CD47 is correlated with immune infiltration and is a prognostic biomarker in colorectal cancer

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We comprehensively examined the potential prognostic value, clinicopathological characteristics, and immune infiltration associated with CD47 in CRC patients. Results: In total, 305 differentially expressed genes (DEGs) were identified. The receiver operating characteristic (ROC) curve analysis of CD47 suggested an area under the ROC curve of 0.819. Kaplan–Meier survival analysis indicated that CRC with high CD47 expression had a better prognosis in the progression-free interval (PFI; P = 0.011). Five drug targets (ABT-510, ALX148, TTI-621, CC-90002, and Hu5F9-G4) were identified for CD47. A positive correlation existed between CD47 expression and infiltrating levels of aDC, macrophages, T helper cells, Tcm, Th1 cells, Th2 cells, CD8 T cells, cytotoxic cells, neutrophils, T cells, and Tgd. In the neoplasm type, CD47 expression was higher in colon adenocarcinoma patients than in rectal adenocarcinoma patients (P = 0.029). In PFI events, CD47 expression was higher in live patients than in dead patients (P = 0.018). Male patients with high CD47 expression showed improved overall survival compared with female patients (P = 0.014). CD47 protein was highly expressed in colorectal tumor tissue and lowly expressed in normal tissues in the Human Protein Atlas(HPA). Methylation analysis of CD47 in CRC revealed that the first and second CpG islands were hypermethylated, whereas the third CpG island was hypomethylated. Genetic alterations in CRC included amplification and deletion of CD47 in colorectal cancer. I153M was found to be a mutational hotspot for CD47. Conclusions: CD47 is correlated with immune infiltration and is a prognostic biomarker in colorectal cancer. CD47 Colorectal cancer Immune infiltration Prognostic biomarker Clinicopathological characteristics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 11 Figure 12 Figure 13 Introduction Exacerbated by the aging of the general population, tumors are becoming an increasingly serious threat to human health. Colorectal cancer (CRC) is the third most common type of cancer and the second leading cause of cancer-related deaths worldwide, with 1.8 million newly diagnosed cases and approximately 881,000 deaths worldwide in 2018[1]. The main strategies for colorectal cancer treatment are surgery, radiation therapy (or chemoradiation), and chemotherapeutic treatment. However, these treatments do not result in satisfactory therapeutic outcomes. Immunotherapy using programmed death-1 (PD-1) inhibitors, have proven promising new treatments. Similar to PD-1 inhibitors, targeting the cluster of differentiation 47 (CD47) is a novel immunotherapeutic strategy for treating human cancers. CD47 is a cell surface molecule that inhibits the phagocytosis of cells that express it by binding to its receptor, signal-regulatory protein alpha (SIRPα), on macrophages, and other immune cells. Thus, CD47 is an innate immune checkpoint and a promising diagnostic and therapeutic target[2]. CD47 is expressed at different levels in neoplastic and normal cells. Correspondingly, high CD47 expression is associated with clinical prognosis in patients with non-small cell lung cancer[3], melanoma[4], and oral squamous cell carcinoma[5]. However, the critical role of CD47 in colorectal cancer and its association with tumor immune infiltration remains unclear. As in previous biological information analyses[6], CD47 has been identified as an immune checkpoint gene. However, this study did not focus on immune cells for further analyses. In this study, to evaluate the expression, characteristics, and function of CD47, we integrated bioinformatic methods to explore the CD47 gene in CRC. In particular, the clinical features, diagnostic characteristic, immune infiltration properties, and prognostic values of CD47 in CRC were determined. Materials And Methods Data acquisition We collected gene expression data from 698 colon and rectal cancer (COADREAD) patients from the Cancer Genome Atlas (TCGA, https://www.cancer.gov/). RNA sequencing (RNA-seq) data with insufficient clinical information were excluded from this analysis, and third-generation high-throughput sequencing (HTSeq) data transformation into transcripts per million (TPM) and filtration were applied before further analysis. According to the median expression level of CD47, the cells were separated into high and low expression groups. TCGA-COADREAD data were used as a testing dataset. The gene expression profile GSE25070 of colorectal cancer patients was downloaded from the Gene Expression Omnibus (GEO) database[7] and used as the validation set. GSE25070 dataset samples were derived from humans (Homo sapiens), based on the GPL570 platform, and included both tumor and matched adjacent normal tissue samples. Identification of differentially expressed genes (DEGs) Differential expression analysis of TCGA-COADREAD over-expression and under-expression count data was performed using the R package DESeq2[8]. The cut-off threshold for differentially expressed genes was |log fold change (FC)| ≥ 1 and an adjusted P-value < 0.05. Genes with logFC ≥1 and P-value < 0.05 were considered upregulated genes, and those with