Integrative analysis identified the key role of LAG3 in T cell exhaustion in glioma

Integrative analysis identified the key role of LAG3 in T cell exhaustion in glioma | 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 Integrative analysis identified the key role of LAG3 in T cell exhaustion in glioma Zheng Wang, Haitao Fu, Yuwei Li, Zewei Liu, Wenhua Fan, Yuqing Liu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7553633/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Lymphocyte-activation gene 3 (LAG3) is an immune checkpoint expressed on the surface of T cells. We conducted a comprehensive molecular characterization and clinical analysis of LAG3 expression in gliomas. Across the CGGA and TCGA glioma datasets, we observed that LAG3 expression increased with higher glioma grade. Further investigation into the molecular subtypes revealed that LAG3 exhibited the highest expression in the Mesenchymal subtype, similar to other immune checkpoints. CIBERSORT analysis showed a significant positive correlation between LAG3 expression and macrophages, suggesting LAG3's involvement in shaping the immune microenvironment. Single-cell data indicated that LAG3 expression appeared to be exclusive to other immune checkpoints, implying multiple mechanisms of T cell exhaustion. Importantly, clinical data analysis demonstrated that elevated LAG3 expression is an independent poor prognostic factor for patient survival. These findings suggest that LAG3 may play a crucial role in regulating the immune microenvironment in gliomas and contribute to T cell exhaustion, alongside other immune checkpoints such as the PD-L1/PD-1 pathway. As such, LAG3 represents a potential therapeutic target for glioma immunotherapy. glioma immune check point LAG3 immunotherapy immune evasion Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Lymphocyte-activation gene 3 (LAG3) is an immune checkpoint predominantly expressed on the surface of various immune cells, including T cells, natural killer cells, and certain myeloid cells 1 . LAG3 has been reported to play a role in the regulation of T cell activity during different immune responses 2 , 3 . The extracellular region of LAG3 contains four immunoglobulin superfamily domains, which share structural homology with CD4, enabling LAG3 to bind to class II major histocompatibility complex (MHC) and execute its immunoregulatory functions. As a key member of the immune checkpoint family, the primary biological function of LAG3 is to suppress the effector functions of T cells through interactions with co-stimulatory factors 1 . While other immune checkpoints, such as PD-L1 4,5 , PD-L2 6 , and B7-H3 7,8 , have been extensively studied in the context of tumor biology, LAG3 has been relatively less explored, especially in the case of glioma. In order to characterize the expression pattern of LAG3 in glioma, we integrated clinical and molecular data including single-cell data, and performed a comprehensive analysis. It was found that LAG3 was universally expressed in T cells within the tumor microenvironment. Interestingly, LAG3 expression appeared to be mutually exclusive to other classical immune checkpoints, such as PD-1 and CTLA-4, in glioma samples. This suggests that LAG3 may play a distinct role in regulating T cell exhaustion within the glioma immune landscape, potentially through alternative mechanisms compared to other well-studied immune checkpoint pathways. These findings warrant further investigation into the specific function of LAG3 in modulating the glioma immune microenvironment and its potential as a therapeutic target. Materials and Methods In this study, we collected RNA expression profiling data (FPKM) of 325 cases of gliomas across all grades from the Chinese Glioma Genome Atlas (CGGA) 9 , 10 . Additionally, we obtained 670 cases of glioma RNA sequencing data (RSEM) from The Cancer Genome Atlas (TCGA) 11 , 12 . Concurrently, we also collected the basic clinical information, such as age, gender, grade, and pathological classification, for the majority of the enrolled samples. This comprehensive dataset allowed us to perform a combined clinical-molecular analysis to characterize the expression pattern and clinical relevance of LAG3 in glioma. For bulk tumor RNA- sequencing data, log2 transformed FPKM data was used as the expression data while analyzing the expression distribution across different grades or subtypes, as well as for functional analysis. In addition, to further investigate the expression of LAG3 at the single-cell level in glioma, we collected public single-cell data published by researchers, including our own CGGA data 13 as well as datasets from Suva 14 and Richards et al 15 . For a more detailed analysis, we re-grouped all the cells according to key cell type markers combined with Seurat clustering results, and divided the cells into four major categories: tumor cells, myeloid cells, T cells, and oligodendrocytes. All the subsequent single-cell analysis was based on this refined cell type classification. The R language was used as the main tool for graphic and analytical work. Critical packages included ggplot2 16 and pheatmap 17 , which were mainly used for data visualization; fgsea and enrichplot were used for gene enrichment analysis; CIBERSORT was employed to analyze the relationship between LAG3 expression and immune cell components; and Seurat was used for the analysis of single-cell data. P-values less than 0.05 were considered statistically significant. Results The expression level of LAG3 increases with glioma pathological grade We reclassified the glioma samples (WHO grades 2, 3, 4) according to the newest WHO CNS tumor grading recommendations 18 . IDH mutation, the most important molecular marker for glioma classification, was also included in the analysis. We found that in both the CGGA and TCGA bulk tumor RNA sequencing datasets, the expression of LAG3 showed a significant upregulation with increasing tumor grade (Fig. 1A, 1B, t-test), suggesting that the intensity of the immune reaction within the tumor as the tumor grade progresses. However, we did not observe a stable correlation between LAG3 expression and IDH mutation status in the two datasets, although it appeared that LAG3 expression was generally higher in IDH-mutant gliomas (Fig. 1C, 1D, t-test). This is in stark contrast to our previous research on the PD-L1/PD-1 axis 4,5 . These findings suggest that the upregulation of LAG3 expression is associated with higher-grade gliomas, independent of the IDH mutation status, and may reflect changes in the tumor immune microenvironment during glioma progression. 2. LAG3 is most significantly expressed in the mesenchymal subtype Molecular subtypes based on expression profile usually indicates the preferred biological process of glioma. We further analyzed the expression of LAG3 according to the molecular subtypes 19 . In both CGGA and TCGA bulk tumor datasets, we observed that the expression of LAG3 was most significantly expressed in the mesenchymal subtype (Fig. 2A, 2B), which was particularly similar to the expression characteristics of PD-L1as described in our previous study 4 , suggesting that LAG3 may play a more significant role in mesenchymal subtype glioma than in that of others. This result was in consistence with the immune infiltrative features of mesenchymal subtype. As an immune checkpoint, LAG3 probably was involved in the formation of mesenchymal transition of glioma. 