Pan-cancer Analysis for the Prognostic and Immunological Role of CD47: Interact with TNFRSF9 Inducing CD8+T Cell Exhaustion.

Pan-cancer Analysis for the Prognostic and Immunological Role of CD47: Interact with TNFRSF9 Inducing CD8+T Cell Exhaustion. | 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 Pan-cancer Analysis for the Prognostic and Immunological Role of CD47: Interact with TNFRSF9 Inducing CD8+T Cell Exhaustion. Hongxin Liang, Lintong Yao, Daipeng Xie, Duo Chen, Jinchi Dai, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3628207/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 May, 2024 Read the published version in Discover Oncology → Version 1 posted You are reading this latest preprint version Abstract Purpose The role of CD47 in the effectiveness of immunotherapy has been researched. An understanding of the impact of CD47 on the tumor immune microenvironment, particularly with regard to CD8 + T cells, remains inadequately clarified. Our research focuses on investigating the prognostic and immunological significance of CD47 to gain a deeper understanding of its potential applications in immunotherapy. Methods The examination of differential gene expression, prognosis, immunological infiltration, pathway enrichment, and correlation was conducted using various R packages, computational tools, datasets, and cohorts. The notion was validated by the use of single-cell sequencing. Results CD47 was expressed in nearly all cancer types, associated with poor prognosis in pan-cancer. The immunological research revealed that CD47 exhibited a stronger correlation with T-cell infiltration as opposed to T-cell rejection in cases of multiple cancers. The cytotoxic CD8 + T cell Top group had a poorer prognosis in the CD47-high group than the CD47-low group showing CD47 might impair CD8 + T cell function. Mechanism exploration found that CD47 differential genes in multiple cancers were enriched in the CD8 + T-cell exhausted pathway. Subsequent analysis of the CD8 TCR Downstream Pathway and correlation analysis of genes further demonstrated the significant involvement of TNFRSF9. Conclusion There is a strong correlation between CD47 and the exhaustion of CD8 + T cells, which in turn can facilitate immune evasion by cancer cells, ultimately resulting in a negative prognosis. Hence, the genes CD47 and T-cell exhaustion-linked genes, particularly TNFRSF9, exhibit potential as dual antigenic targets and offer valuable insights into the realm of immunotherapy. CD47 CD8 + T cells T-cell exhausted pan-cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction With the widespread acceptance of immunotherapies, the survival rates of cancer patients have significantly improved. Overexpression of various immune checkpoints is one of the significant ways of tumor immune evasion, which could be the predominant factor that limits the therapeutic effects of immune therapies. CD47, a star checkpoint of innate immunity, is being addressed as a hotspot mechanism. Exploring the interconnections between CD47 and tumor immune microenvironment (TIME), and drawing a more detailed picture of tumor immune evasion mode may be useful for the development of appropriate therapies and the discovery of immune targets. Those thus, would contribute to solutions to immune therapy resistance, and ultimately to the improved survival of patients with malignancies. CD47 protein is a transmembrane glycoprotein member of the immunoglobulin superfamily, which interacts with signal regulatory protein α (SIRPα). Moreover, it forms the CD47/SIRPα axis, inhibits the accumulation of myosin, and therefore completes the "Do not eat me" signal, avoids phagocytosis of macrophages [ 1 – 2 ], and inhibits innate immunity [ 3 – 5 ]. Plenty of tumor cell types were discovered to escape innate immunity through CD47 overexpression [ 4 ], correlating with poorer survival and response to standard therapies [ 6 – 8 ]. Drugs targeting the CD47/SIRPα axis have been a point of research, including monoclonal antibodies (McAb), bispecific antibodies, fusion proteins, combined chemotherapies, and immune therapies, such as CD47 McAb and fusion proteins containing SIRPα(TTI-621/2). They showed apparent clinical effectiveness and are in phase II or III clinical assessment [ 9 – 11 ]. Notably, the CD47/SIRPα axis also affects adaptive immunity. Research shows that blockade of the CD47/ SIRPα axis can enhance T-cell response directly or through the mediation of myeloid cells [ 12 – 13 ]. For example, a subset of CD8 + T cells would express SIRPα [ 14 ]; and blockading CD47/SIRPα can help solve resistance to CD8-mediated immunotherapy [ 15 – 16 ]. Furthermore, CD47 agonists can induce antigen presentation and cross-priming T-lymphocytes [ 17 – 18 ]. Indeed, in mouse models, the therapeutic activity of CD47 McAb is cross-primed by T-cells, and T-cell-shortage mice have received no therapeutic effect. In contrast, such a phenomenon was eliminated in wild-type (WT) mice [ 19 ]. It is reported that blocking CD47 through combined radiotherapy can directly CD8 + T cells to enhance antitumor immunity [ 20 ]. Hence, it is important to explore the relationship between the checkpoint of CD47 and antitumor T cell immune. An exciting target is tumor necrosis factor (TNF) receptor superfamily member TNFRSF9 (CD137, TNFRSF9). The expression of TNFRSF9 is selectively induced by an interaction between the TCR (the T cell receptor)/main histocompatibility complex (MHC) [ 21 ]. TNFRSF9 is considered the typical marker of Tumor-reactive T cell subsets of T-lymphocytes in TME but is not expressed on static T cells from peripheral blood [ 22 – 23 ]. DSP107, a fusion protein, binds both targets and has dual immune regulatory effects, driving innate and adaptive immune responses and thus exhibiting excellent antitumor effects [ 24 ]. Considering the complicated relationship between CD47 in tumors and adaptive immunity, the survey investigates whether there is an effect induced by CD47 on CD8 + T cells functions in TIME and, thus, the prognosis of patients with malignancies. The analysis is based on data from 33 tumor types and subtypes forming the TCGA database. It also includes data from single-cell sequencing, intended to provide approaches for tumor immune therapies. 2. Methods 2.1 Analysis tools and data collection XENA-TCGA GTEx TCGA ( https://portal.gdc.cancer.gov/ ) and GTEx handling were consolidated by the Toil process in UCSC XENA ( https://xenabrowser.net/datapages/ ). Data (V8.0) conversion: Transcripts per million reads format RNAseq data in TPM format and log2 conversion for analysis and comparison. GTEx, The Genotype-Tissue Expression ( https://www.gtexportal.org/home/ ). After log2 transformation, RNAseq data in TPM (transcripts per million reads) format was examined and contrasted. [ 25 ] UALCAN A comprehensive OMICS cancer data analysis web portal is located in Ualcan ( http://ualcan.path.uab.edu/ ). Transcript per million readings were used to standardize the expression level of CD47. [ 26 ]. TIMER2.0 TIMER2.0, The database for thorough examination of immune cells that infiltrate tumors is called TIMER2.0( https://cistrome.shinyapps.io/timer/ ) [ 27 ]. To assess the number of immune infiltrates, 10897 samples from 32 TCGA cancer types are included in the TIMER database. TIDE [ 28 ] ( http://tide.dfci.harvard.edu/ ) This computational methodology was created to assess if tumor immune escape from cancer sample gene expression profiles is possible. Each tumor sample's TIDE score can be used as a proxy biomarker to forecast the response to immune checkpoint inhibitors. Additionally, possible regulators of tumor immune escape and resistance to cancer immunotherapies are presented by the highly rated genes in TIDE profiles. [ 29 ]. STRING The protein interaction network database STRING database [ 30 ] (string-db.org) is built on data from public databases and published works. It compiles information from multiple public databases, such as Gene Ontology, KEGG, NCBI, and UniProt, to combine it and create a thorough database of protein interaction networks. TISCH To create its scRNA-seq atlas [ 31 ], TISCH gathered information from Array Express [ 32 ] and Gene Expression Omnibus (GEO) [ 33 ], encompassing 2045746 cells from healthy donors and 79 databases. Data sets were handled uniformly to provide clarity of the TME components at the annotated cluster and single-cell levels. STATISTIC P value less than 0.05 was statistically significant. Significance markers: NS, P ≥ 0.05; *, p < 0.05; * *, p < 0.01; * * *, p < 0.001. 2.2 Analysis of differential CD47 expression in average, tumor, stages, and protein levels. Differential CD47 expression levels between tumors and normal tissues adjacent to TCGA cancer types were analyzed using R (version 3.6.3) and R packages (mainly GGGlot2 [version 3.3.3]) from the XENA-TCGA GTEx resource. Furthermore, Protein levels between tumors and adjacent normal tissues were also investigated using the UALCAN interactive web resource. Survival curves were presented using predictive analysis. SurvMiner [version 0.4.9] and Survival package [version 3.2–10] were used (grouped by p-best). The type of prognosis was OS (Overall Survival), DSS (Disease-Specific Survival), and PFI (Progress Free Interval); we also obtained prognostic data from a Cell article [ 34 ]. 2.3 Analysis of tumor immune and immunosuppressive cell infiltration and comparative biomarker analysis. Using the TIMER2 server, we analyzed the correlation between tumor infiltration and CD47 expression, with four immunosuppressive cell types promoting T-cell rejection, MDSCs, CAF, M2-TAM, and Treg across 39 TCGA cancers. The Spearman partial rho value and p < 0.05 were used for correlation analysis. The results of the study used the algorithm with the best positive results. In addition, we used the GSVA R package [version 1.34.0] [ 35 ] to explore correlations between the expression of CD47 and the infiltration of 23 types of immune cells [ 36 ] in TCGA cancers. Then, the overall predictive power of CD47 was compared with standardized biomarkers of tumor immune responses in therapeutic response outcomes and OS. 2.4 Analysis of CD47 on Cytotoxic CD8 + T cell Infiltration Influenced Tumor Prognosis. We used the GSVA R package [version 1.34.0] [ 35 – 36 ] to explore the difference in Cytotoxic T-cell infiltration in the different CD47 expression situations in TCGA pan-cancer. The median method was used to divide patients into CD4- high group and CD47-low group. We also use the TIDE algorithm to assess the effects of CD47 on Cytotoxic CD8 + T cell.[ 29 ] 2.5 Analysis of Pathway We identify DEG between high and low-expression CD47 clusters using the DESeq2 R package [1.26.0 version] [ 37 ]. Division of patients into high and low CD47 groups by median method. These different genes were enriched in the pathway via the R package cluster profile [ 38 – 39 ] [3.14.3 version] (for SEEA analysis) [ 39 ]. [version 3.14.3] (for GSEA analysis) [ 39 ]. Reference gene set: H.all.v7.2.symbols.GMT [Hallmarks]. The species is Homo sapiens. Gene set databases came from MSigDB Collections [ 40 ]. It included the BIOCARTA subset of CP (browse 292 gene sets); KEGG subset of CP (browse 186 gene sets); PID subset of CP (browse 196 gene sets); REACTOME subset of CP (browse 1615 gene sets); WikiPathways subset of CP (browse 664 gene sets). Significance: It is generally considered that the conditions of False Discovery Rate (FDR) < 0.25 and p.adjust < 0.05. Visualization: GGploT2 [version 3.3.3]. Furthermore, CBNplot [ 41 ] was used to investigate molecular regulatory connections. in PID_CD8_TCR_DOWNSTREAM_PATHWAY, exhibiting a Bayesian network inference approach. STRING database was used to detect the PPI (Protein-Protein Interaction Networks), showing the interactions of CD47 protein and core protein of that pathway in biological systems. 2.6 Analysis of Co-expression. We used Software: R (version 3.6.3) to analyze the correlation of CD47 and T-cell exhaustion in TCGA pan-cancer. The data sets were level 3 HTSeq - FPKM RNAseq data format. Visualization of the results is provided by the R package: GGploT2 [version 3.3.3]. Besides, we used TISCH to determine whether CD47 was mainly expressed primarily on CD8 + Tex and whether CD47 has a relationship with the CD8 + Tex gene expressed primarily on CD8 + Tex. The results were presented in the stacked value bar chart (bars of superimposed proportions) to see the expression levels of CD47 and TNFRSF9 in different cohorts through software R (version 3.6.3) and GGPLOT2 [version 3.3.3] (visualization). 3. Results 3.1 Abnormal expression of CD47 in pan-cancer patients is associated with tumor stages and poor prognosis. We looked into CD47's carcinogenic potential using the XENA-TCGA GTEx pan-cancer database. When comparing nearly all cancer types to normal tissue, we discovered that CD47 gene expression was higher in the former. (ACC, BRCA, BLCA, CHOL, COAD, DLBC, ESCA, GBM, HNSC, KIRC, KIRP, LAML, LGG, LIHC, LUAD, OV, PAAD, PRAD, READ, SARC, SKCM, STAD, THCA, THYM, UCEC, UCS) (Fig. 2 a). Additionally, we delved deeper into the CD47 expression of paired samples within the XENA-TCGA database, yielding identical outcomes to the XENA-TCGA GTEx pan-cancer database across various cancer types including BRCA, CHOL, COAD, ESCA, HNSC, KIRC, LIHC, PAAD, STAD, THCA, and UCEC. (Supplementary Fig. 1a). Significantly, we noted an increase in CD47 protein expression in HNSC, PAAD, UCEC, RCC, and OV compared to the normal levels, as indicated by the UALCAN database (Fig. 2 b, Supplementary Fig. 1b). Moreover, in cancer, the expression of CD47 was elevated in advanced tumor stages. For example, patients with M1 stage lung squamous cell carcinoma malignancy expressed more CD47 than patients with M0 stage. The same trend's outcomes were observed in THCA, UCEC, PRAD, KIRC, KIRP, and LIHC. (Supplementary Fig. 1c). Subsequently, we discovered a correlation between excessive CD47 expression and reduced overall survival in ACC, BRCA, LIHC, and KICH; decreased PFI in ACC, LUSC, UVM, and decreased DSS in ACC, LUSC, LGG, and KICH. All of these findings point to CD47 could be an early biomarker for cancer detection, staging, and monitoring. (Fig. 2 c, Supplementary Fig. 1d) 3.2 CD47 is related to tumor immune evasion through infiltration by T lymphocyte cells. Due to its association with tumor immunity evasion, we assessed the associations between CD47 expression levels and the infiltration of MDSCs, CAFs, M2-TAMs, and Treg cells through six algorithms (QUANTISEQ, XCELL, CIBERSORT, CIBERSORT-ABS, TIDE, MCPCOUNTER). These types of immune cells could promote T-cell exclusion. Treg and CAF in BRCA-LumA, Treg and CAF in LICH, Treg, MDSC, and CAF in PRAD, and Treg and CAF in THYM were found to positively correlate with CD47 expression. (r<0.2, p<0.05, every cell type ≥ 2 algorithms positive, and at least two types of these cells are positive ) (Fig. 3 a) Subsequently, we employ the identical cognitive approach to identify the association with T cell infiltration and expression of CD47 through seven different algorithms (QUANTISEQ, XCELL, CIBERSORT, CIBERSORT-ABS, EPIC, MCPCOUNTER, TIMER). The results showed that CD47 expression was positively correlated with infiltration of CD8 + T cell in almost all cancer types. (BLCA, BRCA, BRCA-Basal, COAD, DLBC, ESCA, KIRC, KIRP, LIHC, LUSC, PAAD, PRAD, READ, SKCM, SKCM-Metastasis, STAD, TGCT, THCA, UVM) (r<0.2, p<0.05, at least two types of these