logFC ≤ -1 and P-value < 0.05 were considered downregulated genes. Protein-protein interaction (PPI) network The Search Tool for the Retrieval of Interacting Genes (STRING) database focuses on known and predicted PPI networks[9](https://cn.string-db.org/). Cytoscape software[10]was used to analyze PPI networks and the interactions of the candidate DEG encoding proteins in CRC. Using the plugin Cytohubba[11]in Cytoscape software, the top 20 hub genes were identified from the PPI network. The PPI network was visualized and analyzed using the NetworkAnalyst[12]platform. We imported the top 20 hub genes into NetworkAnalyst (confidence score cut-off of 900) and visualized the key gene interaction networks. Construction of mRNA-TF, mRNA-RBP, mRNA-Drug, and mRNA-miRNA network Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST)[13](https://www.grnpedia.org/trrust/), a manually curated database of human transcriptional regulatory networks, contains not only the target gene of transcription factors but also potential interactions between transcription factors. Post-transcriptional regulation(POSTAR)3[14](http://111.198.139.65/) is a database that uses public large-scale crosslinking immunoprecipitation (CLIP)-seq data and external functional genome annotations, which provides binding sites for RNA-binding proteins, their functional variants, and regulatory mechanisms. The starBase database[15]contains more than 700 CLIP-seq datasets, current degradome data, multiple types of omics data assisted miRNA target prediction, many visualizations of miRNA target interfaces, and analyzed interactions between lncRNA, circRNA, protein and mRNA, or ceRNA mechanism of 23 species. The miRDB[16]is an online database for miRNA target prediction and provides information on the miRNA of humans, mice, and other species. miRWalk[17]is a comprehensive atlas of predicted miRNA target databases of conserved human, mouse, rat, dog, cow, and many other species. The Drug Gene Interaction Database (DGIdb)[18]is a database of integrated drug-gene interaction information and association information of known or potential drugs with genes. To demonstrate the relationship between CD47 and other target genes, we predicted CD47 transcription factors using the TRRUST database. In addition, we utilized the starBase and POSTAR3 databases to predict CD47 RNA Binding proteins. The CD47 miRNA was predicted using three miRNA target prediction databases: miRWalk, miRDB, and starBase. DGIdb was used to predict potentially effective therapeutic targets for CD47 and was visualized using Cytoscape software. Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis GO[19]analysis included biological process (BP), molecular function (MF), and cellular component (CC). KEGG[20]is a widely used database that collects information on genomes, biological pathways, diseases, and drugs. GO terms and KEGG pathway enrichment analyses of CD47 were performed using clusterProfiler[21]A false discovery rate (FDR) threshold < 0.05 was considered statistically significant. The differentially expressed genes were screened with an adj P-value < 0.05 and a Q-value < 0.05. P-values were corrected using the Benjamini and Hochberg (BH) method. GSEA GSEA[22]is a method that determines whether an a priori defined set of genes shows statistically significant expression differences between two biological states. In this study, the groups were divided according to median CD47 expression in the colorectal cancer database. The R package “clusterProfiler” was used to perform GSEA between the CD47 high- and low-expression groups in the COADREAD patients. GSEA was carried out using the following parameters (seed = 2020, calculated number = 1000, gene number per gene data = 10–500). P-values were corrected using the Benjamini and Hochberg (BH) method. The reference gene set used in GSEA was obtained from the Molecular Signatures Database (c2.all.v6.2. symbols.gmt)[23]. Enrichment was considered significant if FDR < 0.25 and P < 0.05. Immunohistochemistry Protein expression patterns in normal human and tumor tissues were used to generate an expression map using data from The Human Protein Atlas[24](https://www.proteinatlas.org/). Next, we used HPA databases, to compare CRC and normal tissue CD47 protein expression. Genome and methylation analysis The cBio Cancer Genomics Portal (cBioPortal) is an open-access Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data[25]TCGA data for clinical information, gene expression, and DNA methylation were visualized using MEXPRESS data[26](http://mexpress.be/). To explore CD47 mutations, genetic alteration of CD47 was analyzed in cBioPortal and further analyzed for CD47 mutation and expression in pan-cancer (TCGA). MEXPRESS was used to explore DNA variations and precise locations of mutations in the CD47 gene in colon and rectal cancer, analyzed CD47 methylation in pan-cancer, and evaluate relationships between multiple clinical variables and CD47 expression. Immune infiltration Immune cells, inflammatory cells, fibroblasts, interstitial tissues, cytokines, and chemokines are important components of the immune microenvironment. Immune infiltration analysis is critically important for understanding, predicting, and treating diseases. Single-sample gene set enrichment