3. LAG3 plays different biological role in terms of IDH mutation status To further elucidate the biological role of LAG3 in glioma, we conducted functional enrichment analysis using two bulk tumor RNA-sequencing datasets. Whole genome Pearson correlation was performed for LAG3 expression in two datasets. Then R value was used as the input for fgsea package for functional analysis. Notably, LAG3 exhibited distinct biological roles based on IDH mutation status. In IDH mutant gliomas, LAG3 expression was primarily linked to cell proliferation, with key biological processes involving DNA replication, the cell cycle, and nuclear division (Fig. 3A-C, CGGA dataset; Figs. 3D-F, TCGA dataset). In IDH wild-type gliomas, LAG3 was associated with adaptive immunity, T cell proliferation, and γ-interferon-related immune responses (Fig. 3G-I, CGGA dataset; Fig. 3J-L, TCGA dataset). To further investigate the intrinsic function of LAG3, we refocused on the immunological role of LAG3 in glioma. Using the online tool CIBERSORT, we performed immune component analysis on the expression data from the two bulk tumor datasets, CGGA and TCGA. The CIBERSORT results showed that the proportion of macrophages, especially M1 type, was significantly correlated with LAG3 expression in both datasets (Pearson’s R values were 0.1901, p-value = 0.0128 in CGGA IDHmt, Fig. 4A; Pearson’s R values were 0.1469, p-value = 0.0023 in TCGA IDHmt, respectively, Fig. 4C). In IDH wild-type gliomas, LAG3 was also highly associated with macrophage M1 type (Pearson’s R values were 0.1842, p-value = 0.0222 in CGGA IDHwt, Fig. 4B; Pearson’s R values were 0.2143, p-value = 0.0010 in TCGA IDHwt, Fig. 4D). 4. Single-cell data reveals that LAG3 is mainly expressed on T cells and meyloids To further verify the expression and distribution characteristics of LAG3, we utilized the CGGA single-cell dataset 13 , as well as two published single-cell datasets - the Suva 2019 dataset 14 and the Richards dataset 15 . We re-grouped the single cells according to key markers combined with Seurat clustering into four major cell categories: tumor cells, myeloid-derived cells (myeloids), T cells, and oligodendrocytes (Fig. 5A-C). Across these three single-cell glioma datasets, we found that LAG3 was predominantly expressed on T cells and myeloids, with especially high expression on T cells. This was in good agreement with the patterns we observed in the bulk tumor RNA profiling analysis (Fig. 4B, D). The single - cell data were obtained from EGAS00001004656(https://ega-archive.org/studies/EGAS00001004656), GSE89567(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89567), and GSE131928(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131928). The expression characteristics of LAG3 were similar to that of PD-1 (PDCD1) 5 , while ITGAM (CD11b) was mainly expressed in myeloids. CD274 (PD-L1) was widely expressed, but predominantly in tumor cells (Fig. 5). These findings suggest that both myeloids and T cells simultaneously express immune checkpoint receptors such as LAG3 and PD-1, which may result in the exhaustion of their cytotoxic functions within the glioma microenvironment. 5. The expression of different immune checkpoints on T cells is mutually exclusive. To explore the relationship between LAG3 and PD-1 (PDCD1) expression in T cells, we extracted the T cell populations from the single-cell datasets and performed a co-expression analysis of these two classical immune checkpoint receptors across the three datasets (Fig. 6A-C). As shown in Fig. 6, except for a small number of cells that co-expressed the two markers (yellow-colored cells), LAG3 and PD-1 were largely expressed in a mutually exclusive manner, with most T cells expressing only one of the two immune checkpoint receptors. This suggested that LAG3 and PD-1 were less likely to be simultaneously expressed on the same individual T cells. To further validate this finding, we conducted a more in-depth analysis enrolling additional immune checkpoint receptors (Fig. 7), including LAG3, PDCD1, HAVCR2, CTLA4, KLRG1, CD244, and BTLA. From the expression heatmap of T cells (Fig. 7A-C), it was evident that the classical immune checkpoint receptors expressed on these T cells were mutually exclusive in most circumstances. With the single cell expression data of these classical immune checkpoints, we performed Spearman correlation analysis for one another. The correlation analysis results were presented with circus plots, showing the mainly negative correlation patterns among the various immune checkpoint receptors (Fig. 7D-F). These results indicate that, within the glioma microenvironment, individual T cells tend to express only a subset of the available immune checkpoint receptors, rather than co-expressing multiple checkpoints simultaneously. This may have important implications for understanding T cell exhaustion and the potential for combination immunotherapy strategies. 6. The expression of LAG3 is significantly correlated with prognosis of glioma patients Subsequently, we obtained the clinical survival data of the patients. It can be seen from the Kaplan-Meier curves that in the CGGA and TCGA datasets, patients with higher LAG3 expression in their tumors tended to suffer shorter overall survival, and there is no significant correlation with IDH mutation status (Fig. 8A-D). Multivariate Cox proportional hazards model also further confirmed the significance of LAG3 as independently prognosis makers (Tables 1 and 2). Table 1 Multi-variates Cox proportional hazards model in CGGA dataset coef exp(coef) lower0.95 upper0.95 P value Age 0.005902 1.005919 0.9905 1.0216 0.454585 Gender (male) 0.108712 1.114841 0.783 1.5873 0.546479 Grade 0.9865 2.681831 2.0459 3.5155 9.11×10 − 13 IDH -0.615078 0.540599 0.3547 0.8239 0.004219 LAG3 0.581851 1.789347 1.2999 2.463 0.000358 Table 2 Multi-variates Cox proportional hazards model in TCGA dataset coef exp(coef) lower 0.95 upper 0.95 P value Age 0.04941 1.05065 1.037 1.0645 1.52×10 − 13 Gender (male) 0.04346 0.95747 0.7 1.3097 0.7857 IDH 1.88445 0.15191 0.102 0.2263 < 2×10 − 16 LAG3 0.15737 1.17043 1.023 1.3395 0.0223 Discussion Although the brain has been recognized as an immune privileged organ, increasing studies have shown that gliomas often exhibit strong immunogenicity due to their intrinsic genetic, transcriptomic, and proteomic heterogeneity 20 , 21 . This leads the immune system to recognize glioma cells as antigenic targets and mount an immune response against them 22 . However, the continuous progression of gliomas typically indicates a complete failure of the body's immune defense, ultimately allowing the tumors to escape immune surveillance 23 . The mechanisms underlying glioma immune evasion are complex, involving not only the tumor cells themselves, but critically, their dynamic interactions and crosstalk with various immune cell populations 24 , 25 . For example, glioma cells can downregulate MHC expression, upregulate immune checkpoint