cells are positive, and at least two types of calculation methods). Interestingly, the infiltration of CD8 + T cell effector memory is comparatively lower in the majority of cancer species compared to other types such as CD8 + T cell central memory and CD8 + T cell naive. As for CD4 + T cell, there were still a lot of CD4 + Tcells closely related to CD47, but it depends on the types of CD4 + T cell. The presence of CD47 in the majority of cancer types was observed to have a positive correlation with the infiltration of CD4 + T cell memory resting and CD4 + T cell Th2, in contrast to CD4 + T cell (non-regulatory) and CD4 + T cell Th1(Fig. 3 b). We further explored which types of T-cell infiltration in tumors were most associated with CD47 using another result. It is worth mentioning that the correlation between CD8 + T cells infiltration and CD47 is the highest in these cancer types (COAD, DLBC, ESCA, HNSC-HPV-, LIHC, LUAD, LUSC, PAAD, STAD) (Fig. 3 b). CD47 expression had strong positive correlations with T cell infiltration in various cancer types including BLCA, CHOL, COADREAD, DLBC, GBM, HNSC, KIRC, KIRP, LAML, LUSC, PAAD, PRAD, SKCM, TGCT, READ, STAD, UCS, and UVM. The T cell subtypes that displayed significant associations included T helper cells, CD8 + T cells, CD4 T cells, Cytotoxic cells, Th1, Th2, Th17, as well as Tgd, Tcm, Tem, and TFH. (Fig. 3 c, Supplementary Fig. 2). Furthermore, it suggested a strong association between the infiltration of CD8 + T cells and CD47 in numerous cancer types (DLBC, ESCA, LUSC, OV, SKCM, STAD, TGCT, THCA, UCS, UVM). (r<0.2, p<0.05) Then, we assessed CD47 biomarker relevance by comparing CD47 with standardized biomarkers based on its response outcomes to ICB sub-cohorts and OS predictive ability. Interestingly, we found that in 16 of the 25 ICB sub-cohorts, CD47 alone had an area greater than 0.5% under the AUC. CD47 was predicted to be more valuable than TMB, T. Clonality, B. Clonality, and MSI. Seven, nine, seven, and 13 ICB subgroups had more significant AUC values than 0.5. However, CD47 is lower than CD274, TIDE, IFNG, CD8, and Merck18. Based on these results, it is strongly indicated that CD47 plays a pivotal role in the immune microenvironment of tumors and exhibits a strong association with T-cell infiltration. (Fig. 3 d) 3.3 CD47 on CD8 + T cells infiltration had an impact on tumor prognosis. Drawing from our prior findings, it can be inferred that the presence of CD8 + T cells exhibited a strong correlation with CD47. Interestingly, we found that in the CD47 High group, the level of Cytotoxic CD8 + T cell was more frequently observed in BLCA, BRCA, CESC, COAD, COADREAD, ESAD, ESCA, GBM, HNSC, KIRC, LUSC, OV, SKCM, STAD, TGCT, THCA, UCS types compared to the CD47 low group based on TCGA database. (Fig. 4 a, Supplemental Fig. 3a) Then we further identified the same conclusion in Multiple immunotherapy cohorts (Nathanson2017_CTLA4-OS, Gide2019_PD1-OS, Gide2019_PD1 + CTLA4-OS, Miao2018_ICB-OS, Mariathasan2018_PDL1-OS, Riaz2017_PD1-OS, Li’_PD1-OS Zhao2019_PD1-OS, VanAllen2015_CTLA4-OS) (Supplemental Fig. 3b) Furthermore, it was observed that the Cytotoxic CD8 + T cell Top group had a poorer prognosis in CD47-high group than the CD47-low group (Fig. 4 b). Especially in these cohorts, high Cytotoxic CD8 + T cell infiltration did not suggest a better prognosis. In the same, this phenomenon was also shown in GSE13507@PRECOG Bladder, GSE10886@PRECOG Breast, Roepman Lung Cancer @PRECOG cohorts, GSE17536 Colorectal OS, E-MTAB-3267-Kidney, GSE31684-Bladder, OV GSE31245@PRECOG, GSE49997 OV, METABRIC BreastLumA, Prostate GSE16560@PRECOG, Gide2019-PD1 + CTLA4 Melanomas (Fig. 4 c, Supplemental Fig. 3c). The same results happened in KIRC, SARC, LIHC in TCGA database (Fig. 4 d). It is well known that T cell dysfunction can negatively impact the prognosis even in the presence of cytotoxic CD8 + T cell, while T cell rejection might negatively impact the prognosis because of the absence of cytotoxic CD8 + T cell infiltration. Consequently, we strongly suggest that CD47 might impair CD8 + T cell function and so negatively impact tumor patients' prognosis. 3.4 CD47 differential genes enrichment in CD8 + Tex pathway. We categorized the TCGA cohort data into high-expression group and low-expression group, and performed pathway enrichment analysis for both groups of differently expressed genes (p < 0.05). We found that some of the same pathways are present in cancers(Fig. 5 a), which includes PID CD8 TCR Downstream Pathway in BLCA, BRCA, ESAD, ESCA, GBM, KIRP, LIHC, LUSC, OV, PRAD, SKCM, STAD, UCS, UVM; PID CD8 TCR Pathway in BLCA, BRCA, ESCA, GBM, LIHC, LUSC, OV, PRAD, SKCM, STAD, TGCT, UCS, UVM; WP T cell Antigen Receptor TCR Signal Pathway in BLCA, BRCA, ESCA, GBM, KIRP, LIHC, LUSC, OV, PRAD, SKCM, STAD, TGCT, UCS, UVM; BIOCARTA CTLA4 Pathway and WP Cancer Immunotherapy By PD1 Blockade in BLCA, BRCA, ESCA, LIHC, LUSC, OV, PRAD, SKCM, STAD, TGCT, UCS, UVM. Furthermore, CD8 + Tcell exhaustion correlated pathways comprise IL2, IL10, IL12, IL17, INF-gamma, T cell, TCR, and JAK-STAT. Furthermore, mountain maps presented a visualization of the above first 5 pathways enrichment results (Fig. 5 b), in BRCA, ESCA, LUSC, OV, and SKCM to further demonstrate the distribution of corresponding numbers of differential genes enriched. The figure illustrates that NES (normalized enrichment score) exhibited positivity, with the majority of the differential genes exhibiting enrichment in the high-expression group. We explore further the gene regulatory network of PID CD8 TCR Downstream Pathway (Fig. 5 c, Supplemental Fig. 4a), we discovered that TNFRSF9 and CD8A were expressed at a high level in above all cancers. Taking into account the regulatory networks deduced from the enriched outcomes, we can direct our attention towards a regulatory pathway extending from TNFRSF9 to IL2RA/B/G, via CD8A. Subsequently, we employ PPI(Fig. 5 d) to determine the association between the CD47 protein and the proteins linked to the core-enriched molecules. The findings indicated a direct interaction between CD47 protein and CD8A, TNFRSF9, IFNG, B2M, and GZMB. In light of our observations, we deduced that the activation of the CD47 within the signaling pathway could potentially exert a crucial influence on the regulation of CD8 + T cell functionality—a phenomenon that may potentially collaborate with TNFRSF9. 3.5 CD47 expression is related to the CD8 + Tex in pan-cancer. We delved deeper into the connection between CD47 and the exhaustion of T-cells. The co-expression heat map showed a relationship between CD47 expression in pan-cancer and T-cell exhausted genes. [ 42 ] CD274, IDO1, CTLA4, ICOS, TIGIT, IL10, TNFRSF9, HAVCR2 exhibited significant co-expression with CD47 in BLCA, BRCA, CESC, COAD, READ, CRC, ESCA, ESCC, GBM, HNSC, LUSC, OSCC, OV, SKCM, STAD, TGCT, THCA, UCS; NFKB1, GRB2, NFATC3, YY1, NFATC2IP, PRDM1, and FOXO1 in other cancers. Additionally, it was demonstrated that TNFRSF9 ranked among the top three genes in the tumors listed below: BRCA, COAD, ESCA, ESAD, ESCC, GBM, LUSC, OV, and DLBC(r > 0.5 but not the top); whereas CD274 held the top three positions in BLCA, BRCA, CESC, COAD, READ, CRC, LAML, LGG, LUAD, OSCC, PCPG, PRAD, READ, SARC, SKCM, STAD, THCA, UCS (Supplemental Table 1). (Fig. 6 a, 6 b, Supplemental 4c) After that, we used the single-cell analysis to evaluate the expression of CD47 in CD8 + Tex and CD8 + T cells from the TISCH database and used the embedded bar chart to show the Data distribution. We found CD47 mainly expressed on CD8 + Tex(<50%) in AML, BRCA, CHOL, CLL, CRC, ESCA, Glioma, HNSC, KICH, LIHC, MCC, MM, NHL, NSCLC, OS, OV, PRAD, SCC, SCLC, SKCM, THCA, UVM(Fig. 6 c). And we further detect the expression of TNFRSF9 and CD47. It was observed that both of them exhibited expression on CD8 + Tex in BRCA, BCC, CHOL, CLL, CRC, Glioma, KIRC, LIHC, MCC, NHL, NSCLC, PAAD, SKCM, UCEC, and UVM (Fig. 6 d). The findings indicate that the association of CD47 and TNFRSF9 with CD8 + Tex in pan-cancer suggests their involvement in the dysregulation of CD8 + Tex in this type of cancer. 4. Discussion These findings indicate a strong correlation between CD47 and the development and advancement of multiple cancers. Earlier studies have also reported functional links between CD47 and tumors, including immune system homeostasis. [ 2 , 43 ] Moreover, we focus on the relationship between expression levels of CD47 with prognosis [ 45 – 46 ], TME [ 47 ], immune evasion [ 48 ], adaptive immunity [ 44 ], and especially T-cell exhaustion [ 49 ] across TCGA based on pan-cancer. The heterogeneity and clinical characteristics of different cancer types and subtypes were significant [ 50 – 51 ]. Here, we investigated the oncogenic role of CD47 in TCGA. This study reports that CD47 mRNA or protein levels are overexpressed in nearly all TCGA cancers, especially BRCA, CHOL, COAD, ESCA, HNSC, KIRC, LIHC, PAAD, STAD, THCA, and UCEC. Moreover, CD47 also functioned with tumor staging or worse prognosis of ACC, HNSC, KICH, KIRC, LGG, LICH, LUSC, OV, PAAD, RCC, THCA, UCEC, and UVM. Our findings align with prior clinical and preclinical research indicating heightened CD47 expression levels in cancer [ 42 , 52 ] and are linked to high-risk characteristics [ 53 – 54 ]. These observations suggested that CD47 could be a biomarker for cancer detection, staging, and follow-up. The correlation between TME, tumor immune evasion, cancer prognosis, and the therapeutic response has been demonstrated [ 55 – 56 ]. Blocking the CD47 / SIRPα axis avoids phagocytosis and is an essential checkpoint of innate immunity correlated with tumor immune evasion. Despite this, there is still a great deal of information to be uncovered concerning adaptive immunity. It was reported that CD8 + T cells express CD47 [ 14 ] and CD47/SIRPα blockade in targeting intrinsic immune resistance to CD8-mediated immunotherapy [ 15 – 16 ]. Thus, two distinct immune evasion mechanisms were proposed: anergic T-cell and dysfunctional T-cell phenotypes [ 57 ]. The two of them facilitated tumor growth, spread, metastasis, and resistance to treatment by encouraging tumor immune evasion. Studies indicated that different oncogenic proteins regulate tumor infiltration of immune cells by different mechanisms. As an illustration, an increase in MYC function hinders the activation and infiltration of T-cells, while the loss of PTEN hampers the effective priming of T-cells [ 58 ]. Then, we assessed the correlation between CD47 expression levels and infiltration of four immunosuppressive cells. Details said these four immunosuppressive cells were CAFs, Tregs, M2-TAMs, and MDSCs, reported as biomarkers of T-cell exclusion in TME [ 59 – 62 ]. Interestingly, we found that in BRCA-LumA, LICH, LUAD, PRAD, and THYM, there is a strong correlation between the expression of immunosuppressive cells and the level of CD47 expression (≥ 2 immunosuppressive cell types, every cell type ≥ 2 calculated method positive, r>0.2 and p<0.05). Thus, we postulate that one of the main mechanisms by which CD47 controls tumor immune cell escape, tumor growth, and metastasis is T-cell rejection. However, other cancer types still need mechanisms to be identified. Therefore, our exploration of the relationship between CD47 and T-cell infiltration. (including ≥ 2 immune types, every cell type ≥ 2 calculated method positive, r > 0.2, p < 0.05). This result showed significant positive correlations in BLCA, BRCA, BRCA-Basal, COAD, DLBC, ESCA, KIRC, KIRP, LIHC, LUSC, PAAD, PRAD, READ, SKCM, SKCM-Metastasis, STAD, TGCT, THCA, UVM. This provides additional evidence in favor of our hypothesis that CD47 activate a malfunctioning T-cell phenotype to control immune evasion and patient outcome [ 62 – 64 ]. While the prognostic impact of CD47-mediated CD8 + T cells remains to be seen, we chose to observe the relationship between CD47 and Cytotoxic CD8 + T cell infiltration levels. Furthermore, the TCGA database revealed a substantial increase in the infiltration of Cytotoxic CD8 + T cell in BLCA, BRCA, CESC, COAD, COADREAD, ESAD, ESCA, GBM, HNSC, KIRC, LUSC, OV, SKCM, STAD, TGCT, THCA, UCS, when compared to the CD47 low group. Then, we explored whether these could affect the treatment efficacy and prognosis. We found no difference in prognosis when grouped by CD47 expression alone. Therefore, we further investigated the effect of Cytotoxic CD8 + T cell infiltration on prognosis in both high and low-expression groups of CD47. Interestingly, when CD47 was expressed high, we further investigated the effect of Cytotoxic CD8 + T cell infiltration on prognosis in both the CD47-high group and CD47-low group. It was also observed in BLCA, BRCA, COAD, KIRC, LIHC, LUNG CANCER (Adeno, Large, Squamous), OV, PAAD, PRAD, ARC, SKCM, TCGA, PRECOG, METABIC cohort. It is widely acknowledged that the effectiveness of immunotherapy is linked to the infiltration of Cytotoxic CD8 + T cell [ 64 – 66 ] (Supplemental Fig. 3a), and good Cytotoxic CD8 + T cell infiltration usually represents a better prognosis [ 67 ]. CD47 could inhibit effector T-cell recruitment as well as activation, which caused immunosuppressive effects of intratumoral. CD47 blockade indirectly Cytotoxic CD8 + T cell maintained the antitumor response by inhibiting immunosuppressive signals expressed in antigen-presenting cells or by protecting tumor-infiltrating Cytotoxic CD8 + T cell from local irradiation of the tumor [ 20 ]. Hence, the expression level of CD47 was associated with dysfunctional T-cells in those cohorts. Subsequently, we classified these tumors into high and low-expression categories using TCGA data and examined the distinctively expressed genes between the two groups by the median CD47. We used them for pathway enrichment to explore the relationship between CD47 high expression and immunomodulator. Elevated CD47 expression in BLCA, BRCA, ESAD, ESCA, GBM, KIRP, LIHC, LUSC, OV, PRAD, SKCM, STAD, UCS, UVM correlated with CD8 + T cells and cytokine interactions associated with pathway interactions in PD-1, CTLA4, IL-10, INF gamma, Cytotoxic CD8 + T cell. The results suggested that CD47 may be vital in regulating the adaptive immune, especially the CD8 + T cell exhaustion. As we all know, PD-1 is a well-known checkpoint expressing a pathway in both immune and tumor cells [ 68 ]. PD-1 blocks modulate the TME [ 69 ] by inducing an immune response in tumor cells. The PD1-blocked pathway-induced cancer immunotherapy showed a strong association with CD47's elevated expression, suggesting that the high expression of CD47 following PD1 inhibitor treatment causes a decrease in CD8 + T cells, resulting in drug resistance. In another instance, IL-10 could help T-cell dysfunction. CD8 + T cells play a key role in immune function, metastasis, and tumorigenesis [ 70 ]. There was a significant correlation between IL-2 and the prognosis of patients diagnosed with pan-cancer [ 71 – 72 ]. As for other cancer types, we failed to find a correlation with the T-dysfunction-related pathway. Maybe that is because the data was insufficient or for other reasons, such as the lack of a critical gene LAYN regulating T cell function or different TME.