analysis (ssGSEA) was performed using the gene set variation analysis (GSVA)[27]R package to evaluate CD47 immune infiltration. Immune cell infiltration was visualized. Statistical significance was set at P < 0.05. Statistical analysis All statistical analyses were performed using R statistical software (v3.6.3). The relationship between CD47 expression and clinicopathological features was analyzed using the t-test. The Kaplan–Meier method was used to estimate overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS). Univariate and multivariate Cox proportional-hazard models were used for CD47 expression of survival and other clinical characteristics (age, T stage, M stage, N stage, residual tumor, and carcinoembryonic antigen(CEA) level). The cutoff value was set as the median value of CD47 expression level. In all tests, statistical significance was set at P < 0.05. CD47 high- and low-expression groups of OS, PFI, and DSS were estimated using the Kaplan–Meier method and two-sided log-rank test. The nomograms were carried out as independent predictors using multivariate analysis based on the Cox regression hazard model. Survival probabilities for individualized survival were predicted for 1-, 3-, and 5-year survival rates. Nomograms containing clinical characteristics and calibration plots were generated with the relapsing multiple sclerosis (RMS) package in R. Using the prediction model nomogram, the calibration curve contrasts observed probabilities of events using histogram assessment. Diagonal values represent the best predictions. The human discriminative ability nomogram used the concordance index (C index) and bootstrap resampling (1,000 re-samplings). The predictive accuracy was compared using the prognostic index, nomogram, and independent prognostic factors. In our study, all tests were two-sided, and P < 0.05 was considered statistically significant. Results CD47 differential expression in pan-cancer and prognostic capacity in colorectal cancer An overview of the workflow is shown in Figure 1. We analyzed the differential expression of CD47 in the 33 cancer types (Figure 2A). We found a significant difference in CD47 expression between cancerous and non-cancerous tissues in BRCA (P < 0.001), CESC (P = 0.009), CHOL (P < 0.001), COAD (P < 0.001), GBM (P = 0.001), HNSC (P < 0.001), KICH (P = 0.007), LIHC (P < 0.001), LUAD (P < 0.001), LUSC (P < 0.001), PRAD (P = 0.004), STAD (P < 0.001), THCA (P < 0.001), and READ (P = 0.007). However, the difference was not statistically significant for BLCA (P = 0.58), ESCA (P = 0.085), KIRC (P = 0.133), KIRP (P = 0.483), PAAD (P = 0.163), PCPG (P = 0.399), READ (P = 0.070), and UCEC (P = 0.078) (Figure 2A). Patients were stratified into high-or low-expression groups according to the median level CD47, and differential expression analysis was performed. We found a significant difference between the over-and-under-CD47 expression groups (P < 0.001) (Figure 2B–C). The diagnostic value of CD47 in patients with COADREAD was assessed using receiver operating characteristic (ROC) curve analysis. The value for CD47 was 0.819, suggesting a potential diagnostic role for CD47 in the colon and rectal cancer (Figure 2D). Kaplan–Meier survival analysis was performed to compare the OS, DSS, and PFI of CD47 in CRC. High CD47 expression was associated with an improved PFI in patients with colorectal cancer (P = 0.011). OS (P = 0.083) and DSS (P = 0.072) failed to demonstrate any significant difference in high- CD47 expression group, although there was a trend toward improved survival (Figure 2E–G). We performed differential expression analysis in CD47 by comparing COADREAD patients with healthy control samples in GSE25070. CD47 expression was significantly higher in cancer patients than in healthy controls using the validation GSE25070 dataset (P = 3.1e-06) (Figure 2H). Differential gene expression analysis was performed between CD47 over-expression and under-expression groups in colorectal cancer. A total of 305 genes were identified as DEGs, including 173 upregulated and 132 downregulated genes (Figure 2I–J). Construction of PPI networks Interactions of differentially expressed genes between groups (high expression vs. low expression) were determined and visualized (Figure 3A–B). The CytoHubba plugin in Cytoscape was performed to explore the top 20 hub genes in CRC ( AURKB, DLGAP5, CEP55, CCNA2, BIRC5, BUBIB, NUSAP1, TTK, UBE2C, KIF4A, TPX2, CDCA8, CDC20, CDK1, KIF20A, NDC80, CCNB1, KIF11, NCAPG, ASPM ) (Figure 3C). Network analysis of the top 20 hub genes was performed using NetworkAnalyst (Figure 3D). mRNA-TF, mRNA-RBP, mRNA-Drug, and RNA-miRNA networks In the mRNA-miRNA network, 28 miRNAs were found to regulate CD47 expression (hsa-miR-15a-5p, hsa-miR-16-5p, hsa-miR-34a-5p, hsa-miR-182-5p, hsa-miR-221-3p, hsa-miR-222-3p, hsa-miR-15b-5p, hsa-miR-130a-3p, hsa-miR-141-3p, hsa-miR-195-5p, hsa-miR-200a-3p, hsa-miR-34c-5p, hsa-miR-299-3p, hsa-miR-301a-3p, hsa-miR-130b-3p, hsa-miR-383-5p, hsa-miR-330-3p, hsa-miR-326, hsa-miR-424-5p, hsa-miR-449a, hsa-miR-431-5p, hsa-miR-497-5p, hsa-miR-449b-5p, hsa-miR-425-5p, hsa-miR-454-3p, hsa-miR-340-5p, and hsa-miR-330-5p) (Figure 4A). The transcription factor regulation network (TF-mRNA-Network) revealed that 39 transcription factors interacted with