10.1126/scisignal.2004088.\u003c/li\u003e\n\u003cli\u003eLi T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS: \u003cstrong\u003eTIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells\u003c/strong\u003e. \u003cem\u003eCancer Res \u003c/em\u003e2017, \u003cstrong\u003e77\u003c/strong\u003e(21):e108-e110.doi.org/10.1158/0008-5472.Can-17-0307.\u003c/li\u003e\n\u003cli\u003eGarg AD, De Ruysscher D, Agostinis P: \u003cstrong\u003eImmunological metagene signatures derived from immunogenic cancer cell death associate with improved survival of patients with lung, breast or ovarian malignancies: A large-scale meta-analysis\u003c/strong\u003e. \u003cem\u003eOncoimmunology \u003c/em\u003e2016, \u003cstrong\u003e5\u003c/strong\u003e(2):e1069938.doi.org/10.1080/2162402x.2015.1069938.\u003c/li\u003e\n\u003cli\u003eZhang B, Wu Q, Li B, Wang D, Wang L, Zhou YL: \u003cstrong\u003em(6)A regulator-mediated methylation modification patterns and tumor microenvironment infiltration characterization in gastric cancer\u003c/strong\u003e. \u003cem\u003eMol Cancer \u003c/em\u003e2020, \u003cstrong\u003e19\u003c/strong\u003e(1):53.doi.org/10.1186/s12943-020-01170-0.\u003c/li\u003e\n\u003cli\u003eTang G, Tan L, Yuan H, Yin W: \u003cstrong\u003eGlycosylation modification patterns reveal distinct tumor metabolism and immune microenvironment landscape in lower-grade gliomas\u003c/strong\u003e. \u003cem\u003eFront Cell Dev Biol \u003c/em\u003e2022, \u003cstrong\u003e10\u003c/strong\u003e:886989.doi.org/10.3389/fcell.2022.886989.\u003c/li\u003e\n\u003cli\u003eZhou H, Wang F, Tao P: \u003cstrong\u003et-Distributed Stochastic Neighbor Embedding Method with the Least Information Loss for Macromolecular Simulations\u003c/strong\u003e. \u003cem\u003eJ Chem Theory Comput \u003c/em\u003e2018, \u003cstrong\u003e14\u003c/strong\u003e(11):5499-5510.doi.org/10.1021/acs.jctc.8b00652.\u003c/li\u003e\n\u003cli\u003eMariet C, Castel D, Grill J, Saffroy R, Dangouloff-Ros V, Boddaert N, Llamas-Guttierrez F, Chapp\u0026eacute; C, Puget S, Hasty L\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePosterior fossa ependymoma H3 K27-mutant: an integrated radiological and histomolecular tumor analysis\u003c/strong\u003e. \u003cem\u003eActa Neuropathol Commun \u003c/em\u003e2022, \u003cstrong\u003e10\u003c/strong\u003e(1):137.doi.org/10.1186/s40478-022-01442-4.\u003c/li\u003e\n\u003cli\u003eH\u0026auml;nzelmann S, Castelo R, Guinney J: \u003cstrong\u003eGSVA: gene set variation analysis for microarray and RNA-seq data\u003c/strong\u003e. \u003cem\u003eBMC Bioinformatics \u003c/em\u003e2013, \u003cstrong\u003e14\u003c/strong\u003e:7.doi.org/10.1186/1471-2105-14-7.\u003c/li\u003e\n\u003cli\u003eNguyen CT, Caruso S, Maille P, Beaufr\u0026egrave;re A, Augustin J, Favre L, Pujals A, Boulagnon-Rombi C, Rhaiem R, Amaddeo G\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eImmune Profiling of Combined Hepatocellular- Cholangiocarcinoma Reveals Distinct Subtypes and Activation of Gene Signatures Predictive of Response to Immunotherapy\u003c/strong\u003e. \u003cem\u003eClin Cancer Res \u003c/em\u003e2022, \u003cstrong\u003e28\u003c/strong\u003e(3):540-551.doi.org/10.1158/1078-0432.Ccr-21-1219.