[ 73 ]. Therefore, to gain a better understanding of the regulatory network, we opted for the enriched pathway-PID CD8 TCR DOWNSTREAM PATHWAY. We conducted Bayesian network inference with CBNplot, which revealed that TNFRSF9 were highly expressed and the regulatory pathway from CD8A, IL2RA/B/G to TNFRSF9 was observed in BRCA, ESCA, LUSC OV, and SKCM. So, it was inferred that the CD47 could regulate the CD8 TCR DOWNSTREAM pathway caused CD8 + T cell exhaustion, which could co-work with TNFRSF9. Then, we use PPI(Fig. 5 d) to find the relationship between the CD47 protein and the proteins corresponding to the core-enriched molecules. The results showed that the CD47 protein directly interacts with TNFRSF9. We investigated the CD47 ligand SIRPA and the PPI network of the pathway core gene and discovered that it is associated with IL2, IL2RA, CD8A, B2M, IFNG, and GZMB, which corroborates our deduction. (Supplemental Fig. 4b) (all combined score>0.3). Next, we analyzed the expression and co-expression of alterations in the T-cell depletion gene set [ 42 ] CD47 in pan-cancer. We found CD274, IDO1, CTLA4, ICOS, TIGIT, IL10, TNFRSF9, HAVCR2 were significantly co-expressed with CD47 in Group1( BLCA, BRCA, CESC, COAD, READ, CRC, ESCA, ESCC, GBM, HNSC, LUSC, OSCC, OV, SKCM, STAD, TGCT, THCA, UCS ); NFKB1, GRB2, NFATC3, YY1, NFATC2IP, PRDM1 and FOXO1 in other cancers named Group 2.Interestingly, TNFRSF9 was the top three of these genes in BRCA, COAD, ESCA, ESAD, ESCC, GBM, LUSC, OV, and DLBC(r>0.5 but not the top); CD274 was the top three in BLCA, BRCA, CESC, COAD, READ, CRC, LAML, LGG, LUAD, OSCC, PCPG, PRAD, READ, SARC, SKCM, STAD, THCA; suggesting that they maybe functional partners for CD47 across cancer types, which also been reported in the study in some of the cancer types. [ 24 , 74 – 75 ]. The TME is recognized for its remarkable heterogeneity [ 76 ]. So, we used the TISCH single-cell database to elucidate how CD47 affects TME. Different immune cell profiles were observed based on the primary and metastatic tumor sites. Firstly, it showed CD47 did express on CD8 + Tex, and more highly than other CD8 + T cell in AML, BRCA, CHOL, CLL, CRC, ESCA, Glioma, HNSC, KICH, LIHC, MCC, MM, NHL, NSCLC, OS, OV, PRAD, SCC, SCLC, SKCM, THCA and UVM(Fig. 6 c). Then, CD47 and TNFRSF9 were both expressed in CD8 + Tex in BRCA, CHOL, CLL, CRC (COADREAD), GBM, HNSC, KIRC, LIHC, MCC, NHL, NSCLC, PAAD, SCC, SKCM, UCEC, and UVM. These results suggested that they are again functional partners of CD8 + Tex, particularly in BRCA, ESCA, LUSC, OV, and SKCM (Supplemental Table 2). Now, CD47-related dual target research progress is in full swing, such as CD47 and TNFRSF9 targeting fusion protein (DSP107) [ 24 , 79 ], PD1 and CD47 bispecific fusion molecule, bispecific antibody CD47xPD-L1, CD47 x HER2, CD47xICAM-1 and so on [ 80 – 84 ]. It can not only reduce the blood toxicity and side effects caused by CD47 monoclonal antibodies but also increase the anti-tumor effect of blocking CD47. We can see how CD47-regulated cell-intrinsic mechanisms promote tumor biology. It could be said that CD47 is a promising therapeutic target in cancer, including solid and non-solid tumors. Limitation In our study, we found there were also negative and opposite results in some cancers. For SKCM, the prognosis in the low CD47 group is poor. CD47 expression in adjacent tissues was found to be higher compared to that in cancer, as observed in KICH and LUSC. We analyzed these could be due to intrinsic tumor heterogeneity, tumor immune microenvironment, particularly telomere Tex cell richness [ 77 – 78 ], or insufficient data, which need more experiments to be further studied and verified. 5. Conclusions CD47 plays a crucial role in TME, prognosis, and immunotherapy through not only macrophages but also tumor-infiltrating T-lymphocyte cells, especially CD8 + T cells in pan-cancer. CD47 interacting with TNFRSF9 induces CD8 + T cells exhaustion, resulting in a poor prognosis. The strategy targeting both CD47 and TNFRSF9 could activate both innate and adaptive immune systems and delivering targeted therapy drugs can emerge as a significant advance in the treatment, especially in BRCA, ESCA, LUSC, OV, and SKCM patients. Declarations Acknowledgments We extend our gratitude to the public databases utilized in our investigation, whose platform facilitated and assisted in the uploading of their valuable datasets. Fundings This work was supported by [Science and Technology Program of Guangzhou, China] (Grant numbers [202201011052]). Declaration of Interests All of the authors state that there are no conflicts of interest. Contributors HX Liang, LT Yao, and DP Xie were major contributors who conceived the main idea. JH Leng helped with data collection; XW Jiao, LY Qiu, and ZM Tang helped write the manuscript; HL Wang and HR Qiu helped adjust the image format; Duo Chen and Hao Li helped design the study. ZN Lin helped oversee the study. All authors approved the submitted version. Ethics approval Approval was granted by the Ethics Committee at Guangdong Provincial People's Hospital. (No KY2023-137-01) The research conducted here adhered to the principles outlined in the Declaration of Helsinki. Data Sharing Statement The datasets provided in this study can be found in online repositories. References R. K. Tsai, D. E. Discher, Inhibition of "self" engulfment through deactivation of myosin-II at the phagocytic synapse between human cells. The Journal of Cell Biology 180, 989–1003 (2008). D. Li et al., SLAMF3 and SLAMF4 are immune checkpoints that constrain macrophage phagocytosis of hematopoietic tumors. Science immunology 7, eabj5501 (2022). H. Okazawa et al., Negative regulation of phagocytosis in macrophages by the CD47-SHPS-1 system. J Immunol 174, 2004-2011 (2005). H. Yang et al., Engineering macrophages to phagocytose cancer cells by blocking the CD47/SIRPɑ axis. Cancer Med 8, 4245-4253 (2019). J. C. Andrechak, L. J. Dooling, D. E. Discher, The macrophage checkpoint CD47: SIRPα for recognition of 'self' cells: from clinical trials of blocking antibodies to mechanobiological fundamentals. Philos Trans R Soc Lond B Biol Sci 374, 20180217 (2019). R. Bouwstra et al., CD47 Expression Defines Efficacy of Rituximab with CHOP in Non-Germinal Center B-cell (Non-GCB) Diffuse Large B-cell Lymphoma Patients (DLBCL), but not in GCB DLBCL. Cancer Immunol Res 7, 1663-1671 (2019). R. Uger, L. Johnson, Blockade of the CD47-SIRPα axis: a promising approach for cancer immunotherapy. Expert Opin Biol Ther 20, 5-8 (2020). Y. Li et al., Overexpression of CD47 predicts poor prognosis and promotes cancer cell invasion in high-grade serous ovarian carcinoma. Am J Transl Res 9, 2901-2910 (2017). R. Feng, H. Zhao, J. Xu, C. Shen, CD47: the next checkpoint target for cancer immunotherapy. Crit Rev Oncol Hematol 152, 103014 (2020). P. S. Petrova et al., TTI-621 (SIRPαFc): A CD47-Blocking Innate Immune Checkpoint Inhibitor with Broad Antitumor Activity and Minimal Erythrocyte Binding. Clin Cancer Res 23, 1068-1079 (2017). R. J. Puro et al., Development of AO-176, a Next-Generation Humanized Anti-CD47 Antibody with Novel Anticancer Properties and Negligible Red Blood Cell Binding. Mol Cancer Ther 19, 835-846 (2020). A. van Duijn, S. H. Van der Burg, F. A. Scheeren, CD47/SIRPα axis: bridging innate and adaptive immunity. J Immunother Cancer 10, (2022). L. B. Cham et al., Immunotherapeutic Blockade of CD47 Inhibitory Signaling Enhances Innate and Adaptive Immune Responses to Viral Infection. Cell reports 31, 107494 (2020). Z. Strizova et al., Tumoral and peritumoral NK cells and CD8(+) T cells of esophageal carcinoma patients express high levels of CD47. Scientific reports 10, 13936 (2020). A. A. Barkal et al., Engagement of MHC class I by the inhibitory receptor LILRB1 suppresses macrophages and is a target of cancer immunotherapy. Nat Immunol 19, 76-84 (2018). S. H. van der Burg, R. Arens, F. Ossendorp, T. van Hall, C. J. Melief, Vaccines for established cancer: overcoming the challenges posed by immune evasion. Nature reviews. Cancer 16, 219-233 (2016). E. Dheilly et al., Tumor-Directed Blockade of CD47 with Bispecific Antibodies Induces Adaptive Antitumor Immunity. Antibodies (Basel) 7, (2018). D. Tseng et al., Anti-CD47 antibody-mediated phagocytosis of cancer by macrophages primes an effective antitumor T-cell response. Proc Natl Acad Sci U S A 110, 11103-11108 (2013). X. Liu et al., CD47 blockade triggers T cell-mediated destruction of immunogenic tumors. Nat Med 21, 1209-1215 (2015). D. R. Soto-Pantoja et al., CD47 in the tumor microenvironment limits cooperation between antitumor T-cell immunity and radiotherapy. Cancer research 74, 6771-6783 (2014). C. Chester, M. F. Sanmamed, J. Wang, I. Melero, Immunotherapy targeting 4-1BB: mechanistic rationale, clinical results, and future strategies. Blood 131, 49-57 (2018). S. Bagheri, E. Safaie Qamsari, M. Yousefi, F. Riazi-Rad, Z. Sharifzadeh, Targeting the 4-1BB costimulatory molecule through single chain antibodies promotes the human T-cell response. Cell Mol Biol Lett 25, 28 (2020). D. T. Chu et al., An Update on Anti-CD137 Antibodies in Immunotherapies for Cancer. Int J Mol Sci 20, (2019). E. Cendrowicz et al., DSP107 combines inhibition of CD47/SIRPα axis with activation of 4-1BB to trigger anti-cancer immunity. Journal of experimental & clinical cancer research: CR 41, 97 (2022). J. Vivian et al., Toil enables reproducible, open source, extensive biomedical data analyses. Nat Biotechnol 35, 314-316 (2017). D. S. Chandrashekar et al., UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia 19, 649-658 (2017). T. Li et al., TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res 48, W509-w514 (2020). J. Fu et al., Large-scale public data reuse to model immunotherapy response and resistance. Genome Med 12, 21 (2020). P. Jiang et al., Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med 24, 1550-1558 (2018). Szklarczyk et al. Nucleic acids research 47.D1 (2018): D607-D613.2 D. Sun et al., TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment. Nucleic Acids Res 49, D1420-d1430 (2021). A. Athar et al., ArrayExpress update - from bulk to single-cell expression data. Nucleic Acids Res 47, D711-d715 (2019). T. Barrett et al., NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res 41, D991-995 (2013). J. Liu et al., An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell 173, 400-416.e411 (2018). S. Hanzelmann, R. Castelo, J. Guinney, GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013). G. Bindea et al., Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39, 782-795 (2013). M. I. Love, W. Huber, S. Anders, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550 (2014). G. Yu, L. G. Wang, Y. Han, Q. Y. He, clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284-287 (2012). A. Subramanian et al., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102, 15545-15550 (2005). N. L. Guo, Y. W. Wan, Network-based identification of biomarkers co-expressed with multiple pathways. Cancer Inform 13, 37-47 (2014). Sato N, Tamada Y, Yu G, Okuno Y. CBNplot : Bayesian Network Plots for Enrichment Analysis. Bioinformatics (2022) btac175. doi: 10.1093/bioinformatics/btac175 L. Yu et al., Significance of CD47 and Its Association With Tumor Immune Microenvironment Heterogeneity in Ovarian Cancer. Frontiers in immunology 12, 768115 (2021). S. M. G. Hayat et al., CD47: role in the immune system and application to cancer therapy. Cellular oncology (Dordrecht) 43, 19-30 (2020). S. H. Chen et al., Dual checkpoint blockade of CD47 and PD-L1 using an affinity-tuned bispecific antibody maximizes antitumor immunity. Journal for immunotherapy of cancer 9, (2021). W. Zhang et al., Advances in Anti-Tumor Treatments Targeting the CD47/SIRPα Axis. Frontiers in immunology 11, 18 (2020). Y. Jiang et al., Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer. Ann Oncol 31, 760-768 (2020). Y. Pan et al., Single-cell RNA sequencing reveals compartmental remodeling of tumor-infiltrating immune cells induced by anti-CD47 targeting in pancreatic cancer. Journal of hematology & oncology 12, 124 (2019). A. Shimizu et al., Exosomal CD47 Plays an Essential Role in Immune Evasion in Ovarian Cancer. Molecular cancer research: MCR 19, 1583-1595 (2021). T. T. Jiang et al., Clinical Response to Anti-CD47 Immunotherapy Is Associated with Rapid Reduction of Exhausted Bystander CD4(+) BTLA (+) T Cells in Tumor Microenvironment of Mycosis Fungoides. Cancers 13, (2021). I. Dagogo-Jack, A. T. Shaw, Tumour heterogeneity and resistance to cancer therapies. Nature reviews. Clinical Oncology 15, 81-94 (2018). I. Vitale, E. Shema, S. Loi, L. Galluzzi, Intratumoral heterogeneity in cancer progression and response to immunotherapy. Nature medicine 27, 212-224 (2021). D. Candas-Green et al., Dual blockade of CD47 and HER2 eliminates radioresistant breast cancer cells. Nature communications 11, 4591 (2020). Y. Yang, Z. Yang, Y. Yang, Potential Role of CD47-Directed Bispecific Antibodies in Cancer Immunotherapy. Front Immunol 12, 686031 (2021). W. B. Yu, Z. H. Ye, X. Chen, J. J. Shi, J. J. Lu, The development of small-molecule inhibitors targeting CD47. Drug Discov Today 26, 561-568 (2021). W. Hong, M. Xue, J. Jiang, Y. Zhang, X. Gao, Circular RNA circ-CPA4/ let-7 miRNA/PD-L1 axis regulates cell growth, stemness, drug resistance and immune evasion in non-small cell lung cancer (NSCLC). Journal of experimental & clinical cancer research: CR 39, 149 (2020). P. Anand et al., Single-cell RNA-seq reveals developmental plasticity with coexisting oncogenic states and immune evasion programs in ETP-ALL. Blood 137, 2463-2480 (2021). A. M. van der Leun, D. S. Thommen, T. N. Schumacher, CD8(+) T cell states in human cancer: insights from single-cell analysis. Nature reviews. Cancer 20, 218-232 (2020). S. Spranger, T. F. Gajewski, Impact of oncogenic pathways on evasion of antitumour immune responses. Nature reviews. Cancer 18, 139-147 (2018). C. J. Hanley, G. J. Thomas, T-cell tumor exclusion and immunotherapy resistance: a role for CAF targeting. British journal of cancer 123, 1353-1355 (2020). D. Verzella et al., GADD45β Loss Ablates Innate Immunosuppression in Cancer. Cancer research 78, 1275-1292 (2018). R. H. Vonderheide, A. S. Bear, Tumor-Derived Myeloid Cell Chemoattractants and T Cell Exclusion in Pancreatic Cancer. Frontiers in immunology 11, 605619 (2020). Y. Togashi, K. Shitara, H. Nishikawa, Regulatory T cells in cancer immunosuppression - implications for anti-cancer therapy. Nature reviews. Clinical oncology 16, 356-371 (2019). M. E. W. Logtenberg et al., Glutaminyl cyclase is an enzymatic modifier of the CD47- SIRPα axis and a target for cancer immunotherapy. Nature medicine 25, 612-619 (2019). A. Papalampros et al., Unique Spatial Immune Profiling in Pancreatic Ductal Adenocarcinoma with Enrichment of Exhausted and Senescent T Cells and Diffused CD47-SIRPα Expression. Cancers 12, (2020). J. Borst, T. Ahrends, N. Bąbała, C. J. M. Melief, W. Kastenmüller, CD4(+) T cell help in cancer immunology and immunotherapy. Nature reviews. Immunology 18, 635-647 (2018). B. Farhood, M. Najafi, K. Mortezaee, CD8(+) cytotoxic T lymphocytes in cancer immunotherapy: A review. Journal of cellular physiology 234, 8509-8521 (2019). Y. Yu et al., Association of Long Noncoding RNA Biomarkers With Clinical Immune Subtype and Prediction of Immunotherapy Response in Patients With Cancer. JAMA Network open 3, e202149 (2020). F. Xie, M. Xu, J. Lu, L. Mao, S. Wang, The role of exosomal PD-L1 in tumor progression and immunotherapy. Molecular cancer 18, 146 (2019). C. Luchini et al., ESMO recommendations on microsatellite instability testing for immunotherapy in cancer, and its relationship with PD-1/PD-L1 expression and tumour mutational burden: a systematic review-based approach. Ann Oncol 30, 