CD47 (LMO2, GATA2, TAL1, ESR1, BRD4, FOXA1, MYB, IRF1, SMAD1, GRHL2, STAG1, POLR2A, GATA1, TCF12, GABPA, MAX, EP300, CBFB, RUNX3, SMARCA4, TFAP2C, HDAC2, RAD21, FLI1, RUNX1, SPI1, EBF1, CDK9, NR3C1, ERG, SNAI2, NFE2, FOXA2, MITF, CEBPA, MED1, TRIM28, and KDM5B) (Figure 4B). A total of 28 RNA-binding proteins regulate CD47 expression according to the mRNA-RBP network (FMR1, FXR1, HNRNPA1, HNRNPKH, NRNPL, HNRNPU, IGF2BP2, IGF2BP3, KHDRBS1, KHDRBS2, MBNL1, MSI1, PTBP1, PUM2, QKI, SRSF1, SRSF10, SRSF9, TARDBP, TIA1, and U2AF2) (Figure 4C). The prediction of drug-mRNA interaction showed that CD47 had five drug targets (ABT-510, ALX148, TTI-621, CC-90002, and Hu5F9-G4) (Figure 4D). Differential gene expression and functional enrichment analysis of CD47 low-and high-expression groups. We also performed DEGs with GO terms and KEGG pathways to explore the biological processes, molecular functions, cellular components, biological pathways, and diseases of CD47 in colorectal cancers (Figure 5; Table 1). The analysis showed enrichment primarily in the antimicrobial humoral response, intestinal cholesterol absorption, intestinal lipid absorption biological process, blood microparticles, nucleosomes, chylomicron cellular components, taste receptor activity, and lipase inhibitor activity molecular function (Figure 5A, C). Next, we performed pathway enrichment analysis for taste transduction, alcoholism, fat digestion and absorption, systemic lupus erythematosus, cholesterol metabolism, neuroactive ligand-receptor interaction, estrogen signaling pathway, and more (Figure 5B, D).Pathway analysis are involved in fat and cholesterol metabolism. Cholesterol metabolism has been linked with the risk of colorectal cancer.These findings accord with the functions of intestinal epithelial cells. Differentially expressed genes between CD47 low- and high-expression groups analyzed by GSEA. To explore the function of CD47 in patients with CRC, we first analyzed GSEA for TCGA-COADREAD. We identified CD47 in colorectal cancer mainly enriched in MAP2K and MAPK activation, vitamin B12 metabolism, IL27 pathway, NOTCH signaling, and JAK/STAT signaling pathways from the enrichment map (Figure 6, Table 2). Clinical significance of CD47 in colorectal cancer To confirm the clinical relevance of this study, we analyzed the differences in clinical parameters with differential expression of CD47 in colorectal cancer (Figure 7). In the neoplasm type, CD47 expression was significantly higher in colon adenocarcinoma patients than in rectal adenocarcinoma patients (P = 0.029; Figure 7A). In PFI events, CD47 expression was significantly higher in live patients than in dead patients (P = 0.018; Figure 7B). CD47 expression did not differ in the other clinical characteristics (Figure 7C–F). The prognostic ability of CD47 was explored in different subgroups based on clinical features. Subgroup analysis showed that male patients with high CD47 expression had an improved overall survival (P = 0.014; Figure 8A). We did not identify any other subgroups of patients that showed differential survival (Figure 8B–F). Constructing a prognostic model of CD47 To identify prognostic factors for overall survival, univariate and multivariate (Figure 9A–B) logistic regression analyses were performed to identify the clinical parameters associated with CD47. For univariate analysis with Cox regression models, the age, T stage, N stage, M stage, residual tumor, and CEA level were noted to influence overall survival significantly (Figure 9A). Moreover, Cox proportional hazards multivariate regression analysis revealed that age, N stage, and residual tumor were independent risk factors for overall survival (Figure 9B). Prognostic factors that were significantly associated with survival in multivariate analysis were included in the nomogram to construct a prognostic model. We found that the prognostic model of CD47 predicting the probability of patients had the largest impact on 1-, 3-, and 5-year overall survival (Figure 9C). We applied decision curve analysis (DCA) and a calibration curve to assess the prognostic value of the prognostic genes. Finally, the clinical variable model combined with the clinical variable model alone was assessed using DCA (Figure 9D). From the calibration curve, the prognostic model had good predictive validity for 1-, 3-, and 5-year overall survival (Figure 9E–G). The expression of CD47 protein in colorectal cancer tissue and normal tissue estimated with immunohistochemistry The immunohistochemical differences in CD47 genes in carcinoma and adjacent tissues in patients with CRC were obtained from the HPA database (Figure 10). The CD47 protein was highly expressed in colorectal tumor tissues and lowly expressed in normal tissue (Figure 10A–B). Mutation analysis of CD47 in cancer CD47 mutations were identified in human cancers using the cBioPortal database. The CD47 gene has been reported to be mutated in lung squamous cell carcinoma, ovarian serous cystadenocarcinomas, and cervical squamous cell carcinoma and 32 other cancers (Figure 11A). In addition, genetic alterations included amplification and deletion of CD47 in colorectal cancer (Figure 11A). The mutation rate was 1.7% for CD47 in all cancer samples (Figure 11B). A total set of 50 mutations were found