receptors, and secrete immunosuppressive cytokines. Additionally, the tumor-associated macrophages are often polarized towards an M2 phenotype, which further promotes an immunosuppressive tumor microenvironment by secreting anti-inflammatory mediators and factors that support tumor growth 26 , 27 . Furthermore, the function of infiltrating T cells is often severely impaired, leading to a state of T cell exhaustion 28 – 30 . Under the combined effects of these multifaceted immune evasion mechanisms, gliomas are able to ultimately escape the cytotoxic effects of the body's immune system, enabling their uncontrolled malignant progress. Understanding these complex immune dynamics within the glioma microenvironment is crucial for developing more effective immunotherapeutic strategies. While progress has been made in targeting immune checkpoint pathways, especially in tumors such as melanoma 31 , 32 , kidney cancer 33 , 34 , and lung cancer 35 , 36 , little advancement has been achieved in the glioma setting. Treatment regimens focused on classical immune checkpoint inhibitors targeting the PD-1/PD-L1 axis have not yielded satisfactory results in glioma 37 , 38 . This warrants additional efforts to investigate other immune checkpoint receptors and explore novel targeting treatment options for this devastating disease. In this context, we performed an integrated clinical and molecular analysis of the immune checkpoint receptor LAG3 in glioma. We obtained glioma RNA-sequencing data from the CGGA and TCGA datasets, as well as published single-cell datasets. Through these comprehensive datasets, we found that LAG3 expression, similar to PD-1/PD-L1, increased with the degree of glioma malignancy. However, unlike PD-1/PD-L1, LAG3 expression levels were not stably associated with IDH mutation status, suggesting that LAG3 expression was independent of IDH mutation. This indicates that the tumor microenvironment, with or without IDH1 mutation, did not significantly affect LAG3 expression. Intriguingly, the biological role of LAG3 appears to differ between IDH-mutant and IDH wild-type gliomas. In IDH wild-type tumors, LAG3 expression was often associated with tumor cell proliferation, while in IDH-mutant gliomas, the primary role of LAG3 was related to immune suppression. These findings suggest that LAG3 may play distinct biological roles in different glioma molecular subgroups and their corresponding microenvironments. Single-cell data analysis revealed that LAG3 was almost exclusively expressed on the surface of T cells, in contrast to other immune checkpoint receptors such as PD-1, TIM-3, and CTLA4. Moreover, the expression of these classical immune checkpoint receptors on T cells was often mutually exclusive. This phenomenon suggests that T cells within the glioma microenvironment typically express a limited subset of immune checkpoint receptors, rather than co-expressing multiple checkpoints simultaneously. This may represent a mechanism by which T cells achieve an exhausted state. To our knowledge, this is the first description of the mutually exclusive expression pattern of immune checkpoint receptors on T cells in the context of malignancy. This unique expression pattern of immune checkpoints on glioma-infiltrating T cells has important implications. It cautions us that when treating glioma patients with immune checkpoint inhibitors, we need to be particularly aware of the potential for multiple checkpoint receptor expression on T cells. This property also provides a strong theoretical basis for the concomitant use of various immune checkpoint inhibitors as a combination immunotherapy approach for glioma patients. Consistent with the role of most immune checkpoint receptors, higher expression levels of LAG3 were associated with worse patient survival, and this prognostic value was independent of IDH mutation status and other clinical factors. Collectively, these findings suggest that LAG3 may hold significant promise as a target for the development of novel immune checkpoint inhibitor therapies for glioma 39 , 40 . Conclusion Through the integrated analysis of the clinical characteristics and molecular data of LAG3, it is revealed that LAG3 is an important immune checkpoint in glioma, participates in the immune evasion of glioma, and is an independent unfavorable prognostic factor for glioma patients. It can be used as a promising candidate target for immune checkpoint inhibitors other than PD-1/PD-L1. Limitations This study has several limitations. First, the dataset size is relatively limited, which may restrict the robustness of the statistical analyses, particularly for rare outcome events. Second, we only used data from public database and did not perform external validation with independent datasets, which may affect the generalizability of our results to other populations. Declarations Publish Declaration We, the corresponding author and all co-authors of the manuscript titled “Integrative analysis identified the key role of LAG3 in T cell exhaustion in glioma”(submitted to Discover Oncology), hereby declare the following regarding the publication of this manuscript: The content is original, has not been published previously, and is not under consideration by any other journal. There is no duplicate publication issue . If accepted, we will sign the copyright agreement as required by the journal, acknowledging that authors retain copyright, Springer Nature holds the right to publish, and the manuscript will be subject to the Creative Commons open access license. We understand the journal’s open access requirements, will pay the Article Processing Charge (APC) as required, and agree that the manuscript will be freely accessible upon publication. Disclosure This work was supported by National Natural Science Foundation of China (81902528、81972816) and Beijing Hospital Authority Projects (PX2021025). The authors declare no interests. The authors used no AI tools during the preparation of this work. Consent to participate Not applicable Consent to publish Not applicable Ethical Approval and accordance Author Contribution Conceptualization: Zheng Wang, Haitao Fu, Zhong Zhang, Liyun Zhong, Wei Yan;Data Curation: Zheng Wang, Haitao Fu, Yuwei Li, Zewei Liu, Wenhua Fan, Yuqing Liu, Chuanbao Zhang, Jingshan Liang;Methodology: all the authors;Supervision: all the authors;Writing – original draft: all the authors;Writing – review & editing: all the authors. Acknowledgement We thank all the researchers for sharing such valuable RNA-seq profiling data and single cell sequencing data. Moreover, we appreciate the R software and all the packages used in the analysis procedure and thank the authors for their selflessness. Data Availability TCGA bulk tumor RNAseq data was obtained from cBioportal(https://www.cbioportal.org/), CGGA bulk tumor RNAseq data was obtained from CGGA website (Chinese Glioma Genome Atlas [https://www.cgga.org.cn](https:/www.cgga.org.cn) ).The single - cell data were obtained from EGAS00001004656(https://ega-archive.org/studies/EGAS00001004656), GSE89567(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE89567), and GSE131928(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131928). References Camisaschi C, et al. LAG-3 expression defines a subset of CD4(+)CD25(high)Foxp3(+) regulatory T cells that are expanded at tumor sites. J Immunol. 