1232-1243 (2019). A. I. Yu et al., Gut Microbiota Modulate CD8 T Cell Responses to Influence Colitis-Associated Tumorigenesis. Cell reports 31, 107471 (2020). D. J. Propper, F. R. Balkwill, Harnessing cytokines and chemokines for cancer therapy. Nature reviews. Clinical oncology 19, 237-253 (2022). T. Lan, L. Chen, X. Wei, Inflammatory Cytokines in Cancer: Comprehensive Understanding and Clinical Progress in Gene Therapy. Cells 10, (2021). J. H. Pan et al., LAYN Is a Prognostic Biomarker and Correlated With Immune Infiltrates in Gastric and Colon Cancers. Front Immunol 10, 6 (2019). R. C. Hsieh et al., ATR-mediated CD47 and PD-L1 up-regulation restricts radiotherapy-induced immune priming and abscopal responses in colorectal cancer. Science immunology 7, eabl9330 (2022). S. Lian et al., Simultaneous blocking of CD47 and PD-L1 increases innate and adaptive cancer immune responses and cytokine release. EBioMedicine 42, 281-295 (2019). G. Biffi, D. A. Tuveson, Diversity, and Biology of Cancer-Associated Fibroblasts. Physiological reviews 101, 147-176 (2021). L. Zheng et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 374, abe6474 (2021). Z. Zhang et al. Pan-cancer landscape of T-cell exhaustion heterogeneity within the tumor microenvironment revealed a progressive roadmap of hierarchical dysfunction associated with prognosis and therapeutic efficacy. EBioMedicine 83, 104207 (2022). A. Saeed et al. Phase 1 dose escalation study of DSP107, a first - in - class CD47 and 4-1BB targeting fusion protein, in combination with atezolizumab in patients with advanced solid tumors. The 2023 ASCO (American Society of Clinical Oncology) Annual Meeting, Chicago, American, June 2-6, 2023; 2632. H.P. Rui et al. D3L-001, a novel bispecific antibody targeting HER2 and CD47, demonstrates potent preclinical efficacy in solid tumors. AACR (American Association for cancer research) Annual Meeting 2023, Orlando, Florida, April 14-19, 2023; 1873. X. Chauchet et al. NI-2901, an affinity-optimized CD47xPD-L1 bispecific antibody for dual immune checkpoint blockade. AACR (American Association for cancer research) Annual Meeting 2023, Orlando, Florida, April 14-19, 2023; 2951. S.M. Liu et al. A novel pegylated bispecific antibody-drug conjugate (P-BsADCpbadc) targeting cancers co-expressing PD-L1 and CD47. AACR (American Association for cancer research) Annual Meeting 2023, Orlando, Florida, April 14-19, 2023; 6307. M. Ma et al. BSI-508, a novel bispecific fusion molecule targeting PD1 and CD47 for cancer immunotherapy. AACR (American Association for cancer research) Annual Meeting 2023, Orlando, Florida, April 14-19, 2023; 2958. O.K. Wang et al. VBI-002, a CD47xICAM-1 bispecific antibody for the treatment of hepatocellular carcinoma, melanoma and non-small cell lung cancers. AACR (American Association for cancer research) Annual Meeting 2023, Orlando, Florida, April 14-19, 2023; 6334. Additional Declarations No competing interests reported. Supplementary Files Suplementalinformation.zip SupplementalFigure.1.tif SupplementalFigure.2.tif SupplementalFigure.3.tif SupplementalFigure.4.tif Cite Share Download PDF Status: Published Journal Publication published 08 May, 2024 Read the published version in Discover Oncology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3628207","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":252411452,"identity":"536133b1-8260-4998-a593-dfc7866553a5","order_by":0,"name":"Hongxin Liang","email":"","orcid":"","institution":"Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hongxin","middleName":"","lastName":"Liang","suffix":""},{"id":252411453,"identity":"a56aa586-9253-4b9e-94fc-c5c6cda0fa9c","order_by":1,"name":"Lintong Yao","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lintong","middleName":"","lastName":"Yao","suffix":""},{"id":252411455,"identity":"5fbe5264-48a5-47f0-9e43-82563a883d8d","order_by":2,"name":"Daipeng Xie","email":"","orcid":"","institution":"Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Daipeng","middleName":"","lastName":"Xie","suffix":""},{"id":252411457,"identity":"88680c07-5973-4101-a0cd-90d33063b6ea","order_by":3,"name":"Duo Chen","email":"","orcid":"","institution":"Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Duo","middleName":"","lastName":"Chen","suffix":""},{"id":252411462,"identity":"23d4013f-bda3-4859-a07f-47e91c5085c4","order_by":4,"name":"Jinchi Dai","email":"","orcid":"","institution":"Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinchi","middleName":"","lastName":"Dai","suffix":""},{"id":252411463,"identity":"44127714-c892-408e-9322-ccc7518a560f","order_by":5,"name":"Hongrui Qiu","email":"","orcid":"","institution":"Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hongrui","middleName":"","lastName":"Qiu","suffix":""},{"id":252411464,"identity":"93e8aaa2-b83b-4c47-8531-7348b94d8d7e","order_by":6,"name":"Huili Wang","email":"","orcid":"","institution":"Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huili","middleName":"","lastName":"Wang","suffix":""},{"id":252411465,"identity":"93a117fe-9090-452e-a75a-34fed539f6cc","order_by":7,"name":"Hao Li","email":"","orcid":"","institution":"Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Li","suffix":""},{"id":252411466,"identity":"09ecea04-6412-459b-b446-494e3692386a","order_by":8,"name":"Jinhang Leng","email":"","orcid":"","institution":"Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinhang","middleName":"","lastName":"Leng","suffix":""},{"id":252411467,"identity":"4579eff1-b855-4019-ae1a-79f8c579e9d4","order_by":9,"name":"Ziming Tang","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ziming","middleName":"","lastName":"Tang","suffix":""},{"id":252411469,"identity":"461b8ef2-3b97-4304-9ec9-e058b9a66eba","order_by":10,"name":"Xiuwei Jiao","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiuwei","middleName":"","lastName":"Jiao","suffix":""},{"id":252411471,"identity":"d1534a68-6733-47e4-a4ac-293a844fda3d","order_by":11,"name":"Liyun Qiu","email":"","orcid":"","institution":"Zhongshan School of Medicine, Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liyun","middleName":"","lastName":"Qiu","suffix":""},{"id":252411472,"identity":"bbe49153-55ac-4269-97dd-a5c54ba12ad0","order_by":12,"name":"Haiyu Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACCQYGZjDJ3sAgwdhAkhaeA6RpATESiNQiP7v5mHRBjUXihpvPL94u3MEgZ96/gPFzAR4tjHOOpUnPOCaRuOF2TrH1zDMMxjI3HjBLz8CjhVkix0yahw2sJU2at40hcYbEATZmHjxa2MBa/gG13DxDpBYekBbeNqCWG+zHIFr4G/BrkZBIS7bm7ZMwnnkmh9l6ZpuEsYQEY7M0Pi3yM5IP3ub5Vifbd/z4w9uFbTZyEvyHD37GpwUGHBsYeAwgcSqR2ECEBgYGe2CKeQCJU/4DROkYBaNgFIyCkQMACVtFlbCo+0sAAAAASUVORK5CYII=","orcid":"","institution":"Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Haiyu","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2023-11-18 01:15:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3628207/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3628207/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12672-024-00951-z","type":"published","date":"2024-05-08T04:08:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":47222004,"identity":"6dbb0494-9fcc-428c-9dd2-dfd46df83560","added_by":"auto","created_at":"2023-11-28 19:07:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1094737,"visible":true,"origin":"","legend":"\u003cp\u003eThe workflow of the study. TCGA, The Cancer Genome Atlas; TME, Tumor microenvironment; TNM, Tumor Node Metastasis; TAMs, tumor-associated macrophages; CAFs, cancer-associated fibroblasts; aDC (activated DC); B cells; CD8 T cells; CTL(Cytotoxic T cells); DC; Eosinophils; iDC (immature DC); Macrophages; Mast cells; Neutrophils; NK CD56bright cells; NK CD56dim cells; NK cells; pDC (Plasmacytoid DC); T cells; T helper cells; Tcm (T central memory); Tem (T effector memory); Tfh (T follicular helper); Tgd (T gamma delta); Th1 cells; Th17 cells; Th2 cells; Treg, regulatory T cells; CAFs, cancer-associated fibroblasts; MDSCs, myeloid-derived suppressor cells; M2-TAMs; M2 subtype of tumor-associated macrophages. MSI, Microsatellite instability; TMB, Tumor mutational burden; CD274, Cluster of differentiation 274; IFNG, interferon-γ; 4-1BB(TNFRSF9), TNF receptor superfamily member 9; CD8+Tex, exhausted CD8 T lymphocyte cell.\u003c/p\u003e","description":"","filename":"Figure.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3628207/v1/8614e8afd3cc5af6b89bdda1.png"},{"id":47222005,"identity":"9fd4be08-9cc2-4765-ae0a-4fc385c7a448","added_by":"auto","created_at":"2023-11-28 19:07:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":675063,"visible":true,"origin":"","legend":"\u003cp\u003eCD47 is aberrantly overexpressed and is associated with poor cancer prognoses. (A) Boxplots showing differential CD47 expression levels (log2FPKM + 1)/ (log2TPM + 1) between tumors in the XENA-TCGA_GTEx database. Box plots showing differential CD47 expression levels (log2FPKM + 1)/ (log2TPM + 1) between tumor and adjacent normal tissues (Paired Patient) across the TCGA database. CD47 is expressed differently in multiple cancers. (B)Boxplots illustrating the varying levels of CD47 expression (protein) among tumors in the CPTAC database. (C)Kaplan-Meier curves of cumulative survival differences between TCGA cancer cohorts with high and those with low expression levels of CD47. The presentation showcases TCGA cancers that exhibit statistically significant variations among the cohorts. UCSC XENA (https://xenabrowser.net/datapages/) by the Toil process unified TCGA RNAseq TPM format data processing. (GTEx)The Genotype-Tissue Expression; Significance representation: ns, p≥0.05; *, p\u0026lt; 0.05; **, p\u0026lt;0.01; ***, p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3628207/v1/7a4d694450fd2dfe8fd46697.png"},{"id":47222006,"identity":"87314be8-24dd-4755-8187-669c9864c623","added_by":"auto","created_at":"2023-11-28 19:07:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1858286,"visible":true,"origin":"","legend":"\u003cp\u003eThe differential expression of CD47 in tumor microenvironment. (A) (B) The heatmap chart showed correlations of CD47 expression with infiltration by different immune cell types and different immunosuppressive cell types in various TCGA cancer types. (C)The differential expression of CD47 in Pan-cancer was predominantly associated with CD8+T cells, CD4+T cells, DC cells, and macrophages, as demonstrated by the lollipop. Correlation is depicted with a purity-corrected partial (D) Bar plot showing the biomarker relevance of CD47 compared to standardized cancer immune evasion biomarkers in immune checkpoint blockade (ICB) sub-cohorts. The AUC was utilized to assess the predictive efficacy of the test biomarkers in determining the ICB response status. Spearman’s rho values and statistical significance were used. (A. B TIMER database) (C. R3.6.3 ssGSEA). Significance representation: ns, p≥0.05; *, p\u0026lt; 0.05; **, p\u0026lt;0.01; ***, p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3628207/v1/72aa7e79071a8aa612b35e53.png"},{"id":47222009,"identity":"8ad79898-9f02-48d2-a555-727e322f8493","added_by":"auto","created_at":"2023-11-28 19:07:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":525585,"visible":true,"origin":"","legend":"\u003cp\u003eCD47 on CD8+T cells Infiltration Influenced Tumor Prognosis. (A) The violin diagram illustrates the disparity in CD47 expression within the infiltration of Cytotoxic CD8+T-cell. Kaplan-Meier curves (the first picture on the left) of survival ratios as a measure of the (B)TCGA cohorts (C)immunotherapeutic response (immune checkpoint blockade) between cancer cohorts (D) GEO and other cohorts with high and those with low expression levels of CD47. The remainder (the second and third picture on the left) of the graph illustrates the prognosis for the two different sets of CTL expressions with CD47-high group and CD47-low group in above cohorts. Only cancers with statistically significant differences between the cohorts are presented. Significance representation: ns, p≥0.05; *, p\u0026lt; 0.05; **, p\u0026lt;0.01; ***, p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure.4.png","url":"https://assets-eu.researchsquare.com/files/rs-3628207/v1/7b3587d3b3fd91cc521a24f2.png"},{"id":47223538,"identity":"2ba162ba-d545-4f91-aa58-fe4ce7122e15","added_by":"auto","created_at":"2023-11-28 19:15:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1767437,"visible":true,"origin":"","legend":"\u003cp\u003eCD47 Differential Genes Enriched in CD8+Tex Pathway. (A) GSEA enrichment analysis results based on CD47 differentially expressed genes in Pan-cancer. (B) The mountain map showcased the path enrichment outcomes, encompassing PID CD8 TCR Downstream Pathway, PID CD8 TCR Pathway, WP Tcell Receptor Signal Pathway, BIOCARTA CTLA4 Pathway, and WP Cancer Immunotherapy by PD1 Blockade, along with the gene regulatory network for the PID CD8 TCR Downstream Pathway. (D) The PPI network demonstrated the correlation between the CD47 protein and the central protein of PID CD8 TCR Downstream Pathway. The representation of significance is as follows: ns, p≥0.05; *, p\u0026lt; 0.05; **, p\u0026lt;0.01; ***, p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure.5.png","url":"https://assets-eu.researchsquare.com/files/rs-3628207/v1/8e8b1007654b783cad73c974.png"},{"id":47222010,"identity":"66f33b62-89d9-4684-b243-90e9c445459c","added_by":"auto","created_at":"2023-11-28 19:07:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":863086,"visible":true,"origin":"","legend":"\u003cp\u003eCD47 expression is related to the CD8+Tex in many cancer types. (A)(B) Co-expression heat map shows the relationship between the expression of CD47 in pan-cancer and the exhausted genes of T cells, especially TNFRSF9, CD274, IDO1, and ICOS. (C) The bar chart displayed a wide range of CD47 expression in CD8+Tex and CD8+T cell from a single-cell database in a pan-cancerous environment. (D) The histogram illustrates the manifestation of CD47 and TNFRSF on CD8+Tex.Significance representation: ns, p≥0.05; *, p\u0026lt; 0.05; **, p\u0026lt;0.01; ***, p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure.6.png","url":"https://assets-eu.researchsquare.com/files/rs-3628207/v1/cf3d9fcb0bdfc780c903b440.png"},{"id":56140802,"identity":"0485c465-b102-49d3-9434-c91005acadc6","added_by":"auto","created_at":"2024-05-09 04:39:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4144230,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3628207/v1/122a06d1-da9f-4078-927e-6a04c00e8cc2.pdf"},{"id":47222008,"identity":"e5b88eb9-0e36-4a9a-b784-2ab1f7cf84ca","added_by":"auto","created_at":"2023-11-28 19:07:35","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":121621,"visible":true,"origin":"","legend":"","description":"","filename":"Suplementalinformation.zip","url":"https://assets-eu.researchsquare.com/files/rs-3628207/v1/225f687693f6b70393b9cef7.zip"},{"id":47222016,"identity":"89c31824-dff9-4bac-b670-5b25e18017fc","added_by":"auto","created_at":"2023-11-28 19:07:36","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10302552,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigure.1.tif","url":"https://assets-eu.researchsquare.com/files/rs-3628207/v1/25f713895394e5d54fe4463d.tif"},{"id":47222012,"identity":"507a7eaa-e548-4243-88ff-26901e946596","added_by":"auto","created_at":"2023-11-28 19:07:35","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2778796,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigure.2.tif","url":"https://assets-eu.researchsquare.com/files/rs-3628207/v1/deeb733dc1ae9b3ff88d9db6.tif"},{"id":47222014,"identity":"92df807e-e940-4468-a1bd-9a253dc6c8aa","added_by":"auto","created_at":"2023-11-28 19:07:36","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":13660128,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigure.3.tif","url":"https://assets-eu.researchsquare.com/files/rs-3628207/v1/226f1840472ca3f4191f059c.tif"},{"id":47222011,"identity":"d3581c15-90a3-40fb-ab08-d91499c9ebf1","added_by":"auto","created_at":"2023-11-28 19:07:35","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":14721748,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigure.4.tif","url":"https://assets-eu.researchsquare.com/files/rs-3628207/v1/03878a0642e469c217b184f1.