to be distributed along the whole gene and included 41 missense mutations, 2 truncating mutations, 3 splice site mutations, and 4 site-directed mutations. I153M was found to be a mutational hotspot of CD47 among the 50 mutations (Figure 11C). Methylation analysis of CD47 in colorectal cancer To investigate the relationship between DNA methylation and the CD47 gene, we used the MEXPRESS database to detect methylation levels. Exploratory endpoints included OS, T stage, N stage, M stage, history of colorectal polyps, and sex arranged along a line of CD47 expression. CD47 expression significantly altered DNA methylation in the colon of cancer tissues compared with that in normal tissues (P = 1.943e-9; Figure 12A). Furthermore, based on probe aggregation, the first and second CpG islands were hypermethylated, whereas the other three CpG islands were hypomethylated in CD47 (Figure 12B). Immune infiltration analysis of CD47 To identify the immune infiltration associated with CD47, ssGSEA was used to measure the CD47 infiltration levels of 25 immune cell types. Spearman’s correlation coefficients were computed for CD47 and immune cell infiltration. In the plot, a positive correlation existed between the CD47 expression level and infiltrating levels of aDC (r = 0.219, P < 0.001), macrophages (r = 0.247, P < 0.001), T helper cells (r = 0.466, P < 0.001), Tcm (r = 0.258, P < 0.001), Th1 cells (r = 0.221, P < 0.001), Th2 cells (r = 0.329, P < 0.001), CD8 T cells (r = 0.103, P = 0.009), cytotoxic cells (r = 0.136, P < 0.001), neutrophils (r = 0.166, P < 0.001), T cells (r = 0.19, P < 0.001), and Tgd (r = 0.197, P < 0.001). Moreover, a negative correlation was observed between the CD47 expression level and infiltrating levels of NK CD56 bright cells (r = -0.148), P < 0.001) and pDC (r = -0.132, P < 0.001). However, other immune cell types did not display any significant correlation with CD47 expression (Figure 13A–G). Discussion CD47 is an innate immune checkpoint gene[2,6]involved in cell proliferation, apoptosis, and adhesion[28]. Several previous studies have focused on the interaction of the immune system and CRC. However, the exact underlying mechanisms remain unknown. In this study, we found that CD47 was highly expressed in colorectal cancer from the TCGA-COADREAD and GSE25070 datasets. Our data indicated that CD47 has relatively high sensitivity and specificity for diagnostic performance, with an AUC of 0.819. Male patients with high CD47 expression showed improved overall survival compared with female patients. The exact reason for this finding remains unclear. These results require more experimental data for validation and more support from clinical data. Accumulating evidence indicates that CD47 is highly expressed in various tumors and is associated with poor prognosis in patients[3-4]. However, in this study, CRC patients with higher CD47 expression had better PFI. The OS and DSS did not demonstrate significant differences, possibly because our study included two tumors (colon and rectal cancer). Another possible reason is that unknown factors are likely to influence survival. Further molecular and cellular studies are required to determine whether CD47 is an indicator of a good prognosis. This novel study revealed the potential diagnostic and prognostic value of CD47. We thoroughly investigated the correlation between CD47 expression and the clinicopathological parameters of CRC patients. Our Study have shown that CD47 expression is higher in colon adenocarcinoma than in rectal adenocarcinoma. Univariate and multivariate assays revealed that age, N stage, and residual tumors were independent risk factors for overall survival. In a similar study, higher CD47 expression was observed in endometrial carcinoma patients with later pathological stages compared to the early stages[29]. Our results demonstrated that CRC patients with high CD47 expression were enriched in the IL27, NOTCH, and JAK/STAT signaling pathways. These pathways are linked to autoimmunity and inflammation. This finding further confirms CD47 as a immunomodulatory factor. Previous studies have revealed that the TNF-NFKB1 signaling pathway directly regulates CD47 expression in breast cancer[30]. In addition, the PI3K/Akt/mTOR signaling pathway regulates CD47 in endometrial carcinoma[29]and glioblastoma[31]. Therefore, it is particularly interesting to consider the molecular mechanisms underlying the abnormal expression of CD47 in CRC. We predicted five drug targets (ABT-510, ALX148, TTI-621, CC-90002, and Hu5F9-G4) in CD47. Hu5F9-G4 and CC-90002 are anti-CD47 antibodies, whereas TTI-621 and ALX148 are SIRPα-Fc fusion proteins[32]. These drugs are currently being evaluated in phase I clinical trials. This would provide a further theoretical basis and potential targets for immunotherapy of CRC. In this study, we observed a positive correlation between CD47 expression and infiltrating levels of aDC, macrophages, T helper cells, Tcm, Th1 cells, Th2 cells, CD8 T cells, cytotoxic cells, neutrophils, T cells, and Tgd. Previous studies have shown that CD47/SIRPα affects CD8+ T-cell proliferation and macrophage phagocytosis[32,33]. A better understanding of the mechanisms that allow tumor cells to avoid immune clearance requires further investigation. However, this