Immune and clinical features of CD96 expression in glioma by large-scale analysis

doi:10.21203/rs.2.23546/v1

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Immune and clinical features of CD96 expression in glioma by large-scale analysis

https://doi.org/10.21203/rs.2.23546/v1

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Background: Immune checkpoints target regulatory pathways in T cells which enhance antitumor immune responses and elicit durable clinical responses . As a novel immune checkpoint, CD96 is an attractive key target for cancer immunotherapy. However, there is no integrative investigation of CD96 in glioma. Our study explored the relationship between CD96 expression and clinical prognosis in glioma.

Methods: A total of 1,024 RNA and clinical data were enrolled in this study, including 325 samples from the Chinese Glioma Genome Atlas (CGGA) database and 699 samples from The Cancer Genome Atlas (TCGA) dataset. R language was used to perform statistical analysis and draw figures.

Results: CD96 had a consistently positive relationship with glioblastoma and highly enriched in IDH-wildtype and mesenchymal subtype glioma. GO enrichment and GSVA analyses suggested that CD96 was more involved in immune functions, especially related to T cell-mediated immune response in glioma. Subsequent immune infiltration analysis manifes ted that CD96 was positively correlated with infiltrating levels of CD4+ T and CD8+ T cells, macrophages , neutrophils, and DCs in GBM and LGG. Additionally, CD96 was tightly associated with other immune checkpoints including PD-1 , CTLA-4 , TIGIT , and TIM-3 . Univariate and multivariate Cox analysis demonstrated that CD96 acts as an independent indicator of poor prognosis in glioma.

Conclusion: CD96 expression was increased in malignant phenotype and negatively associated with overall survival (OS) in glioma. CD96 also showed a positive correlation with other immune checkpoints, immune response, and inflammatory activity. Our findings indicate that CD96 is a promising clinical target for further immunotherapeutic in glioma patients.

Neurobiology of Disease

CD96

glioma

immune checkpoint

prognosis

immunotherapy

As the most prevalent and devastating primary intracranial tumor, glioma, especially glioblastoma multiforme (GBM, WHO grade IV) represents heterogeneity and extensive invasion, characterized by high recurrence and fatality rate [1–3]. Therefore, multiple attempts have been made to prolong life expectancies of glioma patients comprising developing effective therapy, appropriate biomarkers, and molecular targeted drugs. Numerous conventional treatment methods of central nervous system (CNS) tumors have emerged for decades, such as neurosurgical resection, radiotherapy, and chemotherapy[4, 5]. Among them, immunotherapy is reckoned an encouraging treatment resulted from evoking an anti-tumor immune response to restrain immune evasion of tumor[6]. In melanoma and non-small-cell lung cancer, immune checkpoint inhibitors like target programmed cell death protein 1 (PD-1) /programmed death ligand 1 (PD-L1), and cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) have been exploited and applied to clinical successfully [7–9]. Whereas many glioma patients are refractory to current immunotherapy which arouses our interest in identifying additional immune checkpoints to enhance the therapeutic efficacy of glioma [8, 10].

As a novel immune checkpoint receptor target, CD96 has recently entered the limelight to current cancer immunotherapies shown to inhibit natural killer (NK) cells [11]. Compelling evidence confirmed that blocking CD96 containment primary tumor growth in murine model systems in a CD8 + T cell–dependent manner [12, 13]. Of note, the anti-tumor activity of anti-CD96 therapy improved the efficiency in dual-combination with blockade of other immune checkpoints, like PD-1, PD-L1, TIGIT, and CTLA-4 [12]. Besides, CD96 represents several unique features that exhibit profound beneficial effects in the coming age of human cancer therapy[11, 14].

To take a systematic examination of CD96 in glioma, we gathered RNA-seq and clinical data of 325 glioma samples dataset from the Chinese Glioma Genome Atlas (CGGA) project. Simultaneously, another data set contained 699 samples from the Cancer Genome Atlas (TCGA) cohort to further corroborate the findings. Overall, our comprehensive study of molecular and clinicopathological features of CD96 through 1,024 samples will provide a better understanding of CD96 in glioma and pave the way for developing CD96-targeted cancer immunotherapies.