2010;184:6545–51. https://doi.org/10.4049/jimmunol.0903879 . Long L, et al. The promising immune checkpoint LAG-3: from tumor microenvironment to cancer immunotherapy. Genes cancer. 2018;9:176–89. https://doi.org/10.18632/genesandcancer.180 . Sidaway P. LAG3 inhibition improves outcomes. 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Int J Immunopathol Pharmacol. 2021;35:20587384211056505. https://doi.org/10.1177/20587384211056505 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Dec, 2025 Reviews received at journal 06 Dec, 2025 Reviewers agreed at journal 04 Dec, 2025 Reviews received at journal 10 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviewers invited by journal 04 Nov, 2025 Editor invited by journal 27 Oct, 2025 Editor assigned by journal 15 Oct, 2025 Submission checks completed at journal 12 Oct, 2025 First submitted to journal 12 Oct, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7553633","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":543005575,"identity":"75a4ab90-f3c6-4b98-a844-b7bd482f9361","order_by":0,"name":"Zheng Wang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Wang","suffix":""},{"id":543005576,"identity":"bc36ddb0-2e87-40a9-bff7-8f00d97c544b","order_by":1,"name":"Haitao Fu","email":"","orcid":"","institution":"Capital Medical 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jingshan","middleName":"","lastName":"Liang","suffix":""},{"id":543005585,"identity":"ea63bb06-f433-47da-acf4-5d8231902566","order_by":8,"name":"Zhong Zhang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhong","middleName":"","lastName":"Zhang","suffix":""},{"id":543005586,"identity":"f415ac7e-6743-481f-b28e-6baafab431c4","order_by":9,"name":"Liyun Zhong","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liyun","middleName":"","lastName":"Zhong","suffix":""},{"id":543005587,"identity":"1173d885-55d6-4d8a-80ac-392b25c88bfb","order_by":10,"name":"Wei Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDADfgaGBBDN2EC0FskGkrUYHIDQhLWYs/eYffjAcDhx8+2Gp5t5GGxkNxxgfvYAnxbLnjPGM2cAtWy7cyDtNg9DmvGGA2zmBnjdcyPHmJmH4XDuthsJIC2HEzcc4GGTwKvl/huIls0zwFr+E6HlBg9EywYJsJYDRGg5k1bMOIMhvX4G0GE35xgkG888zGaGX8vxw5sZPjBYG/PPyEm78abCTrbvePMzvFrAgPFfM5DkSQCaAKSZCaoHgzogZj9AnNpRMApGwSgYcQAAFBBK0lTVq48AAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2025-09-07 02:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7553633/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7553633/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96048847,"identity":"0089278b-ccb5-4d50-a980-ae6aa3f336c1","added_by":"auto","created_at":"2025-11-17 06:29:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2885083,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7553633/v1/1771019ed3e057582d2494f7.docx"},{"id":96048842,"identity":"ddd87b22-ae83-408b-a1ee-d764e1dc9416","added_by":"auto","created_at":"2025-11-17 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06:29:06","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":52064,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7553633/v1/db8243614f85237585ea31f2.png"},{"id":96048863,"identity":"a336650c-f422-491d-bedb-7dcc05fd5ece","added_by":"auto","created_at":"2025-11-17 06:29:06","extension":"xml","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":99529,"visible":true,"origin":"","legend":"","description":"","filename":"3a2bfd96f80e4fde9bfa7d942e6218491structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7553633/v1/6b33880567e9b77819058ba4.xml"},{"id":96048857,"identity":"da3381ed-9b5d-4b8c-b9dc-96c7a81caedb","added_by":"auto","created_at":"2025-11-17 06:29:06","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110314,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7553633/v1/4d0033d0e2df470f189ea943.html"},{"id":96247325,"identity":"c8d41a70-3daa-4146-a71b-2e2bc12968e2","added_by":"auto","created_at":"2025-11-19 07:27:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":166031,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of LAG3 expression in different WHO grade gliomas in CGGA (A) and TCGA (B) bulk tumor RNA-seq datasets. No significant effect of IDH on LAG3 expression was observed in both datasets (C, D). P value are generated with t-test comparing two groups of continuous values.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7553633/v1/bdaf16d237471b4995bf7f67.png"},{"id":96048844,"identity":"363fbc22-fb29-458e-be86-b215cc011812","added_by":"auto","created_at":"2025-11-17 06:29:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81496,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of LAG3 expression levels in different molecular subtypes (A, B).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7553633/v1/8126182fadaa63e8b883b0cd.png"},{"id":96048846,"identity":"4e7d60c3-b289-461b-b427-d861988afe59","added_by":"auto","created_at":"2025-11-17 06:29:06","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":307796,"visible":true,"origin":"","legend":"\u003cp\u003eLAG3 is mainly associated with cell proliferation in IDH-mutant gliomas (A-C, CGGA dataset; D-F, TCGA dataset), while is mainly associated with immune responses in IDH wild-type gliomas (G-I, CGGA dataset; J-L, TCGA dataset).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7553633/v1/c042f7f4d3858a4b63f598e9.jpeg"},{"id":96246596,"identity":"7dce3117-b24f-468e-bb24-32526e588940","added_by":"auto","created_at":"2025-11-19 07:26:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1178279,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of immune cell components by CIBERSORT revealed that LAG3 expression was predominantly associated with macrophages (M1) in IDH-mutant gliomas (A, CGGA dataset; C, TCGA dataset). In IDH wild-type gliomas, LAG3 expression was also mainly associated with macrophages (B, CGGA dataset; D, TCGA dataset).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7553633/v1/60972444cf16b1849e00157a.png"},{"id":96048850,"identity":"dc918704-0c2a-4603-b29f-ff9dea7c7f70","added_by":"auto","created_at":"2025-11-17 06:29:06","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":220644,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of classical immune markers LAG3, PDCD1 (PD-1), ITGAM (CD11b), and CD274 (PD-L1) in the glioma single cells in three datasets. We found that LAG3 and PDCD1 were mainly expressed in T cells, ITGAM (CD11b) was mainly expressed in myeloids, while CD274 (PD-L1) was relatively widely expressed among tumor cells, myeloids and T cells.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7553633/v1/f55f9535df27587c1907e616.jpeg"},{"id":96048856,"identity":"e2186159-a3e5-42ff-900b-cff048b275b7","added_by":"auto","created_at":"2025-11-17 06:29:06","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":223558,"visible":true,"origin":"","legend":"\u003cp\u003eLAG3 and PDCD1 expression pattern on T cells in three single cell datasets (A-C). Only a small amount of cell co-expressed two immune checkpoints.