study has some limitations. First, the statistical power might be low because of the small sample size available using the TCGA-COADREAD dataset. Second, even though bioinformatic analysis is a powerful tool, further experimental validation of these findings is needed at the organismal, molecular, and cellular levels. Conclusion In conclusion, high CD47 expression was an indicator of a better PFI in CRC making it a potential prognostic marker. In addition, CD47 expression was positively correlated with infiltration of multiple types of immune cells. CD47 is a prominent new target for cancer immunotherapy. Further research is required to improve our understanding of the biological impact of CD47. Declarations Acknowledgments We thank all those specialists not as co-authors in this study for professional comments and critical thoughts on the manuscript. Authors’ Contributions Chuanshu Cai and Feng Dong conceived and designed the study. All authors participated in the acquisition, analysis, and interpretation of data; Peirong Wang, Chunlin Ke and Minmin Shen drafted the manuscript. All authors read and approved the manuscript. Funding Leading Project Foundation of Science and Technology,Fujian Province(No.2021Y0015); Joint Funds for the Innovation of Science and Technology,Fujian Province(No.2019Y9015). Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Availability of data and materials The datasets(TCGA-COADREAD/GSE25070)for this study can be found in the TCGA and GEO databases(https://www.cancer.gov/; http://www.ncbi.nlm.nih.gov/geo). References Baidoun F, Elshiwy K, Elkeraie Y, et al. Colorectal Cancer Epidemiology: Recent Trends and Impact on Outcomes. Curr Drug Targets 2021;22(9):998-1009; doi: 10.2174/1389450121999201117115717. Liu X, Pu Y, Cron K, et al. CD47 blockade triggers T cell-mediated destruction of immunogenic tumors. 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Chen Y, Wang X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res 2020; 48(D1):D127-D131; doi: 10.1093/nar/gkz757. Sticht C, De La Torre C, et al. miRWalk: An online resource for prediction of microRNA binding sites. PLoS One 2018; 13(10):e0206239; doi: 10.1371/journal.pone.0206239. Wagner AH, Coffman AC, Ainscough BJ, et al. DGIdb 2.0: mining clinically relevant drug-gene interactions. Nucleic Acids Res 2016; 44(D1):D1036-44; doi: 10.1093/nar/gkv1165. Yu G. Gene Ontology Semantic Similarity Analysis Using GOSemSim. Methods Mol Biol 2020;2117:207-215; doi: 10.1007/978-1-0716-0301-7_11. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000; 28(1):27-30; doi: 10.1093/nar/28.1.27. Yu G, Wang LG, Han Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 2012; 16(5):284-7; doi: 10.1089/omi.2011.0118. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005; 102(43):15545-50; doi: 10.1073/pnas.0506580102. Liberzon A, Birger C, Thorvaldsdóttir H, et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 2015; 1(6):417-425; doi: 10.1016/j.cels.2015.12.004. Colwill K; Renewable Protein Binder Working Group, Gräslund S. A roadmap to generate renewable protein binders to the human proteome. Nat Methods 2011; 8(7):551-8; doi: 10.1038/nmeth.1607. Cerami E, Gao J, Dogrusoz U, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012; 2(5):401-4; doi: 10.1158/2159-8290.CD-12-0095. Koch A, Jeschke J, Van Criekinge W, et al. MEXPRESS update 2019. Nucleic Acids Res 2019; 47(W1):W561-W565; doi: 10.1093/nar/gkz445. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013; 14:7; doi: 10.1186/1471-2105-14-7. Tan M, Zhu L, Zhuang H, et al. Lewis Y antigen modified CD47 is an independent risk factor for poor prognosis and promotes early ovarian cancer metastasis. Am J Cancer Res 2015; 5(9):2777-87. Liu Y, Chang Y, He X, et al. CD47 Enhances Cell Viability and Migration Ability but Inhibits Apoptosis in Endometrial Carcinoma Cells via the PI3K/Akt/mTOR Signaling Pathway. Front Oncol 2020; 10:1525; doi: 10.3389/fonc.2020.01525. Betancur PA, Abraham BJ, Yiu YY, et al. A CD47-associated super-enhancer links pro-inflammatory signalling to CD47 upregulation in breast cancer. Nat Commun 2017; 8:14802; doi: 10.1038/ncomms14802. Liu X, Wu X, Wang Y, et al. CD47 Promotes Human Glioblastoma Invasion Through Activation of the PI3K/Akt Pathway. Oncol Res 2019; 27(4):415-422; doi: 10.3727/096504018X15155538502359. Zhang W, Huang Q, Xiao W, et al. Advances in Anti-Tumor Treatments Targeting the CD47/SIRα Axis. Front Immunol 2020; 11:18; doi: 10.3389/fimmu.2020.00018. Tseng D, Volkmer JP, Willingham SB, 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 2013; 110(27):11103-8; doi: 10.1073/pnas.1305569110. Tables Table 1. GO terms and KEGG pathways enrichment analysis. ONTOLOGY ID Description pvalue p.adjust qvalue BP GO:0019730 antimicrobial humoral response 1.33E-09 3.94E-06 3.45E-06 BP GO:0030299 intestinal cholesterol absorption 2.83E-08 4.18E-05 3.66E-05 BP GO:0098856 intestinal lipid absorption 6.84E-08 4.27E-05 3.75E-05 CC GO:0072562 blood microparticle 2.39E-10 7.34E-08 6.26E-08 CC GO:0000786 nucleosome 2.91E-07 4.47E-05 3.81E-05 CC GO:0042627 chylomicron 5.87E-07 4.65E-05 3.97E-05 MF GO:0033038 bitter taste receptor activity 6.77E-10 3.18E-07 2.62E-07 MF GO:0008527 taste receptor activity 5.50E-09 1.29E-06 1.07E-06 MF GO:0055102 lipase inhibitor activity 3.21E-06 