\u003c/li\u003e\n\u003cli\u003eBindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, Angell H, Fredriksen T, Lafontaine L, Berger A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eSpatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer\u003c/strong\u003e. \u003cem\u003eImmunity \u003c/em\u003e2013, \u003cstrong\u003e39\u003c/strong\u003e(4):782-795.doi.org/10.1016/j.immuni.2013.10.003.\u003c/li\u003e\n\u003cli\u003eYu G, Wang LG, Han Y, He QY: \u003cstrong\u003eclusterProfiler: an R package for comparing biological themes among gene clusters\u003c/strong\u003e. \u003cem\u003eOmics \u003c/em\u003e2012, \u003cstrong\u003e16\u003c/strong\u003e(5):284-287.doi.org/10.1089/omi.2011.0118.\u003c/li\u003e\n\u003cli\u003eTang G, Peng J, Huo L, Yin W: \u003cstrong\u003eAn N6-methyladenosine regulation- and mRNAsi-related prognostic index reveals the distinct immune microenvironment and immunotherapy responses in lower-grade glioma\u003c/strong\u003e. \u003cem\u003eBMC Bioinformatics \u003c/em\u003e2023, \u003cstrong\u003e24\u003c/strong\u003e(1):225.doi.org/10.1186/s12859-023-05328-7.\u003c/li\u003e\n\u003cli\u003eShen W, Song Z, Zhong X, Huang M, Shen D, Gao P, Qian X, Wang M, He X, Wang T\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eSangerbox: A comprehensive, interaction-friendly clinical bioinformatics analysis platform\u003c/strong\u003e. \u003cem\u003eiMeta \u003c/em\u003e2022, \u003cstrong\u003e1\u003c/strong\u003e(3):e36.doi.org/https://doi.org/10.1002/imt2.36.\u003c/li\u003e\n\u003cli\u003eMayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP: \u003cstrong\u003eMaftools: efficient and comprehensive analysis of somatic variants in cancer\u003c/strong\u003e. \u003cem\u003eGenome Res \u003c/em\u003e2018, \u003cstrong\u003e28\u003c/strong\u003e(11):1747-1756.doi.org/10.1101/gr.239244.118.\u003c/li\u003e\n\u003cli\u003eLin Y, Pan X, Zhao L, Yang C, Zhang Z, Wang B, Gao Z, Jiang K, Ye Y, Wang S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eImmune cell infiltration signatures identified molecular subtypes and underlying mechanisms in gastric cancer\u003c/strong\u003e. \u003cem\u003eNPJ Genom Med \u003c/em\u003e2021, \u003cstrong\u003e6\u003c/strong\u003e(1):83.doi.org/10.1038/s41525-021-00249-x.\u003c/li\u003e\n\u003cli\u003eWculek SK, Cueto FJ, Mujal AM, Melero I, Krummel MF, Sancho D: \u003cstrong\u003eDendritic cells in cancer immunology and immunotherapy\u003c/strong\u003e. \u003cem\u003eNat Rev Immunol \u003c/em\u003e2020, \u003cstrong\u003e20\u003c/strong\u003e(1):7-24.doi.org/10.1038/s41577-019-0210-z.\u003c/li\u003e\n\u003cli\u003eSugiura D, Maruhashi T, Okazaki IM, Shimizu K, Maeda TK, Takemoto T, Okazaki T: \u003cstrong\u003eRestriction of PD-1 function by cis-PD-L1/CD80 interactions is required for optimal T cell responses\u003c/strong\u003e. \u003cem\u003eScience \u003c/em\u003e2019, \u003cstrong\u003e364\u003c/strong\u003e(6440):558-566.doi.org/10.1126/science.aav7062.\u003c/li\u003e\n\u003cli\u003eThorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, Porta-Pardo E, Gao GF, Plaisier CL, Eddy JA\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe Immune Landscape of Cancer\u003c/strong\u003e. \u003cem\u003eImmunity \u003c/em\u003e2018, \u003cstrong\u003e48\u003c/strong\u003e(4):812-830.e814.doi.org/10.1016/j.immuni.2018.03.023.