Data collection from CGGA and TCGA projects

The CD96 transcriptional and clinical data of 325 glioma samples were downloaded from the CGGA cohort (http://cgga.org.cn/) ranging from WHO grade II to IV. Moreover, 699 glioma samples of all grades were obtained from TCGA mRNA-seq dataset (https://tcga-data.nci.nih.gov/repository) including RNA-seq data and clinicopathological information.

Gene Ontology (GO) and Gene Set Variation Analysis (GSVA) Analysis

After Pearson correlation analysis, gene ontology (GO) analysis of the most correlated genes was constructed by R package ‘clusterProfiler’ [15]. GSVA analysis was applied using standard settings, as implemented in the ‘GSVA’ R package [16]. Additionally, inflammatory-related metagenes were described previously[9].

TIMER Database Analysis

Tumor Immune Estimation Resource (TIMER) serves a comprehensive resource to analyze immune infiltrates among 10,897 samples across 32 cancer types[17]. The correlations of CD96 expression with the abundance of immune infiltrates, including B cells, CD4 + T cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells were detected in GBM and LGG patients on TIMER online tools (https://cistrome.shinyapps.io/timer).

Statistical computations

Statistical analysis and figures drawing were conducted using the R language, version 3.6.1 (http://www.r-project.org). Kaplan–Meier plots and Cox proportional hazard model analysis were generated using R packages ‘survminer’ and ‘survival’ [18, 19]. Receiver operating characteristic (ROC) curves were calculated by R package ‘pROC’ [20]. Area under the curve (AUC) values were depicted from the ROC curves. Corrgram and corrplot plots were drawn using the R packages ‘corrgram’, and ‘corrplot’, separately [21, 22]. All statistical tests were two-sided and p-value < 0.05 was regarded as statistical difference.

CD96 was enriched in glioblastoma, IDH-wildtype, and mesenchymal glioma

To clarify differences of CD96 expression pattern in four grades of glioma malignancy, the mRNA level of CD96 was examined in CGGA and TCGA databases separately. The results revealed that glioblastoma (WHO IV) showed a higher CD96 expression in CGGA (Fig. 1A) and TCGA (Fig. 1B) cohorts than WHO grade II and grade III glioma. Compared to other pathological subsets (namely oligodendroglioma, oligoastrocytoma, and astrocytoma), CD96 expression was up-regulated relatively in glioblastoma (Figs. 1C and 1D). Isocitrate dehydrogenase (IDH) mutation distributes in almost 40% glioma which has an outsize impact on glioma development and progression[23]. To this end, the expression profile of CD96 in different IDH states was also explored. We found that CD96 was consistently enriched in IDH-wildtype (IDH^WT) in CGGA as well as in TCGA database (Figs. 1E and 1F). This result suggested that CD96 expression was more prevalent without IDH mutation (IDH^WT) than with IDH mutation (IDH^MUT) in glioma. In order to investigate the expression pattern of CD96, the distribution of CD96 in different molecular subtypes was analyzed. Compared with the other three subtypes (classical, neural, and proneural), CD96 was highly enriched in mesenchymal subtype in both CGGA and TCGA cohorts (Figs. 2A and 2B). To further validate our findings, we carried out ROC curves analysis of CD96 expression and mesenchymal subtype in all grade glioma. Intriguingly, we observed that area under the curve (AUC) was up to 78.6.% and 92.8% in CGGA and TCGA datasets separately (Figs. 2C and 2D). These findings indicated that CD96 acted as a potential biomarker for mesenchymal subtype glioma.

CD96 was significantly associated with immune functions in glioma

Considering CD96 expression was closely tied to malignancy, we inferred that CD96 became an integral part of glioma progression and then performed GO analysis to unfold its role. 54 and 176 genes positively correlated with CD96 were listed by Pearson correlation analysis (Pearson |R| > 0.6) in CGGA and TCGA datasets. According to GO enrichment analysis, we found that genes most relevant to CD96 were more involved in T cell activation and regulation of lymphocyte proliferation in CGGA and TCGA databases, respectively (Figs. 3A and B). To further elucidate the immune function of CD96 in glioma, 1,540 genes from the AmiGO 2 website (http://amigo. geneontology.org/amigo) were reported to be associated with the immune response and we selected 362 and 354 genes most relevant to CD96 (Pearson |R|> 0.3) in CCGA and TCGA cohorts (Table S1) for heatmap analysis[24]. Thereinto, 357 genes were strongly positively correlated with CD96 expression, while 5 genes had a significantly negative relationship with CD96 in CGGA dataset. While 347 and 7 genes displayed directly and inversely proportional in TCGA dataset (Fig. 4). To sum up, CD96 was directly correlated with most immune responses and negatively correlated with few immune responses in glioma.