0.000424773 0.000350093 KEGG hsa04742 Taste transduction 1.26E-07 2.56E-05 2.39E-05 KEGG hsa05034 Alcoholism 1.05E-05 0.001065995 0.000994966 KEGG hsa04975 Fat digestion and absorption 6.31E-05 0.00364268 0.00339996 KEGG hsa05322 Systemic lupus erythematosus 7.18E-05 0.00364268 0.00339996 KEGG hsa04979 Cholesterol metabolism 0.00014988 0.006085135 0.00567967 KEGG hsa04080 Neuroactive ligand-receptor interaction 0.000427394 0.014460157 0.013496646 KEGG hsa04915 Estrogen signaling pathway 0.001844776 0.053498491 0.049933774 Table 2. Gene set enrichment analysis. ID enrichmentScore NES pvalue p.adjust qvalues REACTOME_MAP2K_AND_MAPK_ACTIVATION -0.505997347 -1.851395301 0.001934236 0.024247073 0.019871738 WP_VITAMIN_B12_METABOLISM -0.59238713 -2.287913317 0.001937984 0.024247073 0.019871738 PID_IL27_PATHWAY 0.615702095 2.032879809 0.001956947 0.024247073 0.019871738 WP_NOTCH_SIGNALING -0.476515138 -1.797433284 0.003868472 0.038477455 0.031534277 KEGG_JAK_STAT_SIGNALING_PATHWAY 0.360016559 1.689798021 0.004149378 0.038581076 0.031619199 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1934531","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":175775829,"identity":"b095b282-47b1-4b5e-aa70-6cbf9a1646af","order_by":0,"name":"Chuanshu Cai","email":"","orcid":"","institution":"The First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chuanshu","middleName":"","lastName":"Cai","suffix":""},{"id":175775830,"identity":"bdd57858-8423-4e59-aa32-219306fbe002","order_by":1,"name":"Peirong Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Peirong","middleName":"","lastName":"Wang","suffix":""},{"id":175775831,"identity":"06ba682c-3535-4a63-8da1-abe94ba219fd","order_by":2,"name":"Chunlin Ke","email":"","orcid":"","institution":"The First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chunlin","middleName":"","lastName":"Ke","suffix":""},{"id":175775832,"identity":"fbc7e8f2-237a-4dc3-ba34-acae2fe9bc4b","order_by":3,"name":"Minmin Shen","email":"","orcid":"","institution":"The First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Minmin","middleName":"","lastName":"Shen","suffix":""},{"id":175775833,"identity":"7643cee4-cb9a-44e6-80c6-705f20b76081","order_by":4,"name":"Feng Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDCCA0BcAWLw8D///KOCgYc4LWfAWnjYmIEsUrUwthHhLr7jvYdfHKi4Y9fPc/bY48J5dTLm7AcYP3zMwa1F8sy5NIsDZ54lz+ztSzeeue0wj2VPArPkzG24tRjcyDEz/th2ONngPIOBBO+2AzwGBxLYmHkJaDE4+A+mZU4dj8H5BwS1GD842HDYzuBsj5k0bwMzj8ENArZInjljxnDg2OEEyZ5jyYYzjh0GannYjNcvfMd7jD8cqDlsz8+TfPDBh5o6e4PzyQc/fMSjBQjYJIBEYgNCgLEBh0o4YP4AJOwJqRoFo2AUjIIRDADtUFyzSkxtzwAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Fujian Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2022-08-06 01:14:11","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-1934531/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-1934531/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":32917891,"identity":"fed305ac-ec4e-410b-b355-92c80247741c","added_by":"auto","created_at":"2023-02-14 15:17:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50430,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTechnology Roadmap\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-1934531/v2/23b0585eae717db83dc9b5a6.png"},{"id":32917890,"identity":"e88f927d-b2cb-4173-aace-a2b07f89cbc3","added_by":"auto","created_at":"2023-02-14 15:17:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":340746,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression and diagnostic and prognostic characteristics of CD47. \u003c/strong\u003e(A) Box plots for CD47 expression in pan-cancer; (B–C) The expression levels of CD47 in unpaired and paired colorectal cancer using box plots; (D) ROC curve analysis of CD47 expression in CRC; (E–G) Clinical survival outcome of OS, DSS, and PFI were derived for different CD47 expression groups in colorectal cancer by KM curve analysis; (H) Differential expression of CD47 in validation GSE25070 datasets; (I) Volcano plot showing differential expression of genes in CD47-high vs. CD47-low patients; (J) CD47-gene expression correlation heat map. *P\u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001. A significant difference was considered when P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-1934531/v2/1e65b7878263f923b40ed975.png"},{"id":32923121,"identity":"d21c05bb-6e84-43c3-a48a-c6a4bd35abb4","added_by":"auto","created_at":"2023-02-14 15:49:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":534220,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI network.\u003c/strong\u003e(A) PPI network of differentially expressed genes associated with CD47 expression; (B) Hub genes of PPI network associated with CD47 expression; (C) Top 20 hub genes of PPI; (D) Visualization of 20 hub genes using NetworkAnalyst.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-1934531/v2/c68a6da75a44556612ae2d7f.png"},{"id":32919504,"identity":"e8852ee5-de30-444b-b67e-0ef35b8b1bf9","added_by":"auto","created_at":"2023-02-14 15:25:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":288303,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCD47 network. \u003c/strong\u003e(A) CD47-miRNA network; (B) CD47-TF network; (C) CD47-RBP network; (D) CD47-drug network. Pink boxes represent miRNAs, yellow diamonds represent transcription factors, purple octagons represent RBP, and green hexagons represent drugs.