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePan-cancer Analysis for the Prognostic and Immunological Role of CD47: Interact with TNFRSF9 Inducing CD8+T Cell Exhaustion.\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith the widespread acceptance of immunotherapies, the survival rates of cancer patients have significantly improved. Overexpression of various immune checkpoints is one of the significant ways of tumor immune evasion, which could be the predominant factor that limits the therapeutic effects of immune therapies. CD47, a star checkpoint of innate immunity, is being addressed as a hotspot mechanism. Exploring the interconnections between CD47 and tumor immune microenvironment (TIME), and drawing a more detailed picture of tumor immune evasion mode may be useful for the development of appropriate therapies and the discovery of immune targets. Those thus, would contribute to solutions to immune therapy resistance, and ultimately to the improved survival of patients with malignancies.\u003c/p\u003e \u003cp\u003eCD47 protein is a transmembrane glycoprotein member of the immunoglobulin superfamily, which interacts with signal regulatory protein α (SIRPα). Moreover, it forms the CD47/SIRPα axis, inhibits the accumulation of myosin, and therefore completes the \"Do not eat me\" signal, avoids phagocytosis of macrophages [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and inhibits innate immunity [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Plenty of tumor cell types were discovered to escape innate immunity through CD47 overexpression [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], correlating with poorer survival and response to standard therapies [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Drugs targeting the CD47/SIRPα axis have been a point of research, including monoclonal antibodies (McAb), bispecific antibodies, fusion proteins, combined chemotherapies, and immune therapies, such as CD47 McAb and fusion proteins containing SIRPα(TTI-621/2). They showed apparent clinical effectiveness and are in phase II or III clinical assessment [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, the CD47/SIRPα axis also affects adaptive immunity. Research shows that blockade of the CD47/ SIRPα axis can enhance T-cell response directly or through the mediation of myeloid cells [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For example, a subset of CD8\u0026thinsp;+\u0026thinsp;T cells would express SIRPα [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]; and blockading CD47/SIRPα can help solve resistance to CD8-mediated immunotherapy [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, CD47 agonists can induce antigen presentation and cross-priming T-lymphocytes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Indeed, in mouse models, the therapeutic activity of CD47 McAb is cross-primed by T-cells, and T-cell-shortage mice have received no therapeutic effect. In contrast, such a phenomenon was eliminated in wild-type (WT) mice [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It is reported that blocking CD47 through combined radiotherapy can directly CD8\u0026thinsp;+\u0026thinsp;T cells to enhance antitumor immunity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Hence, it is important to explore the relationship between the checkpoint of CD47 and antitumor T cell immune.\u003c/p\u003e \u003cp\u003eAn exciting target is tumor necrosis factor (TNF) receptor superfamily member TNFRSF9 (CD137, TNFRSF9). The expression of TNFRSF9 is selectively induced by an interaction between the TCR (the T cell receptor)/main histocompatibility complex (MHC) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. TNFRSF9 is considered the typical marker of Tumor-reactive T cell subsets of T-lymphocytes in TME but is not expressed on static T cells from peripheral blood [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. DSP107, a fusion protein, binds both targets and has dual immune regulatory effects, driving innate and adaptive immune responses and thus exhibiting excellent antitumor effects [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsidering the complicated relationship between CD47 in tumors and adaptive immunity, the survey investigates whether there is an effect induced by CD47 on CD8\u0026thinsp;+\u0026thinsp;T cells functions in TIME and, thus, the prognosis of patients with malignancies. The analysis is based on data from 33 tumor types and subtypes forming the TCGA database. It also includes data from single-cell sequencing, intended to provide approaches for tumor immune therapies.\u003c/p\u003e"},{"header":"2. Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Analysis tools and data collection\u003c/h2\u003e \u003cp\u003eXENA-TCGA GTEx\u003c/p\u003e \u003cp\u003eTCGA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GTEx handling were consolidated by the Toil process in UCSC XENA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Data (V8.0) conversion: Transcripts per million reads format RNAseq data in TPM format and log2 conversion for analysis and comparison. GTEx, The Genotype-Tissue Expression (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gtexportal.org/home/\u003c/span\u003e\u003cspan address=\"https://www.gtexportal.org/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). After log2 transformation, RNAseq data in TPM (transcripts per million reads) format was examined and contrasted. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eUALCAN\u003c/p\u003e \u003cp\u003eA comprehensive OMICS cancer data analysis web portal is located in Ualcan (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ualcan.path.uab.edu/\u003c/span\u003e\u003cspan address=\"http://ualcan.path.uab.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Transcript per million readings were used to standardize the expression level of CD47. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTIMER2.0\u003c/p\u003e \u003cp\u003eTIMER2.0, The database for thorough examination of immune cells that infiltrate tumors is called TIMER2.0(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer/\u003c/span\u003e\u003cspan address=\"https://cistrome.shinyapps.io/timer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To assess the number of immune infiltrates, 10897 samples from 32 TCGA cancer types are included in the TIMER database.\u003c/p\u003e \u003cp\u003eTIDE [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThis computational methodology was created to assess if tumor immune escape from cancer sample gene expression profiles is possible. Each tumor sample's TIDE score can be used as a proxy biomarker to forecast the response to immune checkpoint inhibitors. Additionally, possible regulators of tumor immune escape and resistance to cancer immunotherapies are presented by the highly rated genes in TIDE profiles. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSTRING\u003c/p\u003e \u003cp\u003eThe protein interaction network database STRING database [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] (string-db.org) is built on data from public databases and published works. It compiles information from multiple public databases, such as Gene Ontology, KEGG, NCBI, and UniProt, to combine it and create a thorough database of protein interaction networks.\u003c/p\u003e \u003cp\u003eTISCH\u003c/p\u003e \u003cp\u003eTo create its scRNA-seq atlas [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], TISCH gathered information from Array Express [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and Gene Expression Omnibus (GEO) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], encompassing 2045746 cells from healthy donors and 79 databases. Data sets were handled uniformly to provide clarity of the TME components at the annotated cluster and single-cell levels.\u003c/p\u003e \u003cp\u003eSTATISTIC\u003c/p\u003e \u003cp\u003eP value less than 0.05 was statistically significant. Significance markers: NS, P\u0026thinsp;\u0026ge;\u0026thinsp;0.05; *, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; * *, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; * * *, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Analysis of differential CD47 expression in average, tumor, stages, and protein levels.\u003c/h2\u003e \u003cp\u003eDifferential CD47 expression levels between tumors and normal tissues adjacent to TCGA cancer types were analyzed using R (version 3.6.3) and R packages (mainly GGGlot2 [version 3.3.3]) from the XENA-TCGA GTEx resource. Furthermore, Protein levels between tumors and adjacent normal tissues were also investigated using the UALCAN interactive web resource. Survival curves were presented using predictive analysis. SurvMiner [version 0.4.9] and Survival package [version 3.2\u0026ndash;10] were used (grouped by p-best). The type of prognosis was OS (Overall Survival), DSS (Disease-Specific Survival), and PFI (Progress Free Interval); we also obtained prognostic data from a Cell article [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Analysis of tumor immune and immunosuppressive cell infiltration and comparative biomarker analysis.\u003c/h2\u003e \u003cp\u003eUsing the TIMER2 server, we analyzed the correlation between tumor infiltration and CD47 expression, with four immunosuppressive cell types promoting T-cell rejection, MDSCs, CAF, M2-TAM, and Treg across 39 TCGA cancers. The Spearman partial rho value and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were used for correlation analysis. The results of the study used the algorithm with the best positive results. In addition, we used the GSVA R package [version 1.34.0] [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] to explore correlations between the expression of CD47 and the infiltration of 23 types of immune cells [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] in TCGA cancers. Then, the overall predictive power of CD47 was compared with standardized biomarkers of tumor immune responses in therapeutic response outcomes and OS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analysis of CD47 on Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell Infiltration Influenced Tumor Prognosis.\u003c/h2\u003e \u003cp\u003eWe used the GSVA R package [version 1.34.0] [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] to explore the difference in Cytotoxic T-cell infiltration in the different CD47 expression situations in TCGA pan-cancer. The median method was used to divide patients into CD4- high group and CD47-low group. We also use the TIDE algorithm to assess the effects of CD47 on Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Analysis of Pathway\u003c/h2\u003e \u003cp\u003eWe identify DEG between high and low-expression CD47 clusters using the DESeq2 R package [1.26.0 version] [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Division of patients into high and low CD47 groups by median method. These different genes were enriched in the pathway via the R package cluster profile [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] [3.14.3 version] (for SEEA analysis) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. [version 3.14.3] (for GSEA analysis) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Reference gene set: H.all.v7.2.symbols.GMT [Hallmarks]. The species is Homo sapiens. Gene set databases came from MSigDB Collections [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. It included the BIOCARTA subset of CP (browse 292 gene sets); KEGG subset of CP (browse 186 gene sets); PID subset of CP (browse 196 gene sets); REACTOME subset of CP (browse 1615 gene sets); WikiPathways subset of CP (browse 664 gene sets). Significance: It is generally considered that the conditions of False Discovery Rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.25 and p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Visualization: GGploT2 [version 3.3.3]. Furthermore, CBNplot [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] was used to investigate molecular regulatory connections. in PID_CD8_TCR_DOWNSTREAM_PATHWAY, exhibiting a Bayesian network inference approach. STRING database was used to detect the PPI (Protein-Protein Interaction Networks), showing the interactions of CD47 protein and core protein of that pathway in biological systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Analysis of Co-expression.\u003c/h2\u003e \u003cp\u003eWe used Software: R (version 3.6.3) to analyze the correlation of CD47 and T-cell exhaustion in TCGA pan-cancer. The data sets were level 3 HTSeq - FPKM RNAseq data format. Visualization of the results is provided by the R package: GGploT2 [version 3.3.3]. Besides, we used TISCH to determine whether CD47 was mainly expressed primarily on CD8\u0026thinsp;+\u0026thinsp;Tex and whether CD47 has a relationship with the CD8\u0026thinsp;+\u0026thinsp;Tex gene expressed primarily on CD8\u0026thinsp;+\u0026thinsp;Tex. The results were presented in the stacked value bar chart (bars of superimposed proportions) to see the expression levels of CD47 and TNFRSF9 in different cohorts through software R (version 3.6.3) and GGPLOT2 [version 3.3.3] (visualization).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Abnormal expression of CD47 in pan-cancer patients is associated with tumor stages and poor prognosis.\u003c/h2\u003e \u003cp\u003eWe looked into CD47's carcinogenic potential using the XENA-TCGA GTEx pan-cancer database. When comparing nearly all cancer types to normal tissue, we discovered that CD47 gene expression was higher in the former. (ACC, BRCA, BLCA, CHOL, COAD, DLBC, ESCA, GBM, HNSC, KIRC, KIRP, LAML, LGG, LIHC, LUAD, OV, PAAD, PRAD, READ, SARC, SKCM, STAD, THCA, THYM, UCEC, UCS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Additionally, we delved deeper into the CD47 expression of paired samples within the XENA-TCGA database, yielding identical outcomes to the XENA-TCGA GTEx pan-cancer database across various cancer types including BRCA, CHOL, COAD, ESCA, HNSC, KIRC, LIHC, PAAD, STAD, THCA, and UCEC. (Supplementary Fig.\u0026nbsp;1a).\u003c/p\u003e \u003cp\u003eSignificantly, we noted an increase in CD47 protein expression in HNSC, PAAD, UCEC, RCC, and OV compared to the normal levels, as indicated by the UALCAN database (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Supplementary Fig.\u0026nbsp;1b). Moreover, in cancer, the expression of CD47 was elevated in advanced tumor stages. For example, patients with M1 stage lung squamous cell carcinoma malignancy expressed more CD47 than patients with M0 stage. The same trend's outcomes were observed in THCA, UCEC, PRAD, KIRC, KIRP, and LIHC. (Supplementary Fig.