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7553633/v1/e9ede2f89648ce1b7f0fcfba.jpeg"},{"id":96048855,"identity":"57fb8a5b-e528-4289-a6be-abda9f0e98b7","added_by":"auto","created_at":"2025-11-17 06:29:06","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":311800,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmaps (A-C) and correlation analysis (D-F) of T cell surface immune checkpoint expression patterns. D-F, Green strips indicate negative correlations and red strips indicate positive correlations. Both strip color depth and strip width represent the absolute value of the R value. The outer ring of the circus plot represents the sum of the absolute r values of the corresponding marker correlating with all the markers.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7553633/v1/102bd19c054c4f8981d3b6df.jpeg"},{"id":96048858,"identity":"a10fd6a6-c899-4b41-bf77-6ce34f17e816","added_by":"auto","created_at":"2025-11-17 06:29:06","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":275131,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival prognostic analysis. Higher LAG3 expression indicates significantly worse survival outcome (A, C, IDH-mutant glioma; B, D, IDH-wild glioma).\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7553633/v1/92b12af12d34d6d3dfa3ed04.jpeg"},{"id":96256094,"identity":"dd231e62-2dca-4d3e-9816-dfe84e19be32","added_by":"auto","created_at":"2025-11-19 07:49:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3432841,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7553633/v1/64b7408a-aed6-451c-9af1-a40e37730d63.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative analysis identified the key role of LAG3 in T cell exhaustion in glioma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLymphocyte-activation gene 3 (LAG3) is an immune checkpoint predominantly expressed on the surface of various immune cells, including T cells, natural killer cells, and certain myeloid cells\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. LAG3 has been reported to play a role in the regulation of T cell activity during different immune responses\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The extracellular region of LAG3 contains four immunoglobulin superfamily domains, which share structural homology with CD4, enabling LAG3 to bind to class II major histocompatibility complex (MHC) and execute its immunoregulatory functions. As a key member of the immune checkpoint family, the primary biological function of LAG3 is to suppress the effector functions of T cells through interactions with co-stimulatory factors\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. While other immune checkpoints, such as PD-L1\u003csup\u003e4,5\u003c/sup\u003e, PD-L2\u003csup\u003e6\u003c/sup\u003e, and B7-H3\u003csup\u003e7,8\u003c/sup\u003e, have been extensively studied in the context of tumor biology, LAG3 has been relatively less explored, especially in the case of glioma.\u003c/p\u003e\u003cp\u003eIn order to characterize the expression pattern of LAG3 in glioma, we integrated clinical and molecular data including single-cell data, and performed a comprehensive analysis. It was found that LAG3 was universally expressed in T cells within the tumor microenvironment. Interestingly, LAG3 expression appeared to be mutually exclusive to other classical immune checkpoints, such as PD-1 and CTLA-4, in glioma samples. This suggests that LAG3 may play a distinct role in regulating T cell exhaustion within the glioma immune landscape, potentially through alternative mechanisms compared to other well-studied immune checkpoint pathways. These findings warrant further investigation into the specific function of LAG3 in modulating the glioma immune microenvironment and its potential as a therapeutic target.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eIn this study, we collected RNA expression profiling data (FPKM) of 325 cases of gliomas across all grades from the Chinese Glioma Genome Atlas (CGGA) \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Additionally, we obtained 670 cases of glioma RNA sequencing data (RSEM) from The Cancer Genome Atlas (TCGA) \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Concurrently, we also collected the basic clinical information, such as age, gender, grade, and pathological classification, for the majority of the enrolled samples. This comprehensive dataset allowed us to perform a combined clinical-molecular analysis to characterize the expression pattern and clinical relevance of LAG3 in glioma.\u003c/p\u003e\u003cp\u003eFor bulk tumor RNA- sequencing data, log2 transformed FPKM data was used as the expression data while analyzing the expression distribution across different grades or subtypes, as well as for functional analysis.\u003c/p\u003e\u003cp\u003eIn addition, to further investigate the expression of LAG3 at the single-cell level in glioma, we collected public single-cell data published by researchers, including our own CGGA data\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e as well as datasets from Suva\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and Richards et al\u003csup\u003e15\u003c/sup\u003e. For a more detailed analysis, we re-grouped all the cells according to key cell type markers combined with Seurat clustering results, and divided the cells into four major categories: tumor cells, myeloid cells, T cells, and oligodendrocytes. All the subsequent single-cell analysis was based on this refined cell type classification.\u003c/p\u003e\u003cp\u003eThe R language was used as the main tool for graphic and analytical work. Critical packages included ggplot2\u003csup\u003e16\u003c/sup\u003e and pheatmap\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, which were mainly used for data visualization; fgsea and enrichplot were used for gene enrichment analysis; CIBERSORT was employed to analyze the relationship between LAG3 expression and immune cell components; and Seurat was used for the analysis of single-cell data. P-values less than 0.05 were considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003col\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eThe expression level of LAG3 increases with glioma pathological grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe reclassified the glioma samples (WHO grades 2, 3, 4) according to the newest WHO CNS tumor grading recommendations\u003csup\u003e18\u003c/sup\u003e. IDH mutation, the most important molecular marker for glioma classification, was also included in the analysis. We found that in both the CGGA and TCGA bulk tumor RNA sequencing datasets, the expression of LAG3 showed a significant upregulation with increasing tumor grade (Fig.\u0026nbsp;1A, 1B, t-test), suggesting that the intensity of the immune reaction within the tumor as the tumor grade progresses. However, we did not observe a stable correlation between LAG3 expression and IDH mutation status in the two datasets, although it appeared that LAG3 expression was generally higher in IDH-mutant gliomas (Fig.