The correlation between CD96 and T cell mediated immunity in glioma

To fully understand the relationship between CD96 and T cell immune in glioma, we performed gene set variation analysis (GSVA) to assess differential activities of pathways between sets of genes. As delineated in Figs. 5A and 5B, these relationships were similar in both CGGA and TCGA databases. Specifically, CD96 showed a positive correlation with T-helper 1/2 type immune response (GO:0042088 and GO:0042092), T-helper 1/2 cell cytokine production (GO:2000556 and GO:2000553), and natural killer cell mediated cytotoxicity directed against tumor cell target (GO:0002860). Conversely, CD96 was correlated negatively with T cell mediated immune response to tumor cell (GO:0002842) and T cell mediated cytotoxicity directed against tumor cell target (GO:0002852). This result re-validated that the special immune function of CD96 is to act an inhibitory role in T cell immune to tumor cells in glioma.

Correlation Between CD96 And Other Immune Checkpoints

As therapeutic targets, a growing number of immune checkpoints have been emerged and examined in clinical trials or clinical situations [25, 26]. Thereby, we analyzed the relationship between CD96 and other immune checkpoints, such as PD-1, CTLA-4, TIGIT, TIM-3, NR2F6, and GITR. Pearson correlation analysis revealed that CD96 was tightly associated with PD-1, CTLA-4, TIGIT, and TIM-3. ໿Co-expression of PD-1 with CD96 was consistent with the previous research [12]. These results were validated in CGGA and TCGA datasets mutually (Figs. 5C and 5D), implying the possible synergistic effects of CD96 with these checkpoint members. Accordingly, we postulated CD96 may contribute significantly to the inflammatory response in glioma and used the previous method to test[9]. As shown in Figure S1, CD96 was positively associated with HCK, MHC-I, MHC-II, STAT1, STAT2, and FCGR2A, especially with LCK. This finding additionally evidenced the vital immune function of CD96 in glioma.

Correlation between CD96 and immune infiltration level in GBM and LGG

Tumor-infiltrating lymphocytes are an independent predictor of sentinel lymph node status and survival in cancers. Hence, we investigated whether CD96 expression was connected with immune infiltration levels in GBM (glioblastoma multiforme) and LGG (Low Grade Glioma) by TIMER website tools. Our findings manifested that CD96 expression related positively with infiltrating levels of dendritic cell (r = 0.504), neutrophil (r = 0.487), macrophage (r = 0.431), and CD8 + T cell (r = 0.406) in LGG. Simultaneously, CD96 had a marginal positively association with B cell (r = 0.329) and CD4 + T cell (r = 0.37) infiltration level in LGG. On the other hand, CD96 expression has no significant relationships with tumor purity (r = -0.117) infiltrating levels of B cell (r = 0.101), CD8 + T cell (r = -0.157), CD4 + T cell (r = -0.1), and so on in GBM (Fig. 6). These differences implied that CD96 would make more contribution to immune infiltration in LGG than GBM, especially in dendritic cells.

CD96 predicted worse survival in glioma

Owing to the high relevance between CD96 and immune suppressor in glioma, the prognostic impact of CD96 was verified via Kaplan–Meier method. Overall survival analysis in glioma and GBM patients demonstrated that high expression of CD96 predicted relatively poor survival in CGGA and in TCGA cohorts (Fig. 7). Eventually, we evaluated the independence of the clinicopathological significance of CD96 in glioma by univariate and multivariate Cox regression analyses. The results indicated that CD96, age, gender, WHO grade, IDH mutation, and h1p19q codeletion status were closely associated with overall survival and revealed CD96 is an independent prognosticator for glioma patients (Table S2).