\u0026nbsp;1c). Subsequently, we discovered a correlation between excessive CD47 expression and reduced overall survival in ACC, BRCA, LIHC, and KICH; decreased PFI in ACC, LUSC, UVM, and decreased DSS in ACC, LUSC, LGG, and KICH. All of these findings point to CD47 could be an early biomarker for cancer detection, staging, and monitoring. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, Supplementary Fig.\u0026nbsp;1d)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 CD47 is related to tumor immune evasion through infiltration by T lymphocyte cells.\u003c/h2\u003e \u003cp\u003eDue to its association with tumor immunity evasion, we assessed the associations between CD47 expression levels and the infiltration of MDSCs, CAFs, M2-TAMs, and Treg cells through six algorithms (QUANTISEQ, XCELL, CIBERSORT, CIBERSORT-ABS, TIDE, MCPCOUNTER). These types of immune cells could promote T-cell exclusion. Treg and CAF in BRCA-LumA, Treg and CAF in LICH, Treg, MDSC, and CAF in PRAD, and Treg and CAF in THYM were found to positively correlate with CD47 expression. (r\u0026lt;0.2, p\u0026lt;0.05, every cell type\u0026thinsp;\u0026ge;\u0026thinsp;2 algorithms positive, and at least two types of these cells are positive ) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea)\u003c/p\u003e \u003cp\u003eSubsequently, we employ the identical cognitive approach to identify the association with T cell infiltration and expression of CD47 through seven different algorithms (QUANTISEQ, XCELL, CIBERSORT, CIBERSORT-ABS, EPIC, MCPCOUNTER, TIMER). The results showed that CD47 expression was positively correlated with infiltration of CD8\u0026thinsp;+\u0026thinsp;T cell in almost all cancer types. (BLCA, BRCA, BRCA-Basal, COAD, DLBC, ESCA, KIRC, KIRP, LIHC, LUSC, PAAD, PRAD, READ, SKCM, SKCM-Metastasis, STAD, TGCT, THCA, UVM) (r\u0026lt;0.2, p\u0026lt;0.05, at least two types of these cells are positive, and at least two types of calculation methods). Interestingly, the infiltration of CD8\u0026thinsp;+\u0026thinsp;T cell effector memory is comparatively lower in the majority of cancer species compared to other types such as CD8\u0026thinsp;+\u0026thinsp;T cell central memory and CD8\u0026thinsp;+\u0026thinsp;T cell naive. As for CD4\u0026thinsp;+\u0026thinsp;T cell, there were still a lot of CD4\u0026thinsp;+\u0026thinsp;Tcells closely related to CD47, but it depends on the types of CD4\u0026thinsp;+\u0026thinsp;T cell. The presence of CD47 in the majority of cancer types was observed to have a positive correlation with the infiltration of CD4\u0026thinsp;+\u0026thinsp;T cell memory resting and CD4\u0026thinsp;+\u0026thinsp;T cell Th2, in contrast to CD4\u0026thinsp;+\u0026thinsp;T cell (non-regulatory) and CD4\u0026thinsp;+\u0026thinsp;T cell Th1(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eWe further explored which types of T-cell infiltration in tumors were most associated with CD47 using another result. It is worth mentioning that the correlation between CD8\u0026thinsp;+\u0026thinsp;T cells infiltration and CD47 is the highest in these cancer types (COAD, DLBC, ESCA, HNSC-HPV-, LIHC, LUAD, LUSC, PAAD, STAD) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). CD47 expression had strong positive correlations with T cell infiltration in various cancer types including BLCA, CHOL, COADREAD, DLBC, GBM, HNSC, KIRC, KIRP, LAML, LUSC, PAAD, PRAD, SKCM, TGCT, READ, STAD, UCS, and UVM. The T cell subtypes that displayed significant associations included T helper cells, CD8\u0026thinsp;+\u0026thinsp;T cells, CD4 T cells, Cytotoxic cells, Th1, Th2, Th17, as well as Tgd, Tcm, Tem, and TFH. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, Supplementary Fig.\u0026nbsp;2). Furthermore, it suggested a strong association between the infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells and CD47 in numerous cancer types (DLBC, ESCA, LUSC, OV, SKCM, STAD, TGCT, THCA, UCS, UVM). (r\u0026lt;0.2, p\u0026lt;0.05)\u003c/p\u003e \u003cp\u003eThen, we assessed CD47 biomarker relevance by comparing CD47 with standardized biomarkers based on its response outcomes to ICB sub-cohorts and OS predictive ability. Interestingly, we found that in 16 of the 25 ICB sub-cohorts, CD47 alone had an area greater than 0.5% under the AUC. CD47 was predicted to be more valuable than TMB, T. Clonality, B. Clonality, and MSI. Seven, nine, seven, and 13 ICB subgroups had more significant AUC values than 0.5. However, CD47 is lower than CD274, TIDE, IFNG, CD8, and Merck18. Based on these results, it is strongly indicated that CD47 plays a pivotal role in the immune microenvironment of tumors and exhibits a strong association with T-cell infiltration. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 CD47 on CD8\u0026thinsp;+\u0026thinsp;T cells infiltration had an impact on tumor prognosis.\u003c/h2\u003e \u003cp\u003eDrawing from our prior findings, it can be inferred that the presence of CD8\u0026thinsp;+\u0026thinsp;T cells exhibited a strong correlation with CD47. Interestingly, we found that in the CD47 High group, the level of Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell was more frequently observed in BLCA, BRCA, CESC, COAD, COADREAD, ESAD, ESCA, GBM, HNSC, KIRC, LUSC, OV, SKCM, STAD, TGCT, THCA, UCS types compared to the CD47 low group based on TCGA database. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, Supplemental Fig.\u0026nbsp;3a) Then we further identified the same conclusion in Multiple immunotherapy cohorts (Nathanson2017_CTLA4-OS, Gide2019_PD1-OS, Gide2019_PD1\u0026thinsp;+\u0026thinsp;CTLA4-OS, Miao2018_ICB-OS, Mariathasan2018_PDL1-OS, Riaz2017_PD1-OS, Li’_PD1-OS Zhao2019_PD1-OS, VanAllen2015_CTLA4-OS) (Supplemental Fig.\u0026nbsp;3b)\u003c/p\u003e \u003cp\u003eFurthermore, it was observed that the Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell Top group had a poorer prognosis in CD47-high group than the CD47-low group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Especially in these cohorts, high Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell infiltration did not suggest a better prognosis. In the same, this phenomenon was also shown in GSE13507@PRECOG Bladder, GSE10886@PRECOG Breast, Roepman Lung Cancer @PRECOG cohorts, GSE17536 Colorectal OS, E-MTAB-3267-Kidney, GSE31684-Bladder, OV GSE31245@PRECOG, GSE49997 OV, METABRIC BreastLumA, Prostate GSE16560@PRECOG, Gide2019-PD1\u0026thinsp;+\u0026thinsp;CTLA4 Melanomas (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, Supplemental Fig.\u0026nbsp;3c). The same results happened in KIRC, SARC, LIHC in TCGA database (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). It is well known that T cell dysfunction can negatively impact the prognosis even in the presence of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell, while T cell rejection might negatively impact the prognosis because of the absence of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell infiltration. Consequently, we strongly suggest that CD47 might impair CD8\u0026thinsp;+\u0026thinsp;T cell function and so negatively impact tumor patients' prognosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 CD47 differential genes enrichment in CD8\u0026thinsp;+\u0026thinsp;Tex pathway.\u003c/h2\u003e \u003cp\u003eWe categorized the TCGA cohort data into high-expression group and low-expression group, and performed pathway enrichment analysis for both groups of differently expressed genes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We found that some of the same pathways are present in cancers(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), which includes PID CD8 TCR Downstream Pathway in BLCA, BRCA, ESAD, ESCA, GBM, KIRP, LIHC, LUSC, OV, PRAD, SKCM, STAD, UCS, UVM; PID CD8 TCR Pathway in BLCA, BRCA, ESCA, GBM, LIHC, LUSC, OV, PRAD, SKCM, STAD, TGCT, UCS, UVM; WP T cell Antigen Receptor TCR Signal Pathway in BLCA, BRCA, ESCA, GBM, KIRP, LIHC, LUSC, OV, PRAD, SKCM, STAD, TGCT, UCS, UVM; BIOCARTA CTLA4 Pathway and WP Cancer Immunotherapy By PD1 Blockade in BLCA, BRCA, ESCA, LIHC, LUSC, OV, PRAD, SKCM, STAD, TGCT, UCS, UVM. Furthermore, CD8\u0026thinsp;+\u0026thinsp;Tcell exhaustion correlated pathways comprise IL2, IL10, IL12, IL17, INF-gamma, T cell, TCR, and JAK-STAT.\u003c/p\u003e \u003cp\u003eFurthermore, mountain maps presented a visualization of the above first 5 pathways enrichment results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), in BRCA, ESCA, LUSC, OV, and SKCM to further demonstrate the distribution of corresponding numbers of differential genes enriched. The figure illustrates that NES (normalized enrichment score) exhibited positivity, with the majority of the differential genes exhibiting enrichment in the high-expression group. We explore further the gene regulatory network of PID CD8 TCR Downstream Pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, Supplemental Fig.\u0026nbsp;4a), we discovered that TNFRSF9 and CD8A were expressed at a high level in above all cancers. Taking into account the regulatory networks deduced from the enriched outcomes, we can direct our attention towards a regulatory pathway extending from TNFRSF9 to IL2RA/B/G, via CD8A. Subsequently, we employ PPI(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed) to determine the association between the CD47 protein and the proteins linked to the core-enriched molecules. The findings indicated a direct interaction between CD47 protein and CD8A, TNFRSF9, IFNG, B2M, and GZMB. In light of our observations, we deduced that the activation of the CD47 within the signaling pathway could potentially exert a crucial influence on the regulation of CD8\u0026thinsp;+\u0026thinsp;T cell functionality\u0026mdash;a phenomenon that may potentially collaborate with TNFRSF9.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 CD47 expression is related to the CD8\u0026thinsp;+\u0026thinsp;Tex in pan-cancer.\u003c/h2\u003e \u003cp\u003eWe delved deeper into the connection between CD47 and the exhaustion of T-cells. The co-expression heat map showed a relationship between CD47 expression in pan-cancer and T-cell exhausted genes. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] CD274, IDO1, CTLA4, ICOS, TIGIT, IL10, TNFRSF9, HAVCR2 exhibited significant co-expression with CD47 in BLCA, BRCA, CESC, COAD, READ, CRC, ESCA, ESCC, GBM, HNSC, LUSC, OSCC, OV, SKCM, STAD, TGCT, THCA, UCS; NFKB1, GRB2, NFATC3, YY1, NFATC2IP, PRDM1, and FOXO1 in other cancers. Additionally, it was demonstrated that TNFRSF9 ranked among the top three genes in the tumors listed below: BRCA, COAD, ESCA, ESAD, ESCC, GBM, LUSC, OV, and DLBC(r\u0026thinsp;\u0026gt;\u0026thinsp;0.5 but not the top); whereas CD274 held the top three positions in BLCA, BRCA, CESC, COAD, READ, CRC, LAML, LGG, LUAD, OSCC, PCPG, PRAD, READ, SARC, SKCM, STAD, THCA, UCS (Supplemental Table\u0026nbsp;1). (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, Supplemental 4c)\u003c/p\u003e \u003cp\u003eAfter that, we used the single-cell analysis to evaluate the expression of CD47 in CD8\u0026thinsp;+\u0026thinsp;Tex and CD8\u0026thinsp;+\u0026thinsp;T cells from the TISCH database and used the embedded bar chart to show the Data distribution. We found CD47 mainly expressed on CD8\u0026thinsp;+\u0026thinsp;Tex(\u0026lt;50%) in AML, BRCA, CHOL, CLL, CRC, ESCA, Glioma, HNSC, KICH, LIHC, MCC, MM, NHL, NSCLC, OS, OV, PRAD, SCC, SCLC, SKCM, THCA, UVM(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). And we further detect the expression of TNFRSF9 and CD47. It was observed that both of them exhibited expression on CD8\u0026thinsp;+\u0026thinsp;Tex in BRCA, BCC, CHOL, CLL, CRC, Glioma, KIRC, LIHC, MCC, NHL, NSCLC, PAAD, SKCM, UCEC, and UVM (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). The findings indicate that the association of CD47 and TNFRSF9 with CD8\u0026thinsp;+\u0026thinsp;Tex in pan-cancer suggests their involvement in the dysregulation of CD8\u0026thinsp;+\u0026thinsp;Tex in this type of cancer.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThese findings indicate a strong correlation between CD47 and the development and advancement of multiple cancers. Earlier studies have also reported functional links between CD47 and tumors, including immune system homeostasis. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] Moreover, we focus on the relationship between expression levels of CD47 with prognosis [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], TME [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], immune evasion [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], adaptive immunity [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and especially T-cell exhaustion [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] across TCGA based on pan-cancer. The heterogeneity and clinical characteristics of different cancer types and subtypes were significant [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHere, we investigated the oncogenic role of CD47 in TCGA. This study reports that CD47 mRNA or protein levels are overexpressed in nearly all TCGA cancers, especially BRCA, CHOL, COAD, ESCA, HNSC, KIRC, LIHC, PAAD, STAD, THCA, and UCEC. Moreover, CD47 also functioned with tumor staging or worse prognosis of ACC, HNSC, KICH, KIRC, LGG, LICH, LUSC, OV, PAAD, RCC, THCA, UCEC, and UVM. Our findings align with prior clinical and preclinical research indicating heightened CD47 expression levels in cancer [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and are linked to high-risk characteristics [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. These observations suggested that CD47 could be a biomarker for cancer detection, staging, and follow-up.\u003c/p\u003e \u003cp\u003eThe correlation between TME, tumor immune evasion, cancer prognosis, and the therapeutic response has been demonstrated [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Blocking the CD47 / SIRPα axis avoids phagocytosis and is an essential checkpoint of innate immunity correlated with tumor immune evasion. Despite this, there is still a great deal of information to be uncovered concerning adaptive immunity. It was reported that CD8\u0026thinsp;+\u0026thinsp;T cells express CD47 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and CD47/SIRPα blockade in targeting intrinsic immune resistance to CD8-mediated immunotherapy [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Thus, two distinct immune evasion mechanisms were proposed: anergic T-cell and dysfunctional T-cell phenotypes [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The two of them facilitated tumor growth, spread, metastasis, and resistance to treatment by encouraging tumor immune evasion. Studies indicated that different oncogenic proteins regulate tumor infiltration of immune cells by different mechanisms. As an illustration, an increase in MYC function hinders the activation and infiltration of T-cells, while the loss of PTEN hampers the effective priming of T-cells [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThen, we assessed the correlation between CD47 expression levels and infiltration of four immunosuppressive cells. Details said these four immunosuppressive cells were CAFs, Tregs, M2-TAMs, and MDSCs, reported as biomarkers of T-cell exclusion in TME [\u003cspan additionalcitationids=\"CR60 CR61\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Interestingly, we found that in BRCA-LumA, LICH, LUAD, PRAD, and THYM, there is a strong correlation between the expression of immunosuppressive cells and the level of CD47 expression (\u0026ge;\u0026thinsp;2 immunosuppressive cell types, every cell type\u0026thinsp;\u0026ge;\u0026thinsp;2 calculated method positive, r\u0026gt;0.2 and p\u0026lt;0.05). Thus, we postulate that one of the main mechanisms by which CD47 controls tumor immune cell escape, tumor growth, and metastasis is T-cell rejection.