\u0026nbsp;1C, 1D, t-test). This is in stark contrast to our previous research on the PD-L1/PD-1 axis\u003csup\u003e4,5\u003c/sup\u003e. These findings suggest that the upregulation of LAG3 expression is associated with higher-grade gliomas, independent of the IDH mutation status, and may reflect changes in the tumor immune microenvironment during glioma progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. LAG3 is most significantly expressed in the mesenchymal subtype\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular subtypes based on expression profile usually indicates the preferred biological process of glioma. We further analyzed the expression of LAG3 according to the molecular subtypes\u003csup\u003e19\u003c/sup\u003e. In both CGGA and TCGA bulk tumor datasets, we observed that the expression of LAG3 was most significantly expressed in the mesenchymal subtype (Fig.\u0026nbsp;2A, 2B), which was particularly similar to the expression characteristics of PD-L1as described in our previous study\u003csup\u003e4\u003c/sup\u003e, suggesting that LAG3 may play a more significant role in mesenchymal subtype glioma than in that of others. This result was in consistence with the immune infiltrative features of mesenchymal subtype. As an immune checkpoint, LAG3 probably was involved in the formation of mesenchymal transition of glioma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. LAG3 plays different biological role in terms of IDH mutation status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further elucidate the biological role of LAG3 in glioma, we conducted functional enrichment analysis using two bulk tumor RNA-sequencing datasets. Whole genome Pearson correlation was performed for LAG3 expression in two datasets. Then R value was used as the input for fgsea package for functional analysis.\u003c/p\u003e\n\u003cp\u003eNotably, LAG3 exhibited distinct biological roles based on IDH mutation status. In IDH mutant gliomas, LAG3 expression was primarily linked to cell proliferation, with key biological processes involving DNA replication, the cell cycle, and nuclear division (Fig.\u0026nbsp;3A-C, CGGA dataset; Figs.\u0026nbsp;3D-F, TCGA dataset). In IDH wild-type gliomas, LAG3 was associated with adaptive immunity, T cell proliferation, and \u0026gamma;-interferon-related immune responses (Fig.\u0026nbsp;3G-I, CGGA dataset; Fig.\u0026nbsp;3J-L, TCGA dataset).\u003c/p\u003e\n\u003cp\u003eTo further investigate the intrinsic function of LAG3, we refocused on the immunological role of LAG3 in glioma. Using the online tool CIBERSORT, we performed immune component analysis on the expression data from the two bulk tumor datasets, CGGA and TCGA. The CIBERSORT results showed that the proportion of macrophages, especially M1 type, was significantly correlated with LAG3 expression in both datasets (Pearson\u0026rsquo;s R values were 0.1901, p-value\u0026thinsp;=\u0026thinsp;0.0128 in CGGA IDHmt, Fig.\u0026nbsp;4A; Pearson\u0026rsquo;s R values were 0.1469, p-value\u0026thinsp;=\u0026thinsp;0.0023 in TCGA IDHmt, respectively, Fig.\u0026nbsp;4C). In IDH wild-type gliomas, LAG3 was also highly associated with macrophage M1 type (Pearson\u0026rsquo;s R values were 0.1842, p-value\u0026thinsp;=\u0026thinsp;0.0222 in CGGA IDHwt, Fig.\u0026nbsp;4B; Pearson\u0026rsquo;s R values were 0.2143, p-value\u0026thinsp;=\u0026thinsp;0.0010 in TCGA IDHwt, Fig.\u0026nbsp;4D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Single-cell data reveals that LAG3 is mainly expressed on T cells and meyloids\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further verify the expression and distribution characteristics of LAG3, we utilized the CGGA single-cell dataset\u003csup\u003e13\u003c/sup\u003e, as well as two published single-cell datasets - the Suva 2019 dataset\u003csup\u003e14\u003c/sup\u003e and the Richards dataset\u003csup\u003e15\u003c/sup\u003e. We re-grouped the single cells according to key markers combined with Seurat clustering into four major cell categories: tumor cells, myeloid-derived cells (myeloids), T cells, and oligodendrocytes (Fig.\u0026nbsp;5A-C). Across these three single-cell glioma datasets, we found that LAG3 was predominantly expressed on T cells and myeloids, with especially high expression on T cells. This was in good agreement with the patterns we observed in the bulk tumor RNA profiling analysis (Fig.\u0026nbsp;4B, D). The single - cell data were obtained from EGAS00001004656(https://ega-archive.org/studies/EGAS00001004656), GSE89567(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89567), and GSE131928(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131928).\u003c/p\u003e\n\u003cp\u003eThe expression characteristics of LAG3 were similar to that of PD-1 (PDCD1)\u003csup\u003e5\u003c/sup\u003e, while ITGAM (CD11b) was mainly expressed in myeloids. CD274 (PD-L1) was widely expressed, but predominantly in tumor cells (Fig.\u0026nbsp;5). These findings suggest that both myeloids and T cells simultaneously express immune checkpoint receptors such as LAG3 and PD-1, which may result in the exhaustion of their cytotoxic functions within the glioma microenvironment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. The expression of different immune checkpoints on T cells is mutually exclusive.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the relationship between LAG3 and PD-1 (PDCD1) expression in T cells, we extracted the T cell populations from the single-cell datasets and performed a co-expression analysis of these two classical immune checkpoint receptors across the three datasets (Fig.\u0026nbsp;6A-C). As shown in Fig.\u0026nbsp;6, except for a small number of cells that co-expressed the two markers (yellow-colored cells), LAG3 and PD-1 were largely expressed in a mutually exclusive manner, with most T cells expressing only one of the two immune checkpoint receptors. This suggested that LAG3 and PD-1 were less likely to be simultaneously expressed on the same individual T cells.\u003c/p\u003e\n\u003cp\u003eTo further validate this finding, we conducted a more in-depth analysis enrolling additional immune checkpoint receptors (Fig.\u0026nbsp;7), including LAG3, PDCD1, HAVCR2, CTLA4, KLRG1, CD244, and BTLA. From the expression heatmap of T cells (Fig.\u0026nbsp;7A-C), it was evident that the classical immune checkpoint receptors expressed on these T cells were mutually exclusive in most circumstances. With the single cell expression data of these classical immune checkpoints, we performed Spearman correlation analysis for one another. The correlation analysis results were presented with circus plots, showing the mainly negative correlation patterns among the various immune checkpoint receptors (Fig.\u0026nbsp;7D-F).\u003c/p\u003e\n\u003cp\u003eThese results indicate that, within the glioma microenvironment, individual T cells tend to express only a subset of the available immune checkpoint receptors, rather than co-expressing multiple checkpoints simultaneously. This may have important implications for understanding T cell exhaustion and the potential for combination immunotherapy strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. The expression of LAG3 is significantly correlated with prognosis of glioma patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubsequently, we obtained the clinical survival data of the patients. It can be seen from the Kaplan-Meier curves that in the CGGA and TCGA datasets, patients with higher LAG3 expression in their tumors tended to suffer shorter overall survival, and there is no significant correlation with IDH mutation status (Fig.