\u003c/li\u003e\n\u003cli\u003eWang H, Xu T, Huang Q, Jin W, Chen J: \u003cstrong\u003eImmunotherapy for Malignant Glioma: Current Status and Future Directions\u003c/strong\u003e. \u003cem\u003eTrends Pharmacol Sci \u003c/em\u003e2020, \u003cstrong\u003e41\u003c/strong\u003e(2):123-138.doi.org/10.1016/j.tips.2019.12.003.\u003c/li\u003e\n\u003cli\u003eHodges TR, Ott M, Xiu J, Gatalica Z, Swensen J, Zhou S, Huse JT, de Groot J, Li S, Overwijk WW\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMutational burden, immune checkpoint expression, and mismatch repair in glioma: implications for immune checkpoint immunotherapy\u003c/strong\u003e. \u003cem\u003eNeuro Oncol \u003c/em\u003e2017, \u003cstrong\u003e19\u003c/strong\u003e(8):1047-1057.doi.org/10.1093/neuonc/nox026.\u003c/li\u003e\n\u003cli\u003eSha D, Jin Z, Budczies J, Kluck K, Stenzinger A, Sinicrope FA: \u003cstrong\u003eTumor Mutational Burden as a Predictive Biomarker in Solid Tumors\u003c/strong\u003e. \u003cem\u003eCancer Discov \u003c/em\u003e2020, \u003cstrong\u003e10\u003c/strong\u003e(12):1808-1825.doi.org/10.1158/2159-8290.Cd-20-0522.\u003c/li\u003e\n\u003cli\u003eZipp F, Bittner S, Schafer DP: \u003cstrong\u003eCytokines as emerging regulators of central nervous system synapses\u003c/strong\u003e. \u003cem\u003eImmunity \u003c/em\u003e2023, \u003cstrong\u003e56\u003c/strong\u003e(5):914-925.doi.org/10.1016/j.immuni.2023.04.011.\u003c/li\u003e\n\u003cli\u003ePiekarska K, Bonowicz K, Grzanka A, Jaworski Ł M, Reiter RJ, Slominski AT, Steinbrink K, Kleszczyński K, Gagat M: \u003cstrong\u003eMelatonin and TGF-\u0026beta;-Mediated Release of Extracellular Vesicles\u003c/strong\u003e. \u003cem\u003eMetabolites \u003c/em\u003e2023, \u003cstrong\u003e13\u003c/strong\u003e(4).doi.org/10.3390/metabo13040575.\u003c/li\u003e\n\u003cli\u003eXu S, Tang L, Li X, Fan F, Liu Z: \u003cstrong\u003eImmunotherapy for glioma: Current management and future application\u003c/strong\u003e. \u003cem\u003eCancer Lett \u003c/em\u003e2020, \u003cstrong\u003e476\u003c/strong\u003e:1-12.doi.org/10.1016/j.canlet.2020.02.002.\u003c/li\u003e\n\u003cli\u003eSong YC, Huang HC, Chang CY, Lee HJ, Liu CT, Lo HY, Ho TY, Lin WC, Yen HR: \u003cstrong\u003eA Potential Herbal Adjuvant Combined With a Peptide-Based Vaccine Acts Against HPV-Related Tumors Through Enhancing Effector and Memory T-Cell Immune Responses\u003c/strong\u003e. \u003cem\u003eFront Immunol \u003c/em\u003e2020, \u003cstrong\u003e11\u003c/strong\u003e:62.doi.org/10.3389/fimmu.2020.00062.\u003c/li\u003e\n\u003cli\u003eYe L, Wang L, Yang J, Hu P, Zhang C, Tong S, Liu Z, Tian D: \u003cstrong\u003eIdentification of tumor antigens and immune subtypes in lower grade gliomas for mRNA vaccine development\u003c/strong\u003e. \u003cem\u003eJ Transl Med \u003c/em\u003e2021, \u003cstrong\u003e19\u003c/strong\u003e(1):352.doi.org/10.1186/s12967-021-03014-x.