\u003c/p\u003e \u003cp\u003eHowever, other cancer types still need mechanisms to be identified. Therefore, our exploration of the relationship between CD47 and T-cell infiltration. (including\u0026thinsp;\u0026ge;\u0026thinsp;2 immune types, every cell type\u0026thinsp;\u0026ge;\u0026thinsp;2 calculated method positive, r\u0026thinsp;\u0026gt;\u0026thinsp;0.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This result showed significant positive correlations in BLCA, BRCA, BRCA-Basal, COAD, DLBC, ESCA, KIRC, KIRP, LIHC, LUSC, PAAD, PRAD, READ, SKCM, SKCM-Metastasis, STAD, TGCT, THCA, UVM. This provides additional evidence in favor of our hypothesis that CD47 activate a malfunctioning T-cell phenotype to control immune evasion and patient outcome [\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile the prognostic impact of CD47-mediated CD8\u0026thinsp;+\u0026thinsp;T cells remains to be seen, we chose to observe the relationship between CD47 and Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell infiltration levels. Furthermore, the TCGA database revealed a substantial increase in the infiltration of Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell in BLCA, BRCA, CESC, COAD, COADREAD, ESAD, ESCA, GBM, HNSC, KIRC, LUSC, OV, SKCM, STAD, TGCT, THCA, UCS, when compared to the CD47 low group. Then, we explored whether these could affect the treatment efficacy and prognosis. We found no difference in prognosis when grouped by CD47 expression alone. Therefore, we further investigated the effect of Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell infiltration on prognosis in both high and low-expression groups of CD47. Interestingly, when CD47 was expressed high, we further investigated the effect of Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell infiltration on prognosis in both the CD47-high group and CD47-low group. It was also observed in BLCA, BRCA, COAD, KIRC, LIHC, LUNG CANCER (Adeno, Large, Squamous), OV, PAAD, PRAD, ARC, SKCM, TCGA, PRECOG, METABIC cohort. It is widely acknowledged that the effectiveness of immunotherapy is linked to the infiltration of Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell [\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] (Supplemental Fig.\u0026nbsp;3a), and good Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell infiltration usually represents a better prognosis [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. CD47 could inhibit effector T-cell recruitment as well as activation, which caused immunosuppressive effects of intratumoral. CD47 blockade indirectly Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell maintained the antitumor response by inhibiting immunosuppressive signals expressed in antigen-presenting cells or by protecting tumor-infiltrating Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell from local irradiation of the tumor [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Hence, the expression level of CD47 was associated with dysfunctional T-cells in those cohorts.\u003c/p\u003e \u003cp\u003eSubsequently, we classified these tumors into high and low-expression categories using TCGA data and examined the distinctively expressed genes between the two groups by the median CD47. We used them for pathway enrichment to explore the relationship between CD47 high expression and immunomodulator. Elevated CD47 expression in BLCA, BRCA, ESAD, ESCA, GBM, KIRP, LIHC, LUSC, OV, PRAD, SKCM, STAD, UCS, UVM correlated with CD8\u0026thinsp;+\u0026thinsp;T cells and cytokine interactions associated with pathway interactions in PD-1, CTLA4, IL-10, INF gamma, Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell. The results suggested that CD47 may be vital in regulating the adaptive immune, especially the CD8\u0026thinsp;+\u0026thinsp;T cell exhaustion. As we all know, PD-1 is a well-known checkpoint expressing a pathway in both immune and tumor cells [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. PD-1 blocks modulate the TME [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] by inducing an immune response in tumor cells. The PD1-blocked pathway-induced cancer immunotherapy showed a strong association with CD47's elevated expression, suggesting that the high expression of CD47 following PD1 inhibitor treatment causes a decrease in CD8\u0026thinsp;+\u0026thinsp;T cells, resulting in drug resistance. In another instance, IL-10 could help T-cell dysfunction. CD8\u0026thinsp;+\u0026thinsp;T cells play a key role in immune function, metastasis, and tumorigenesis [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. There was a significant correlation between IL-2 and the prognosis of patients diagnosed with pan-cancer [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. As for other cancer types, we failed to find a correlation with the T-dysfunction-related pathway. Maybe that is because the data was insufficient or for other reasons, such as the lack of a critical gene LAYN regulating T cell function or different TME.[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, to gain a better understanding of the regulatory network, we opted for the enriched pathway-PID CD8 TCR DOWNSTREAM PATHWAY. We conducted Bayesian network inference with CBNplot, which revealed that TNFRSF9 were highly expressed and the regulatory pathway from CD8A, IL2RA/B/G to TNFRSF9 was observed in BRCA, ESCA, LUSC OV, and SKCM. So, it was inferred that the CD47 could regulate the CD8 TCR DOWNSTREAM pathway caused CD8\u0026thinsp;+\u0026thinsp;T cell exhaustion, which could co-work with TNFRSF9. Then, we use PPI(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed) to find the relationship between the CD47 protein and the proteins corresponding to the core-enriched molecules. The results showed that the CD47 protein directly interacts with TNFRSF9. We investigated the CD47 ligand SIRPA and the PPI network of the pathway core gene and discovered that it is associated with IL2, IL2RA, CD8A, B2M, IFNG, and GZMB, which corroborates our deduction. (Supplemental Fig.\u0026nbsp;4b) (all combined score\u0026gt;0.3).\u003c/p\u003e \u003cp\u003eNext, we analyzed the expression and co-expression of alterations in the T-cell depletion gene set [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] CD47 in pan-cancer. We found CD274, IDO1, CTLA4, ICOS, TIGIT, IL10, TNFRSF9, HAVCR2 were significantly co-expressed with CD47 in Group1( BLCA, BRCA, CESC, COAD, READ, CRC, ESCA, ESCC, GBM, HNSC, LUSC, OSCC, OV, SKCM, STAD, TGCT, THCA, UCS ); NFKB1, GRB2, NFATC3, YY1, NFATC2IP, PRDM1 and FOXO1 in other cancers named Group 2.Interestingly, TNFRSF9 was the top three of these genes in BRCA, COAD, ESCA, ESAD, ESCC, GBM, LUSC, OV, and DLBC(r\u0026gt;0.5 but not the top); CD274 was the top three in BLCA, BRCA, CESC, COAD, READ, CRC, LAML, LGG, LUAD, OSCC, PCPG, PRAD, READ, SARC, SKCM, STAD, THCA; suggesting that they maybe functional partners for CD47 across cancer types, which also been reported in the study in some of the cancer types. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe TME is recognized for its remarkable heterogeneity [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. So, we used the TISCH single-cell database to elucidate how CD47 affects TME. Different immune cell profiles were observed based on the primary and metastatic tumor sites. Firstly, it showed CD47 did express on CD8\u0026thinsp;+\u0026thinsp;Tex, and more highly than other CD8\u0026thinsp;+\u0026thinsp;T cell in AML, BRCA, CHOL, CLL, CRC, ESCA, Glioma, HNSC, KICH, LIHC, MCC, MM, NHL, NSCLC, OS, OV, PRAD, SCC, SCLC, SKCM, THCA and UVM(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Then, CD47 and TNFRSF9 were both expressed in CD8\u0026thinsp;+\u0026thinsp;Tex in BRCA, CHOL, CLL, CRC (COADREAD), GBM, HNSC, KIRC, LIHC, MCC, NHL, NSCLC, PAAD, SCC, SKCM, UCEC, and UVM. These results suggested that they are again functional partners of CD8\u0026thinsp;+\u0026thinsp;Tex, particularly in BRCA, ESCA, LUSC, OV, and SKCM (Supplemental Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eNow, CD47-related dual target research progress is in full swing, such as CD47 and TNFRSF9 targeting fusion protein (DSP107) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e], PD1 and CD47 bispecific fusion molecule, bispecific antibody CD47xPD-L1, CD47 x HER2, CD47xICAM-1 and so on [\u003cspan additionalcitationids=\"CR81 CR82 CR83\" citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. It can not only reduce the blood toxicity and side effects caused by CD47 monoclonal antibodies but also increase the anti-tumor effect of blocking CD47. We can see how CD47-regulated cell-intrinsic mechanisms promote tumor biology. It could be said that CD47 is a promising therapeutic target in cancer, including solid and non-solid tumors.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitation\u003c/b\u003e In our study, we found there were also negative and opposite results in some cancers. For SKCM, the prognosis in the low CD47 group is poor. CD47 expression in adjacent tissues was found to be higher compared to that in cancer, as observed in KICH and LUSC. We analyzed these could be due to intrinsic tumor heterogeneity, tumor immune microenvironment, particularly telomere Tex cell richness [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e], or insufficient data, which need more experiments to be further studied and verified.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eCD47 plays a crucial role in TME, prognosis, and immunotherapy through not only macrophages but also tumor-infiltrating T-lymphocyte cells, especially CD8\u0026thinsp;+\u0026thinsp;T cells in pan-cancer. CD47 interacting with TNFRSF9 induces CD8\u0026thinsp;+\u0026thinsp;T cells exhaustion, resulting in a poor prognosis. The strategy targeting both CD47 and TNFRSF9 could activate both innate and adaptive immune systems and delivering targeted therapy drugs can emerge as a significant advance in the treatment, especially in BRCA, ESCA, LUSC, OV, and SKCM patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our gratitude to the public databases utilized in our investigation, whose platform facilitated and assisted in the uploading of their valuable datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by [Science and Technology Program of Guangzhou, China] (Grant numbers [202201011052]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll of the authors state that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHX Liang, LT Yao, and DP Xie were major contributors who conceived the main idea. JH Leng helped with data collection; XW Jiao, LY Qiu, and ZM Tang helped write the manuscript; HL Wang and HR Qiu helped adjust the image format; Duo Chen and Hao Li helped design the study. ZN Lin helped oversee the study. All authors approved the submitted version. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval was granted by the Ethics Committee at Guangdong Provincial People\u0026apos;s Hospital. (No KY2023-137-01) The research conducted here adhered to the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sharing Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets provided in this study can be found in online repositories.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eR. K. Tsai, D. E. Discher, Inhibition of \u0026quot;self\u0026quot; engulfment through deactivation of myosin-II at the phagocytic synapse between human cells. The Journal of Cell Biology 180, 989\u0026ndash;1003 (2008).\u003c/li\u003e\n\u003cli\u003eD. Li et al., SLAMF3 and SLAMF4 are immune checkpoints that constrain macrophage phagocytosis of hematopoietic tumors. Science immunology 7, eabj5501 (2022).\u003c/li\u003e\n\u003cli\u003eH. Okazawa et al., Negative regulation of phagocytosis in macrophages by the CD47-SHPS-1 system. J Immunol 174, 2004-2011 (2005).\u003c/li\u003e\n\u003cli\u003eH. Yang et al., Engineering macrophages to phagocytose cancer cells by blocking the CD47/SIRPɑ axis. Cancer Med 8, 4245-4253 (2019).\u003c/li\u003e\n\u003cli\u003eJ. C. Andrechak, L. J. Dooling, D. E. Discher, The macrophage checkpoint CD47: SIRP\u0026alpha; for recognition of \u0026apos;self\u0026apos; cells: from clinical trials of blocking antibodies to mechanobiological fundamentals. Philos Trans R Soc Lond B Biol Sci 374, 20180217 (2019).\u003c/li\u003e\n\u003cli\u003eR. Bouwstra et al., CD47 Expression Defines Efficacy of Rituximab with CHOP in Non-Germinal Center B-cell (Non-GCB) Diffuse Large B-cell Lymphoma Patients (DLBCL), but not in GCB DLBCL. Cancer Immunol Res 7, 1663-1671 (2019).\u003c/li\u003e\n\u003cli\u003eR. Uger, L. Johnson, Blockade of the CD47-SIRP\u0026alpha; axis: a promising approach for cancer immunotherapy. Expert Opin Biol Ther 20, 5-8 (2020).\u003c/li\u003e\n\u003cli\u003eY. Li et al., Overexpression of CD47 predicts poor prognosis and promotes cancer cell invasion in high-grade serous ovarian carcinoma. Am J Transl Res 9, 2901-2910 (2017).\u003c/li\u003e\n\u003cli\u003eR. Feng, H. Zhao, J. Xu, C. Shen, CD47: the next checkpoint target for cancer immunotherapy. Crit Rev Oncol Hematol 152, 103014 (2020).\u003c/li\u003e\n\u003cli\u003eP. S. Petrova et al., TTI-621 (SIRP\u0026alpha;Fc): A CD47-Blocking Innate Immune Checkpoint Inhibitor with Broad Antitumor Activity and Minimal Erythrocyte Binding. Clin Cancer Res 23, 1068-1079 (2017).\u003c/li\u003e\n\u003cli\u003eR. J. Puro et al., Development of AO-176, a Next-Generation Humanized Anti-CD47 Antibody with Novel Anticancer Properties and Negligible Red Blood Cell Binding. Mol Cancer Ther 19, 835-846 (2020).\u003c/li\u003e\n\u003cli\u003eA. van Duijn, S. H. Van der Burg, F. A. Scheeren, CD47/SIRP\u0026alpha; axis: bridging innate and adaptive immunity. J Immunother Cancer 10, (2022).\u003c/li\u003e\n\u003cli\u003eL. B. Cham et al., Immunotherapeutic Blockade of CD47 Inhibitory Signaling Enhances Innate and Adaptive Immune Responses to Viral Infection. Cell reports 31, 107494 (2020).\u003c/li\u003e\n\u003cli\u003eZ. Strizova et al., Tumoral and peritumoral NK cells and CD8(+) T cells of esophageal carcinoma patients express high levels of CD47. Scientific reports 10, 13936 (2020).\u003c/li\u003e\n\u003cli\u003eA. A. Barkal et al., Engagement of MHC class I by the inhibitory receptor LILRB1 suppresses macrophages and is a target of cancer immunotherapy. Nat Immunol 19, 76-84 (2018).\u003c/li\u003e\n\u003cli\u003eS. H. van der Burg, R. Arens, F. Ossendorp, T. van Hall, C. J. Melief, Vaccines for established cancer: overcoming the challenges posed by immune evasion. Nature reviews. Cancer 16, 219-233 (2016).\u003c/li\u003e\n\u003cli\u003eE. Dheilly et al., Tumor-Directed Blockade of CD47 with Bispecific Antibodies Induces Adaptive Antitumor Immunity. Antibodies (Basel) 7, (2018).\u003c/li\u003e\n\u003cli\u003eD. Tseng et al., Anti-CD47 antibody-mediated phagocytosis of cancer by macrophages primes an effective antitumor T-cell response. Proc Natl Acad Sci U S A 110, 11103-11108 (2013).\u003c/li\u003e\n\u003cli\u003eX. Liu et al., CD47 blockade triggers T cell-mediated destruction of immunogenic tumors. Nat Med 21, 1209-1215 (2015).\u003c/li\u003e\n\u003cli\u003eD. R. Soto-Pantoja et al., CD47 in the tumor microenvironment limits cooperation between antitumor T-cell immunity and radiotherapy. Cancer research 74, 6771-6783 (2014).