Recent studies about tumor immunotherapy continue to soar and bring hope to glioma patients. Among those immunotherapeutic strategies, immune checkpoint blockade offers remarkable benefits to the therapies of various tumor types by increasing anti-tumor immunity[27]. However, researches on the field of neural tumor immunotherapy mainly focus on CTLA-4 and PD-1/PD-L1 blockade currently. Nonetheless, potential immune-related adverse events hindered the wide clinical application of immunotherapy[27]. The identification of alternative checkpoint targets may facilitate the solution of this predicament and furtherance therapeutic benefits for cancer treatment.

Increasing researches demonstrated that CD96 ໿emerges as a potent modulator of anti-tumor immune responses [11]. CD96 has been shown to regulate negatively NK cell-mediated immune surveillance and intervene multidimensional adhesion, inhibition, and activation of participating cells[11, 28–30]. ໿The special effect of CD96 has also been reported in some tumors. Hepatocellular carcinoma (HCC) patients with the high expression level of CD96 within tumor are strongly correlated with deteriorating disease situations, shorter disease-free survival, and overall survival times[31]. Targeting host CD96 appears as an innovative strategy for clinical application combination with current immunotherapies. Herein, we probed the biologic functions of CD96 in glioma through the large scale and in-depth analyses. CD96 expression was markedly enriched in higher malignant pathological type gliomas. Moreover, high expression of CD96 was observed in the malignant molecules phenotype, including IDH wildtype and mesenchymal subtype. Collectively, CD96 exhibited a malignant biological property in glioma. Meanwhile, through the analysis of the relationship between CD96 and biological processes, we noted that CD96 had a positive association with immune response and inflammatory activities.

A range of immunotherapy targeting checkpoint inhibitors and blocking monoclonal antibodies (mAbs) have been widely adopted ໿and several of them are ongoing in glioblastoma [8, 32]. Compared to monotherapy treatments, combination approaches were reported to be more effective and associated with substantially longer progression-free survival[33, 34]. In our research, CD96 showed a high concordance with immune checkpoints, PD-1, CTLA-4, TIGIT, TIM-3, NR2F6, and GITR, indicating the potential synergistic effects of these markers. We inferred that CD96 collaborating with other checkpoint members especially PD-1 may increase effect glioma immunotherapy. Indeed, the previous studies have demonstrated that concurrent blockade of CD96 and PD-1 increased anti-tumor immunity over targeting PD-1 alone without inducing serious immune-related toxicities potentially[35, 36]. This provided support to our research. We also discovered that higher CD96 expression predicted worse survival rates in glioma and GBM patients. This significant prognostic signature implied that CD96 blockade may significantly improve the prognosis of glioma patients, especially GBM patients.

To sum up, we initially explored the genetic and clinical characteristics of CD96 based on CGGA and TCGA datasets. Our results highlighted CD96 may be a promising biomarker and therapeutic target for glioma which present favorable application prospects.

Additional file 1 Figure S1. The relationship between CD96 and inflammatory activities in glioma.

Additional file 2 Table S1. The most relevant immune genes to CD96 in CGGA and TCGA datasets.

Additional file 3 Table S2. Univariate and multivariate analyses of clinical prognostic parameters in CGGA and TCGA datasets.

AUC: Area under the curve; CGGA: Chinese Glioma Genome Atlas; GBM: Glioblastomas; GO: Gene ontology; GSVA: The gene set variation analysis; IDH: Isocitrate dehydrogenase; OS: Overall survival; PD-L1: Programmed death-ligand 1; TCGA: The Cancer Genome Atlas dataset; TIM3: T cell immunoglobulin mucin-3; WHO: World Health Organization

Consent for publication

All authors have reviewed the manuscript and consented for publication.

Acknowledgments

We thank Dr. Tao Jiang and Dr. Zhao Zheng from Beijing Neurosurgical Institute for assistance with data collection in CGGA and helpful suggestions.

Funding

This research was financially supported by Natural Science Foundation of China (81660421) and Young talents Found of Zunyi medical University (18zy-005).

Availability of data and materials

All the data in this study were obtained from The Cancer Genome Atlas dataset (TCGA; https://tcga-data.nci.nih.gov/repository) and Chinese Glioma Genome Atlas dataset (CGGA; http://www.cgga.org.cn/).