\u003c/li\u003e\n\u003cli\u003ePlatten M, Bunse L, Wick A, Bunse T, Le Cornet L, Harting I, Sahm F, Sanghvi K, Tan CL, Poschke I\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eA vaccine targeting mutant IDH1 in newly diagnosed glioma\u003c/strong\u003e. \u003cem\u003eNature \u003c/em\u003e2021, \u003cstrong\u003e592\u003c/strong\u003e(7854):463-468.doi.org/10.1038/s41586-021-03363-z.\u003c/li\u003e\n\u003cli\u003eLee AF, Yip S, Smith AC, Hayes MM, Nielsen TO, O\u0026apos;Connell JX: \u003cstrong\u003eLow-grade fibromyxoid sarcoma of the perineum with heterotopic ossification: case report and review of the literature\u003c/strong\u003e. \u003cem\u003eHum Pathol \u003c/em\u003e2011, \u003cstrong\u003e42\u003c/strong\u003e(11):1804-1809.doi.org/10.1016/j.humpath.2011.01.023.\u003c/li\u003e\n\u003cli\u003eSampieri L, Di Giusto P, Alvarez C: \u003cstrong\u003eCREB3 Transcription Factors: ER-Golgi Stress Transducers as Hubs for Cellular Homeostasis\u003c/strong\u003e. \u003cem\u003eFront Cell Dev Biol \u003c/em\u003e2019, \u003cstrong\u003e7\u003c/strong\u003e:123.doi.org/10.3389/fcell.2019.00123.\u003c/li\u003e\n\u003cli\u003eElkamhawy A, Lu Q, Nada H, Woo J, Quan G, Lee K: \u003cstrong\u003eThe Journey of DDR1 and DDR2 Kinase Inhibitors as Rising Stars in the Fight Against Cancer\u003c/strong\u003e. \u003cem\u003eInt J Mol Sci \u003c/em\u003e2021, \u003cstrong\u003e22\u003c/strong\u003e(12).doi.org/10.3390/ijms22126535.\u003c/li\u003e\n\u003cli\u003eKim D, You E, Jeong J, Ko P, Kim JW, Rhee S: \u003cstrong\u003eDDR2 controls the epithelial-mesenchymal-transition-related gene expression via c-Myb acetylation upon matrix stiffening\u003c/strong\u003e. \u003cem\u003eSci Rep \u003c/em\u003e2017, \u003cstrong\u003e7\u003c/strong\u003e(1):6847.doi.org/10.1038/s41598-017-07126-7.\u003c/li\u003e\n\u003cli\u003eDeng L, Liu G, Zheng C, Zhang L, Kang Y, Yang F: \u003cstrong\u003eCirc-LAMP1 promotes T-cell lymphoblastic lymphoma progression via acting as a ceRNA for miR-615-5p to regulate DDR2 expression\u003c/strong\u003e. \u003cem\u003eGene \u003c/em\u003e2019, \u003cstrong\u003e701\u003c/strong\u003e:146-151.doi.org/10.1016/j.gene.2019.03.052.\u003c/li\u003e\n\u003cli\u003eSun M, Shen Z: \u003cstrong\u003eKnockdown of Long Non-Coding RNA (lncRNA) Colon Cancer-Associated Transcript-1 (CCAT1) Suppresses Oral Squamous Cell Carcinoma Proliferation, Invasion, and Migration by Inhibiting the Discoidin Domain Receptor 2 (DDR2)/ERK/AKT Axis\u003c/strong\u003e. \u003cem\u003eMed Sci Monit \u003c/em\u003e2020, \u003cstrong\u003e26\u003c/strong\u003e:e920020.doi.org/10.12659/msm.920020.\u003c/li\u003e\n\u003cli\u003eGao Y, Zhou J, Li J: \u003cstrong\u003eDiscoidin domain receptors orchestrate cancer progression: A focus on cancer therapies\u003c/strong\u003e. \u003cem\u003eCancer Sci \u003c/em\u003e2021, \u003cstrong\u003e112\u003c/strong\u003e(3):962-969.doi.org/10.1111/cas.14789.\u003c/li\u003e\n\u003cli\u003eTong S, Ye L, Xu Y, Sun Q, Gao L, Cai J, Ye Z, Tian D, Chen Q: \u003cstrong\u003eIRF2-ferroptosis related gene is associated with prognosis and EMT in gliomas\u003c/strong\u003e. \u003cem\u003eTransl Oncol \u003c/em\u003e2022, \u003cstrong\u003e26\u003c/strong\u003e:101544.doi.org/10.1016/j.tranon.2022.101544.