\u0026nbsp;8A-D). Multivariate Cox proportional hazards model also further confirmed the significance of LAG3 as independently prognosis makers (Tables\u0026nbsp;1 and 2).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMulti-variates Cox proportional hazards model in CGGA dataset\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ecoef\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eexp(coef)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elower0.95\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eupper0.95\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.005919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.454585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.108712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.114841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.546479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.681831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.5155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.11\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.615078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.540599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.581851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.789347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMulti-variates Cox proportional hazards model in TCGA dataset\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ecoef\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eexp(coef)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elower 0.95\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eupper 0.95\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.88445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.17043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough the brain has been recognized as an immune privileged organ, increasing studies have shown that gliomas often exhibit strong immunogenicity due to their intrinsic genetic, transcriptomic, and proteomic heterogeneity\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This leads the immune system to recognize glioma cells as antigenic targets and mount an immune response against them\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, the continuous progression of gliomas typically indicates a complete failure of the body's immune defense, ultimately allowing the tumors to escape immune surveillance\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe mechanisms underlying glioma immune evasion are complex, involving not only the tumor cells themselves, but critically, their dynamic interactions and crosstalk with various immune cell populations\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. For example, glioma cells can downregulate MHC expression, upregulate immune checkpoint receptors, and secrete immunosuppressive cytokines. Additionally, the tumor-associated macrophages are often polarized towards an M2 phenotype, which further promotes an immunosuppressive tumor microenvironment by secreting anti-inflammatory mediators and factors that support tumor growth\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Furthermore, the function of infiltrating T cells is often severely impaired, leading to a state of T cell exhaustion\u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUnder the combined effects of these multifaceted immune evasion mechanisms, gliomas are able to ultimately escape the cytotoxic effects of the body's immune system, enabling their uncontrolled malignant progress. Understanding these complex immune dynamics within the glioma microenvironment is crucial for developing more effective immunotherapeutic strategies.\u003c/p\u003e\u003cp\u003eWhile progress has been made in targeting immune checkpoint pathways, especially in tumors such as melanoma\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, kidney cancer\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, and lung cancer\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, little advancement has been achieved in the glioma setting. Treatment regimens focused on classical immune checkpoint inhibitors targeting the PD-1/PD-L1 axis have not yielded satisfactory results in glioma\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. This warrants additional efforts to investigate other immune checkpoint receptors and explore novel targeting treatment options for this devastating disease.\u003c/p\u003e\u003cp\u003eIn this context, we performed an integrated clinical and molecular analysis of the immune checkpoint receptor LAG3 in glioma. We obtained glioma RNA-sequencing data from the CGGA and TCGA datasets, as well as published single-cell datasets. Through these comprehensive datasets, we found that LAG3 expression, similar to PD-1/PD-L1, increased with the degree of glioma malignancy. However, unlike PD-1/PD-L1, LAG3 expression levels were not stably associated with IDH mutation status, suggesting that LAG3 expression was independent of IDH mutation. This indicates that the tumor microenvironment, with or without IDH1 mutation, did not significantly affect LAG3 expression. Intriguingly, the biological role of LAG3 appears to differ between IDH-mutant and IDH wild-type gliomas. In IDH wild-type tumors, LAG3 expression was often associated with tumor cell proliferation, while in IDH-mutant gliomas, the primary role of LAG3 was related to immune suppression. These findings suggest that LAG3 may play distinct biological roles in different glioma molecular subgroups and their corresponding microenvironments.\u003c/p\u003e\u003cp\u003eSingle-cell data analysis revealed that LAG3 was almost exclusively expressed on the surface of T cells, in contrast to other immune checkpoint receptors such as PD-1, TIM-3, and CTLA4. Moreover, the expression of these classical immune checkpoint receptors on T cells was often mutually exclusive. This phenomenon suggests that T cells within the glioma microenvironment typically express a limited subset of immune checkpoint receptors, rather than co-expressing multiple checkpoints simultaneously. This may represent a mechanism by which T cells achieve an exhausted state. To our knowledge, this is the first description of the mutually exclusive expression pattern of immune checkpoint receptors on T cells in the context of malignancy.\u003c/p\u003e\u003cp\u003eThis unique expression pattern of immune checkpoints on glioma-infiltrating T cells has important implications. It cautions us that when treating glioma patients with immune checkpoint inhibitors, we need to be particularly aware of the potential for multiple checkpoint receptor expression on T cells. This property also provides a strong theoretical basis for the concomitant use of various immune checkpoint inhibitors as a combination immunotherapy approach for glioma patients.