Authors’ contributions

Shengtao Y and Fang C conceptualized and designed this study. Hua Z performed the data collection and analysis. Qiang Z wrote the manuscript. Yinchun F, Qian L, and Jiancheng S participated in drawing diagrams and revision. All authors gave final approval of the manuscript.

Ethics approval and consent to participate

All the procedures in this study were approved by the ethics committees of all hospitals, and written informed consent was obtained from all patients.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author details

¹Department of Cerebrovascular Disease, the First Affiliated Hospital of Zunyi Medical University, No.149, Dalian Road, Zunyi, Guizhou, 563000, China

² College of Life Sciences, Wuhan University, Wuhan, Hubei, 430072, China

Van Meir EG, Hadjipanayis CG, Norden AD, Shu HK, Wen PY, Olson JJ. Exciting New Advances in Neuro-Oncology: The Avenue to a Cure for Malignant Glioma. CA Cancer J Clin. 2010;60:166–93.
Jiang T, Mao Y, Ma W, Mao Q, You Y, Yang X, et al. CGCG clinical practice guidelines for the management of adult diffuse gliomas. Cancer Lett. 2016;375:263–73.
Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 2016. p. 131:803-820.
Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJ, Janzer RC, et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol. 2009;
Walker D, Bendel A, Stiller C, Indelicato D, Smith S, Murray M, et al. Central Nervous System Tumors. Pediatr Oncol. 2017.
Louveau A, Smirnov I, Keyes TJ, Eccles JD, Rouhani SJ, Peske JD, et al. Structural and functional features of central nervous system lymphatic vessels. Nature. 2015;
Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med. 2012;
Huang J, Liu F, Liu Z, Tang H, Wu H, Gong Q, et al. Immune checkpoint in glioblastoma: Promising and challenging. Front. Pharmacol. 2017. p. 8:242.
Wang Z, Zhang C, Liu X, Wang Z, Sun L, Li G, et al. Molecular and clinical characterization of PD-L1 expression at transcriptional level via 976 samples of brain glioma. Oncoimmunology. 2016;
Reardon DA, Freeman G, Wu C, Chiocca EA, Wucherpfennig KW, Wen PY, et al. Immunotherapy advances for glioblastoma. Neuro Oncol. 2014;16:1441–58.
Georgiev H, Ravens I, Papadogianni G, Bernhardt G. Coming of age: CD96 emerges as modulator of immune responses. Front Immunol. 2018;9:1–10.
Mittal D, Lepletier A, Madore J, Aguilera AR, Stannard K, Blake SJ, et al. CD96 Is an Immune Checkpoint That Regulates CD8þ T-cell Antitumor Function. Cancer Immunol Res. 2019;7:559–71.
Dougall WC, Kurtulus S, Smyth MJ, Anderson AC. TIGIT and CD96: new checkpoint receptor targets for cancer immunotherapy. Immunol Rev. 2017;276:112–20.
Deuss FA, Watson GM, Fu Z, Rossjohn J, Berry R. Structural Basis for CD96 Immune Receptor Recognition of Nectin-like Protein-5, CD155. Structure [Internet]. Elsevier Ltd.; 2019;27:219-228.e3. Available from: https://doi.org/10.1016/j.str.2018.10.023
Yu G, Wang LG, Han Y, He QY. ClusterProfiler: An R package for comparing biological themes among gene clusters. Omi A J Integr Biol. 2012;
Hänzelmann S, Castelo R, Guinney J. GSVA: Gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 2013;
Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, et al. TIMER: A web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;
Kassambara A, Kosinski M, Biecek P, Fabian S. survminer: Drawing Survival Curves using “ggplot2” (R package). version 0.4.3. 2018.
Therneau TM, T. Lumley. Package ‘ survival .’ R Top Doc. 2015;
Robin AX, Turck N, Hainard A, Lisacek F, Sanchez J, Müller M, et al. Package ‘ pROC .’ 2012-09-10 09:34:56. 2013;
Friendly M. Corrgrams: Exploratory displays for correlatigon matrices. Am Stat. 2002;