\u003c/li\u003e\n\u003cli\u003eLi H, Lan T, Xu L, Liu H, Wang J, Li J, Chen X, Huang J, Li X, Yuan K\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eNCSTN promotes hepatocellular carcinoma cell growth and metastasis via \u0026beta;-catenin activation in a Notch1/AKT dependent manner\u003c/strong\u003e. \u003cem\u003eJ Exp Clin Cancer Res \u003c/em\u003e2020, \u003cstrong\u003e39\u003c/strong\u003e(1):128.doi.org/10.1186/s13046-020-01638-3.\u003c/li\u003e\n\u003cli\u003eFilipović A, Lombardo Y, Faronato M, Abrahams J, Aboagye E, Nguyen QD, d\u0026apos;Aqua BB, Ridley A, Green A, Rahka E\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAnti-nicastrin monoclonal antibodies elicit pleiotropic anti-tumour pharmacological effects in invasive breast cancer cells\u003c/strong\u003e. \u003cem\u003eBreast Cancer Res Treat \u003c/em\u003e2014, \u003cstrong\u003e148\u003c/strong\u003e(2):455-462.doi.org/10.1007/s10549-014-3119-z.\u003c/li\u003e\n\u003cli\u003eDebnath S, Sharma S: \u003cstrong\u003eRECQ1 Helicase in Genomic Stability and Cancer\u003c/strong\u003e. \u003cem\u003eGenes (Basel) \u003c/em\u003e2020, \u003cstrong\u003e11\u003c/strong\u003e(6).doi.org/10.3390/genes11060622.\u003c/li\u003e\n\u003cli\u003eViziteu E, Klein B, Basbous J, Lin YL, Hirtz C, Gourzones C, Tiers L, Bruyer A, Vincent L, Grandmougin C\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eRECQ1 helicase is involved in replication stress survival and drug resistance in multiple myeloma\u003c/strong\u003e. \u003cem\u003eLeukemia \u003c/em\u003e2017, \u003cstrong\u003e31\u003c/strong\u003e(10):2104-2113.doi.org/10.1038/leu.2017.54.\u003c/li\u003e\n\u003cli\u003eViziteu E, Kassambara A, Pasero P, Klein B, Moreaux J: \u003cstrong\u003eRECQ helicases are deregulated in hematological malignancies in association with a prognostic value\u003c/strong\u003e. \u003cem\u003eBiomark Res \u003c/em\u003e2016, \u003cstrong\u003e4\u003c/strong\u003e:3.doi.org/10.1186/s40364-016-0057-4.\u003c/li\u003e\n\u003cli\u003eMendoza-Maldonado R, Faoro V, Bajpai S, Berti M, Odreman F, Vindigni M, Ius T, Ghasemian A, Bonin S, Skrap M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe human RECQ1 helicase is highly expressed in glioblastoma and plays an important role in tumor cell proliferation\u003c/strong\u003e. \u003cem\u003eMol Cancer \u003c/em\u003e2011, \u003cstrong\u003e10\u003c/strong\u003e:83.doi.org/10.1186/1476-4598-10-83.\u003c/li\u003e\n\u003cli\u003eWang G, Yang X, Qi M, Li M, Dong M, Xu R, Zhang C: \u003cstrong\u003eSystematic analysis identifies REST as an oncogenic and immunological biomarker in glioma\u003c/strong\u003e. \u003cem\u003eSci Rep \u003c/em\u003e2023, \u003cstrong\u003e13\u003c/strong\u003e(1):3023.doi.org/10.1038/s41598-023-30248-0.\u003c/li\u003e\n\u003cli\u003eLi C, Zou H, Wang Z, Tang X, Fan X, Zhang K, Liu J, Li Z: \u003cstrong\u003eREST, not REST4, is a risk factor associated with radiotherapy plus chemotherapy efficacy in glioma\u003c/strong\u003e. \u003cem\u003eDrug Des Devel Ther \u003c/em\u003e2018, \u003cstrong\u003e12\u003c/strong\u003e:1363-1371.doi.org/10.2147/dddt.S161602.