\u003c/p\u003e\u003cp\u003eConsistent with the role of most immune checkpoint receptors, higher expression levels of LAG3 were associated with worse patient survival, and this prognostic value was independent of IDH mutation status and other clinical factors. Collectively, these findings suggest that LAG3 may hold significant promise as a target for the development of novel immune checkpoint inhibitor therapies for glioma\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThrough the integrated analysis of the clinical characteristics and molecular data of LAG3, it is revealed that LAG3 is an important immune checkpoint in glioma, participates in the immune evasion of glioma, and is an independent unfavorable prognostic factor for glioma patients. It can be used as a promising candidate target for immune checkpoint inhibitors other than PD-1/PD-L1.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study has several limitations. First, the dataset size is relatively limited, which may restrict the robustness of the statistical analyses, particularly for rare outcome events. Second, we only used data from public database and did not perform external validation with independent datasets, which may affect the generalizability of our results to other populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003ePublish Declaration\u003c/h2\u003e\u003cp\u003eWe, the corresponding author and all co-authors of the manuscript titled \u0026ldquo;Integrative analysis identified the key role of LAG3 in T cell exhaustion in glioma\u0026rdquo;(submitted to Discover Oncology), hereby declare the following regarding the publication of this manuscript:\u003c/p\u003e\u003cp\u003eThe content is original, has not been published previously, and is not under consideration by any other journal. There is no duplicate publication issue .\u003c/p\u003e\u003cp\u003eIf accepted, we will sign the copyright agreement as required by the journal, acknowledging that authors retain copyright, Springer Nature holds the right to publish, and the manuscript will be subject to the Creative Commons open access license.\u003c/p\u003e\u003cp\u003eWe understand the journal\u0026rsquo;s open access requirements, will pay the Article Processing Charge (APC) as required, and agree that the manuscript will be freely accessible upon publication.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eDisclosure\u003c/h2\u003e\u003cp\u003eThis work was supported by National Natural Science Foundation of China (81902528、81972816) and Beijing Hospital Authority Projects (PX2021025). The authors declare no interests. The authors used no AI tools during the preparation of this work.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConsent to participate\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003cp\u003e\u003cb\u003eand accordance\u003c/b\u003e\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: Zheng Wang, Haitao Fu, Zhong Zhang, Liyun Zhong, Wei Yan;Data Curation: Zheng Wang, Haitao Fu, Yuwei Li, Zewei Liu, Wenhua Fan, Yuqing Liu, Chuanbao Zhang, Jingshan Liang;Methodology: all the authors;Supervision: all the authors;Writing \u0026ndash; original draft: all the authors;Writing \u0026ndash; review \u0026amp;amp; editing: all the authors.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank all the researchers for sharing such valuable RNA-seq profiling data and single cell sequencing data. Moreover, we appreciate the R software and all the packages used in the analysis procedure and thank the authors for their selflessness.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eTCGA bulk tumor RNAseq data was obtained from cBioportal(https://www.cbioportal.org/), CGGA bulk tumor RNAseq data was obtained from CGGA website (Chinese\u0026nbsp;Glioma\u0026nbsp;Genome\u0026nbsp;Atlas [https://www.cgga.org.cn](https:/www.cgga.org.cn) ).The single - cell data were obtained from EGAS00001004656(https://ega-archive.org/studies/EGAS00001004656), GSE89567(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE89567), and GSE131928(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131928).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCamisaschi C, et al. LAG-3 expression defines a subset of CD4(+)CD25(high)Foxp3(+) regulatory T cells that are expanded at tumor sites. 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Int J Immunopathol Pharmacol. 2021;35:20587384211056505. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/20587384211056505\u003c/span\u003e\u003cspan address=\"10.1177/20587384211056505\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"glioma, immune check point, LAG3, immunotherapy, immune evasion","lastPublishedDoi":"10.21203/rs.3.rs-7553633/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7553633/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLymphocyte-activation gene 3 (LAG3) is an immune checkpoint expressed on the surface of T cells. We conducted a comprehensive molecular characterization and clinical analysis of LAG3 expression in gliomas. Across the CGGA and TCGA glioma datasets, we observed that LAG3 expression increased with higher glioma grade. Further investigation into the molecular subtypes revealed that LAG3 exhibited the highest expression in the Mesenchymal subtype, similar to other immune checkpoints. CIBERSORT analysis showed a significant positive correlation between LAG3 expression and macrophages, suggesting LAG3's involvement in shaping the immune microenvironment. Single-cell data indicated that LAG3 expression appeared to be exclusive to other immune checkpoints, implying multiple mechanisms of T cell exhaustion. Importantly, clinical data analysis demonstrated that elevated LAG3 expression is an independent poor prognostic factor for patient survival. These findings suggest that LAG3 may play a crucial role in regulating the immune microenvironment in gliomas and contribute to T cell exhaustion, alongside other immune checkpoints such as the PD-L1/PD-1 pathway. As such, LAG3 represents a potential therapeutic target for glioma immunotherapy.\u003c/p\u003e","manuscriptTitle":"Integrative analysis identified the key role of LAG3 in T cell exhaustion in glioma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 06:29:01","doi":"10.21203/rs.3.rs-7553633/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-11T09:13:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-06T08:08:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272132071907739460027588492845430348635","date":"2025-12-05T02:22:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-11T02:08:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20869304965740042126115011543934433747","date":"2025-11-10T09:52:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115414671740037329150846888535872327982","date":"2025-11-04T13:39:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-04T13:09:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-27T07:29:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-15T11:59:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-12T06:56:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-10-12T06:52:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e2c6b582-57cd-475e-ae33-869fa7d8e262","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T08:09:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 06:29:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7553633","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7553633","identity":"rs-7553633","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}