\u003c/li\u003e\n\u003cli\u003eC. Chester, M. F. Sanmamed, J. Wang, I. Melero, Immunotherapy targeting 4-1BB: mechanistic rationale, clinical results, and future strategies. Blood 131, 49-57 (2018).\u003c/li\u003e\n\u003cli\u003eS. Bagheri, E. Safaie Qamsari, M. Yousefi, F. Riazi-Rad, Z. Sharifzadeh, Targeting the 4-1BB costimulatory molecule through single chain antibodies promotes the human T-cell response. Cell Mol Biol Lett 25, 28 (2020).\u003c/li\u003e\n\u003cli\u003eD. T. Chu et al., An Update on Anti-CD137 Antibodies in Immunotherapies for Cancer. Int J Mol Sci 20, (2019).\u003c/li\u003e\n\u003cli\u003eE. Cendrowicz et al., DSP107 combines inhibition of CD47/SIRP\u0026alpha; axis with activation of 4-1BB to trigger anti-cancer immunity. Journal of experimental \u0026amp; clinical cancer research: CR 41, 97 (2022).\u003c/li\u003e\n\u003cli\u003eJ. Vivian et al., Toil enables reproducible, open source, extensive biomedical data analyses. Nat Biotechnol 35, 314-316 (2017).\u003c/li\u003e\n\u003cli\u003eD. S. Chandrashekar et al., UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia 19, 649-658 (2017).\u003c/li\u003e\n\u003cli\u003eT. Li et al., TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res 48, W509-w514 (2020).\u003c/li\u003e\n\u003cli\u003eJ. Fu et al., Large-scale public data reuse to model immunotherapy response and resistance. Genome Med 12, 21 (2020).\u003c/li\u003e\n\u003cli\u003eP. Jiang et al., Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med 24, 1550-1558 (2018).\u003c/li\u003e\n\u003cli\u003eSzklarczyk et al. Nucleic acids research 47.D1 (2018): D607-D613.2\u003c/li\u003e\n\u003cli\u003eD. Sun et al., TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment. Nucleic Acids Res 49, D1420-d1430 (2021).\u003c/li\u003e\n\u003cli\u003eA. Athar et al., ArrayExpress update - from bulk to single-cell expression data. Nucleic Acids Res 47, D711-d715 (2019).\u003c/li\u003e\n\u003cli\u003eT. Barrett et al., NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res 41, D991-995 (2013).\u003c/li\u003e\n\u003cli\u003eJ. Liu et al., An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell 173, 400-416.e411 (2018).\u003c/li\u003e\n\u003cli\u003eS. Hanzelmann, R. Castelo, J. Guinney, GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).\u003c/li\u003e\n\u003cli\u003eG. Bindea et al., Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39, 782-795 (2013).\u003c/li\u003e\n\u003cli\u003eM. I. Love, W. Huber, S. Anders, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550 (2014).\u003c/li\u003e\n\u003cli\u003eG. Yu, L. G. Wang, Y. Han, Q. Y. He, clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284-287 (2012).\u003c/li\u003e\n\u003cli\u003eA. Subramanian et al., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102, 15545-15550 (2005).\u003c/li\u003e\n\u003cli\u003eN. L. Guo, Y. W. Wan, Network-based identification of biomarkers co-expressed with multiple pathways. Cancer Inform 13, 37-47 (2014).\u003c/li\u003e\n\u003cli\u003eSato N, Tamada Y, Yu G, Okuno Y. CBNplot : Bayesian Network Plots for Enrichment Analysis. Bioinformatics (2022) btac175. doi: 10.1093/bioinformatics/btac175\u003c/li\u003e\n\u003cli\u003eL. Yu et al., Significance of CD47 and Its Association With Tumor Immune Microenvironment Heterogeneity in Ovarian Cancer. Frontiers in immunology 12, 768115 (2021).\u003c/li\u003e\n\u003cli\u003eS. M. G. Hayat et al., CD47: role in the immune system and application to cancer therapy. Cellular oncology (Dordrecht) 43, 19-30 (2020).\u003c/li\u003e\n\u003cli\u003eS. H. Chen et al., Dual checkpoint blockade of CD47 and PD-L1 using an affinity-tuned bispecific antibody maximizes antitumor immunity. Journal for immunotherapy of cancer 9, (2021).\u003c/li\u003e\n\u003cli\u003eW. Zhang et al., Advances in Anti-Tumor Treatments Targeting the CD47/SIRP\u0026alpha; Axis. Frontiers in immunology 11, 18 (2020).\u003c/li\u003e\n\u003cli\u003eY. Jiang et al., Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer. Ann Oncol 31, 760-768 (2020).\u003c/li\u003e\n\u003cli\u003eY. Pan et al., Single-cell RNA sequencing reveals compartmental remodeling of tumor-infiltrating immune cells induced by anti-CD47 targeting in pancreatic cancer. Journal of hematology \u0026amp; oncology 12, 124 (2019).\u003c/li\u003e\n\u003cli\u003eA. Shimizu et al., Exosomal CD47 Plays an Essential Role in Immune Evasion in Ovarian Cancer. Molecular cancer research: MCR 19, 1583-1595 (2021).\u003c/li\u003e\n\u003cli\u003eT. T. Jiang et al., Clinical Response to Anti-CD47 Immunotherapy Is Associated with Rapid Reduction of Exhausted Bystander CD4(+) BTLA (+) T Cells in Tumor Microenvironment of Mycosis Fungoides. Cancers 13, (2021).\u003c/li\u003e\n\u003cli\u003eI. Dagogo-Jack, A. T. Shaw, Tumour heterogeneity and resistance to cancer therapies. Nature reviews. Clinical Oncology 15, 81-94 (2018).\u003c/li\u003e\n\u003cli\u003eI. Vitale, E. Shema, S. Loi, L. Galluzzi, Intratumoral heterogeneity in cancer progression and response to immunotherapy. Nature medicine 27, 212-224 (2021).\u003c/li\u003e\n\u003cli\u003eD. Candas-Green et al., Dual blockade of CD47 and HER2 eliminates radioresistant breast cancer cells. Nature communications 11, 4591 (2020).\u003c/li\u003e\n\u003cli\u003eY. Yang, Z. Yang, Y. Yang, Potential Role of CD47-Directed Bispecific Antibodies in Cancer Immunotherapy. Front Immunol 12, 686031 (2021).\u003c/li\u003e\n\u003cli\u003eW. B. Yu, Z. H. Ye, X. Chen, J. J. Shi, J. J. Lu, The development of small-molecule inhibitors targeting CD47. Drug Discov Today 26, 561-568 (2021).\u003c/li\u003e\n\u003cli\u003eW. Hong, M. Xue, J. Jiang, Y. Zhang, X. Gao, Circular RNA circ-CPA4/ let-7 miRNA/PD-L1 axis regulates cell growth, stemness, drug resistance and immune evasion in non-small cell lung cancer (NSCLC). Journal of experimental \u0026amp; clinical cancer research: CR 39, 149 (2020).\u003c/li\u003e\n\u003cli\u003eP. Anand et al., Single-cell RNA-seq reveals developmental plasticity with coexisting oncogenic states and immune evasion programs in ETP-ALL. Blood 137, 2463-2480 (2021).\u003c/li\u003e\n\u003cli\u003eA. M. van der Leun, D. S. Thommen, T. N. Schumacher, CD8(+) T cell states in human cancer: insights from single-cell analysis. Nature reviews. Cancer 20, 218-232 (2020).\u003c/li\u003e\n\u003cli\u003eS. Spranger, T. F. Gajewski, Impact of oncogenic pathways on evasion of antitumour immune responses. Nature reviews. Cancer 18, 139-147 (2018).\u003c/li\u003e\n\u003cli\u003eC. J. Hanley, G. J. Thomas, T-cell tumor exclusion and immunotherapy resistance: a role for CAF targeting. British journal of cancer 123, 1353-1355 (2020).\u003c/li\u003e\n\u003cli\u003eD. Verzella et al., GADD45\u0026beta; Loss Ablates Innate Immunosuppression in Cancer. Cancer research 78, 1275-1292 (2018).\u003c/li\u003e\n\u003cli\u003eR. H. Vonderheide, A. S. Bear, Tumor-Derived Myeloid Cell Chemoattractants and T Cell Exclusion in Pancreatic Cancer. Frontiers in immunology 11, 605619 (2020).\u003c/li\u003e\n\u003cli\u003eY. Togashi, K. Shitara, H. Nishikawa, Regulatory T cells in cancer immunosuppression - implications for anti-cancer therapy. Nature reviews. Clinical oncology 16, 356-371 (2019).\u003c/li\u003e\n\u003cli\u003eM. E. W. Logtenberg et al., Glutaminyl cyclase is an enzymatic modifier of the CD47- SIRP\u0026alpha; axis and a target for cancer immunotherapy. Nature medicine 25, 612-619 (2019).\u003c/li\u003e\n\u003cli\u003eA. Papalampros et al., Unique Spatial Immune Profiling in Pancreatic Ductal Adenocarcinoma with Enrichment of Exhausted and Senescent T Cells and Diffused CD47-SIRP\u0026alpha; Expression. Cancers 12, (2020).\u003c/li\u003e\n\u003cli\u003eJ. Borst, T. Ahrends, N. Bąbała, C. J. M. Melief, W. Kastenm\u0026uuml;ller, CD4(+) T cell help in cancer immunology and immunotherapy. Nature reviews. Immunology 18, 635-647 (2018).\u003c/li\u003e\n\u003cli\u003eB. Farhood, M. Najafi, K. Mortezaee, CD8(+) cytotoxic T lymphocytes in cancer immunotherapy: A review. Journal of cellular physiology 234, 8509-8521 (2019).\u003c/li\u003e\n\u003cli\u003eY. Yu et al., Association of Long Noncoding RNA Biomarkers With Clinical Immune Subtype and Prediction of Immunotherapy Response in Patients With Cancer. JAMA Network open 3, e202149 (2020).\u003c/li\u003e\n\u003cli\u003eF. Xie, M. Xu, J. Lu, L. Mao, S. Wang, The role of exosomal PD-L1 in tumor progression and immunotherapy. Molecular cancer 18, 146 (2019).\u003c/li\u003e\n\u003cli\u003eC. Luchini et al., ESMO recommendations on microsatellite instability testing for immunotherapy in cancer, and its relationship with PD-1/PD-L1 expression and tumour mutational burden: a systematic review-based approach. Ann Oncol 30, 1232-1243 (2019).\u003c/li\u003e\n\u003cli\u003eA. I. Yu et al., Gut Microbiota Modulate CD8 T Cell Responses to Influence Colitis-Associated Tumorigenesis. Cell reports 31, 107471 (2020).\u003c/li\u003e\n\u003cli\u003eD. J. Propper, F. R. Balkwill, Harnessing cytokines and chemokines for cancer therapy. Nature reviews. Clinical oncology 19, 237-253 (2022).\u003c/li\u003e\n\u003cli\u003eT. Lan, L. Chen, X. Wei, Inflammatory Cytokines in Cancer: Comprehensive Understanding and Clinical Progress in Gene Therapy. Cells 10, (2021).\u003c/li\u003e\n\u003cli\u003eJ. H. Pan et al., LAYN Is a Prognostic Biomarker and Correlated With Immune Infiltrates in Gastric and Colon Cancers. Front Immunol 10, 6 (2019).\u003c/li\u003e\n\u003cli\u003eR. C. Hsieh et al., ATR-mediated CD47 and PD-L1 up-regulation restricts radiotherapy-induced immune priming and abscopal responses in colorectal cancer. Science immunology 7, eabl9330 (2022).\u003c/li\u003e\n\u003cli\u003eS. Lian et al., Simultaneous blocking of CD47 and PD-L1 increases innate and adaptive cancer immune responses and cytokine release. EBioMedicine 42, 281-295 (2019).\u003c/li\u003e\n\u003cli\u003eG. Biffi, D. A. Tuveson, Diversity, and Biology of Cancer-Associated Fibroblasts. Physiological reviews 101, 147-176 (2021).\u003c/li\u003e\n\u003cli\u003eL. Zheng et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 374, abe6474 (2021).\u003c/li\u003e\n\u003cli\u003eZ. Zhang et al. Pan-cancer landscape of T-cell exhaustion heterogeneity within the tumor microenvironment revealed a progressive roadmap of hierarchical dysfunction associated with prognosis and therapeutic efficacy. EBioMedicine 83, 104207 (2022).\u003c/li\u003e\n\u003cli\u003eA. Saeed et al. Phase 1 dose escalation study of DSP107, a first - in - class CD47 and 4-1BB targeting fusion protein, in combination with atezolizumab in patients with advanced solid tumors. The 2023 ASCO (American Society of Clinical Oncology) Annual Meeting, Chicago, American, June 2-6, 2023; 2632.\u003c/li\u003e\n\u003cli\u003eH.P. Rui et al. D3L-001, a novel bispecific antibody targeting HER2 and CD47, demonstrates potent preclinical efficacy in solid tumors. AACR (American Association for cancer research) Annual Meeting 2023, Orlando, Florida, April 14-19, 2023; 1873.\u003c/li\u003e\n\u003cli\u003eX. Chauchet et al. NI-2901, an affinity-optimized CD47xPD-L1 bispecific antibody for dual immune checkpoint blockade. AACR (American Association for cancer research) Annual Meeting 2023, Orlando, Florida, April 14-19, 2023; 2951.\u003c/li\u003e\n\u003cli\u003eS.M. Liu et al. A novel pegylated bispecific antibody-drug conjugate (P-BsADCpbadc) targeting cancers co-expressing PD-L1 and CD47. AACR (American Association for cancer research) Annual Meeting 2023, Orlando, Florida, April 14-19, 2023; 6307.\u003c/li\u003e\n\u003cli\u003eM. Ma et al. BSI-508, a novel bispecific fusion molecule targeting PD1 and CD47 for cancer immunotherapy. AACR (American Association for cancer research) Annual Meeting 2023, Orlando, Florida, April 14-19, 2023; 2958.\u003c/li\u003e\n\u003cli\u003eO.K. Wang et al. VBI-002, a CD47xICAM-1 bispecific antibody for the treatment of hepatocellular carcinoma, melanoma and non-small cell lung cancers. AACR (American Association for cancer research) Annual Meeting 2023, Orlando, Florida, April 14-19, 2023; 6334.\u003c/li\u003e\n\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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CD47, CD8 + T cells, T-cell exhausted, pan-cancer","lastPublishedDoi":"10.21203/rs.3.rs-3628207/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3628207/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe role of CD47 in the effectiveness of immunotherapy has been researched. An understanding of the impact of CD47 on the tumor immune microenvironment, particularly with regard to CD8\u0026thinsp;+\u0026thinsp;T cells, remains inadequately clarified. Our research focuses on investigating the prognostic and immunological significance of CD47 to gain a deeper understanding of its potential applications in immunotherapy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe examination of differential gene expression, prognosis, immunological infiltration, pathway enrichment, and correlation was conducted using various R packages, computational tools, datasets, and cohorts. The notion was validated by the use of single-cell sequencing.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCD47 was expressed in nearly all cancer types, associated with poor prognosis in pan-cancer. The immunological research revealed that CD47 exhibited a stronger correlation with T-cell infiltration as opposed to T-cell rejection in cases of multiple cancers. The cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cell Top group had a poorer prognosis in the CD47-high group than the CD47-low group showing CD47 might impair CD8\u0026thinsp;+\u0026thinsp;T cell function. Mechanism exploration found that CD47 differential genes in multiple cancers were enriched in the CD8\u0026thinsp;+\u0026thinsp;T-cell exhausted pathway. Subsequent analysis of the CD8 TCR Downstream Pathway and correlation analysis of genes further demonstrated the significant involvement of TNFRSF9.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThere is a strong correlation between CD47 and the exhaustion of CD8\u0026thinsp;+\u0026thinsp;T cells, which in turn can facilitate immune evasion by cancer cells, ultimately resulting in a negative prognosis. Hence, the genes CD47 and T-cell exhaustion-linked genes, particularly TNFRSF9, exhibit potential as dual antigenic targets and offer valuable insights into the realm of immunotherapy.\u003c/p\u003e","manuscriptTitle":"Pan-cancer Analysis for the Prognostic and Immunological Role of CD47: Interact with TNFRSF9 Inducing CD8+T Cell Exhaustion.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-28 19:07:30","doi":"10.21203/rs.3.rs-3628207/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3638f704-dbe7-488d-a181-9139af2b1d51","owner":[],"postedDate":"November 28th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-09T04:08:02+00:00","versionOfRecord":{"articleIdentity":"rs-3628207","link":"https://doi.org/10.1007/s12672-024-00951-z","journal":{"identity":"discover-oncology","isVorOnly":false,"title":"Discover Oncology"},"publishedOn":"2024-05-08 04:08:01","publishedOnDateReadable":"May 8th, 2024"},"versionCreatedAt":"2023-11-28 19:07:30","video":"","vorDoi":"10.1007/s12672-024-00951-z","vorDoiUrl":"https://doi.org/10.1007/s12672-024-00951-z","workflowStages":[]},"version":"v1","identity":"rs-3628207","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3628207","identity":"rs-3628207","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}