\u003c/li\u003e\n\u003cli\u003eWang J, Xiang H, Lu Y, Wu T: \u003cstrong\u003eRole and clinical significance of TGF\u003c/strong\u003e\u003cstrong\u003e‑\u0026beta;1 and TGF\u003c/strong\u003e\u003cstrong\u003e‑\u0026beta;R1 in malignant tumors (Review)\u003c/strong\u003e. \u003cem\u003eInt J Mol Med \u003c/em\u003e2021, \u003cstrong\u003e47\u003c/strong\u003e(4).doi.org/10.3892/ijmm.2021.4888.\u003c/li\u003e\n\u003cli\u003eJiang F, Yu Q, Chu Y, Zhu X, Lu W, Liu Q, Wang Q: \u003cstrong\u003eMicroRNA-98-5p inhibits proliferation and metastasis in non-small cell lung cancer by targeting TGFBR1\u003c/strong\u003e. \u003cem\u003eInt J Oncol \u003c/em\u003e2019, \u003cstrong\u003e54\u003c/strong\u003e(1):128-138.doi.org/10.3892/ijo.2018.4610.\u003c/li\u003e\n\u003cli\u003eZhang L, Song X, Chen X, Wang Q, Zheng X, Wu C, Jiang J: \u003cstrong\u003eCircular RNA CircCACTIN Promotes Gastric Cancer Progression by Sponging MiR-331-3p and Regulating TGFBR1 Expression\u003c/strong\u003e. \u003cem\u003eInt J Biol Sci \u003c/em\u003e2019, \u003cstrong\u003e15\u003c/strong\u003e(5):1091-1103.doi.org/10.7150/ijbs.31533.\u003c/li\u003e\n\u003cli\u003eYu X, Han N, Dong Z, Dang Y, Zhang Q, Hu W, Wang C, Du S, Lu Y: \u003cstrong\u003eCombined Chemo-Immuno-Photothermal Therapy for Effective Cancer Treatment via an All-in-One and One-for-All Nanoplatform\u003c/strong\u003e. \u003cem\u003eACS Appl Mater Interfaces \u003c/em\u003e2022, \u003cstrong\u003e14\u003c/strong\u003e(38):42988-43009.doi.org/10.1021/acsami.2c12969.\u003c/li\u003e\n\u003cli\u003eZhao Y, Liu X, Liu X, Yu J, Bai X, Wu X, Guo X, Liu Z, Liu X: \u003cstrong\u003eCombination of phototherapy with immune checkpoint blockade: Theory and practice in cancer\u003c/strong\u003e. \u003cem\u003eFront Immunol \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e:955920.doi.org/10.3389/fimmu.2022.955920.\u003c/li\u003e\n\u003cli\u003eFujimoto K, Arita H, Satomi K, Yamasaki K, Matsushita Y, Nakamura T, Miyakita Y, Umehara T, Kobayashi K, Tamura K\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eTERT promoter mutation status is necessary and sufficient to diagnose IDH-wildtype diffuse astrocytic glioma with molecular features of glioblastoma\u003c/strong\u003e. \u003cem\u003eActa Neuropathol \u003c/em\u003e2021, \u003cstrong\u003e142\u003c/strong\u003e(2):323-338.doi.org/10.1007/s00401-021-02337-9.\u003c/li\u003e\n\u003cli\u003eLi J, Lv F, Jin T: \u003cstrong\u003eStructuring and validating a prognostic model for low-grade gliomas based on the genes for plasma membrane tension\u003c/strong\u003e. \u003cem\u003eFront Neurol \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e:1024869.doi.org/10.3389/fneur.2022.1024869.\u003c/li\u003e\n\u003cli\u003eGubin MM, Artyomov MN, Mardis ER, Schreiber RD: \u003cstrong\u003eTumor neoantigens: building a framework for personalized cancer immunotherapy\u003c/strong\u003e. \u003cem\u003eJ Clin Invest \u003c/em\u003e2015, \u003cstrong\u003e125\u003c/strong\u003e(9):3413-3421.doi.org/10.1172/jci80008.\u003c/li\u003e\n\u003cli\u003eChan TA, Yarchoan M, Jaffee E, Swanton C, Quezada SA, Stenzinger A, Peters S: \u003cstrong\u003eDevelopment of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic\u003c/strong\u003e. \u003cem\u003eAnn Oncol \u003c/em\u003e2019, \u003cstrong\u003e30\u003c/strong\u003e(1):44-56.doi.org/10.1093/annonc/mdy495.