Wei T. Package “corrplot” for R: Visualization of a Correlation Matrix. CRAN. 2017;
Branzoli F, Di Stefano AL, Capelle L, Ottolenghi C, Valabrègue R, Deelchand DK, et al. Highly specific determination of IDH status using edited in vivo magnetic resonance spectroscopy. Neuro Oncol. 2018;
Liu F, Huang J, Xiong Y, Li S, Liu Z. Large-scale analysis reveals the specific clinical and immune features of CD155 in glioma. Aging (Albany NY). 2019;
Ribas A, Wolchok JD. Cancer immunotherapy using checkpoint blockade. Science (80-. ). 2018.
Feng E, Liang T, Wang X, Du J, Tang K, Wang X, et al. Correlation of alteration of HLA-F expression and clinical characterization in 593 brain glioma samples. J Neuroinflammation. Journal of Neuroinflammation; 2019;16:1–8.
Postow MA, Sidlow R, Hellmann MD. Immune-related adverse events associated with immune checkpoint blockade. N Engl J Med. 2018;
Fuchs A, Cella M, Giurisato E, Shaw AS, Colonna M. Cutting Edge: CD96 (Tactile) Promotes NK Cell-Target Cell Adhesion by Interacting with the Poliovirus Receptor (CD155). J Immunol. 2004;
Roman Aguilera A, Lutzky VP, Mittal D, Li XY, Stannard K, Takeda K, et al. CD96 targeted antibodies need not block CD96-CD155 interactions to promote NK cell anti-metastatic activity. Oncoimmunology [Internet]. Taylor & Francis; 2018;7:1–10. Available from: https://doi.org/10.1080/2162402X.2018.1424677
Chan CJ, Martinet L, Gilfillan S, Souza-Fonseca-Guimaraes F, Chow MT, Town L, et al. The receptors CD96 and CD226 oppose each other in the regulation of natural killer cell functions. Nat Immunol. 2014;
Sun H, Huang Q, Huang M, Wen H, Lin R, Zheng M, et al. Human CD96 Correlates to Natural Killer Cell Exhaustion and Predicts the Prognosis of Human Hepatocellular Carcinoma. Hepatology. 2019.
Bouffet E, Larouche V, Campbell BB, Merico D, De Borja R, Aronson M, et al. Immune checkpoint inhibition for hypermutant glioblastoma multiforme resulting from germline biallelic mismatch repair deficiency. J Clin Oncol. 2016;
Larkin J, Chiarion-Sileni V, Gonzalez R, Grob JJ, Cowey CL, Lao CD, et al. Combined nivolumab and ipilimumab or monotherapy in untreated Melanoma. N Engl J Med. 2015;
Postow MA, Chesney J, Pavlick AC, Robert C, Grossmann K, McDermott D, et al. Nivolumab and ipilimumab versus ipilimumab in untreated melanoma. N Engl J Med. 2015;
Blake SJ, Stannard K, Liu J, Allen S, Yong MCR, Mittal D, et al. Suppression of metastases using a new lymphocyte checkpoint target for cancer immunotherapy. Cancer Discov. 2016;6:446–59.
Harjunpää H, Blake SJ, Ahern E, Allen S, Liu J, Yan J, et al. Deficiency of host CD96 and PD-1 or TIGIT enhances tumor immunity without significantly compromising immune homeostasis. Oncoimmunology [Internet]. Taylor & Francis; 2018;7:1–11. Available from: https://doi.org/10.1080/2162402X.2018.1445949

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Immune and clinical features of CD96 expression in glioma by large-scale analysis

Status:

Version 1

Abstract

Figures

Background

Methods

Data collection from CGGA and TCGA projects

Gene Ontology (GO) and Gene Set Variation Analysis (GSVA) Analysis

TIMER Database Analysis

Statistical computations

Results

CD96 was enriched in glioblastoma, IDH-wildtype, and mesenchymal glioma

CD96 was significantly associated with immune functions in glioma

The correlation between CD96 and T cell mediated immunity in glioma

Correlation Between CD96 And Other Immune Checkpoints

Correlation between CD96 and immune infiltration level in GBM and LGG

CD96 predicted worse survival in glioma

Discussion

Conclusions

Additional Files

Abbreviations

Declarations

References

Supplementary Files

Status:

Version 1