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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 (LGGs), immunogenic cell death (ICD) subtypes, mRNA vaccine, tumor antigens, immunotherapy, tumor immune microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-3505524/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3505524/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Accumulating evidence demonstrated the effectiveness of mRNA vaccine against many cancers, however, their development in LGGs is still urgently needed. In addition, increasing evidence demonstrated that Immunogenic cell death (ICD) was associated with antitumor immune response. Thus, the aim of our study was to identify potential LGG tumor antigens for mRNA vaccine development and select suitable patients for vaccination based on ICD subtypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Gene expression matrix and matched clinical information of LGG were downloaded from the UCSC Xena website and CGGA databases. Differential expression analysis was conducted by GEPIA, and altered genomes were obtained from cBioPortal. TIMER was used for immune cell infiltration analysis, consensus clustering for typing ICD subtypes, and WGCNA for identifying hub modules and genes related to ICD subtypes. Eighty-two glioma tissue samples were collected and immunohistochemical staining was used to validate the correlation between tumor antigens and co-stimulatory factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e We identified seven potential LGG tumor antigens significantly correlated with poor prognosis and strongly positively correlated with infiltration of antigen-presenting cells, including CREB3L2, DDR2, IRF2, NCSTN, RECQL, REST, and TGFBR1. Furthermore, we identified two ICD subtypes in LGGs with different clinical, cellular, and molecular characteristics. Icds1 is an immunological \"hot\" and immunosuppression phenotype with a worse prognosis, while Icds2 is an immunological cold phenotype with a better prognosis. Finally, WGCNA identified hub immune-related genes associated with ICD subtypes, which could be potential vaccination biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e In summary, CREB3L2, DDR2, IRF2, NCSTN, RECQL, REST, and TGFBR1 are LGGs’ potential tumor antigens for mRNA vaccine development. The Icds2 subtype is suitable for vaccination.\u003c/p\u003e","manuscriptTitle":"Tumor antigens and immunogenic cell death subtypes guided mRNA vaccine development for lower-grade gliomas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-31 20:39:25","doi":"10.21203/rs.3.rs-3505524/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":"1ab67a5c-6e78-48dc-872d-858d21341335","owner":[],"postedDate":"October 31st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-05T16:10:05+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-31 20:39:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3505524","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3505524","identity":"rs-3505524","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}