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-1934531/v2/1b23276163a0e17804b2af28.png"},{"id":32917894,"identity":"8df6639d-a763-4083-8836-a3472753dfac","added_by":"auto","created_at":"2023-02-14 15:17:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":489879,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential gene expression associated with CD47 and functional enrichment analysis. \u003c/strong\u003e(A) Bubble chart showing GO enrichment analysis; (B) Bubbles of KEGG pathways for differential gene enrichment, the number of genes enrichment and pathways are indicated by the node size; redder colors signify higher P values; (C) GO analysis chord diagram; (D) KEGG analysis chord diagram.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-1934531/v2/3756153c56eb0887179d033f.png"},{"id":32920916,"identity":"965e4504-4b66-43d9-a281-887baac2111f","added_by":"auto","created_at":"2023-02-14 15:33:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":229510,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment plots from GSEA.\u003c/strong\u003e (A) mountain map; (B) MAP2K and MAPK activation; (C) vitamin B12 metabolism; (D) IL27 pathway; (E) NOTCH signaling; (F) JAK/STAT signaling pathway.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-1934531/v2/1b65031792a155cd0db078ab.png"},{"id":32919506,"identity":"f171348c-bae5-49c6-9e72-77db9a176355","added_by":"auto","created_at":"2023-02-14 15:25:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":98475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCD47 expression status in different clinical characteristics. \u003c/strong\u003e(A–B) High CD47 expression was associated with colon adenocarcinoma and alive patients in Neoplasm type and PFI event. (C–F) There was no statistically significant difference between CD47 expression and pathologic, T, M, and N stages. *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001. A significant difference was considered when P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-1934531/v2/ff682b038475924085b7d10f.png"},{"id":32917902,"identity":"5dd37296-c74f-4f41-bfcc-88aaca550c59","added_by":"auto","created_at":"2023-02-14 15:17:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":174195,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier survival analysis evaluating CD47 high-and low-expression groups with colorectal cancer according to different clinical subgroup characteristics. \u003c/strong\u003e(A) Gender: Male; (B) M stage: M0; (C) N stage：N0; (D) Age: ≤ 65; (E) CEA level: ≤ 5; (F) Perineural invasion: No. P \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-1934531/v2/096054b1f83779f2c287ed00.png"},{"id":32917901,"identity":"4cf1dc77-4b51-4bb2-9d45-abdb1ac906fb","added_by":"auto","created_at":"2023-02-14 15:17:11","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":295640,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCD47 and OS prognostic model in colorectal cancer.\u003c/strong\u003e (A) univariate analysis; (B) multivariate analysis; (C) nomogram analysis; (D) DCA; (E) one-year calibration curve; (F) three-year calibration curve; (G) five-year calibration curve.\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-1934531/v2/02ae524107335b897404a0ef.png"},{"id":32920917,"identity":"718d68f1-e1f7-4337-a57e-7a96cc5ca239","added_by":"auto","created_at":"2023-02-14 15:33:11","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":316183,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenome mutations of CD47. \u003c/strong\u003e(A–B) Frequencies and genetic alterations of CD47 in all cancers; (C) Structural regions of the protein mutations of CD47 in almost all types of human cancer.\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-1934531/v2/02950223f25c29a27ffc2057.png"},{"id":32917895,"identity":"99fd87b6-0027-440b-9b39-bcae41a40f42","added_by":"auto","created_at":"2023-02-14 15:17:11","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":160403,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCD47 methylation in colon cancer.\u003c/strong\u003e (A) Genetic alterations of CD47 in colon cancer analyzed using the MEXPRESS database; (B) probe of a targeted genomic region in colon cancer.\u003c/p\u003e","description":"","filename":"Fig12.png","url":"https://assets-eu.researchsquare.com/files/rs-1934531/v2/2640df782d767bef261bf3f0.png"},{"id":32922094,"identity":"358c8532-c184-4b3e-a0da-151c9f68f5ca","added_by":"auto","created_at":"2023-02-14 15:41:11","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":394057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between CD47 expression and immune cell infiltrates.\u003c/strong\u003e (A) Lollipop plot showing the distribution of different immune cells associated with CD47. A positive correlation scatter plot existed between the CD47 expression level and infiltrating levels of aDC (B) macrophages (C), T helper cells (D), Tcm (E), Th1 cells (F), and Th2 cells (G). P \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e","description":"","filename":"Fig13.png","url":"https://assets-eu.researchsquare.com/files/rs-1934531/v2/59ca5b7a38dd20bb49fdc6ac.png"},{"id":34947574,"identity":"b5ad5c08-c31a-422e-b980-6d384be67bb4","added_by":"auto","created_at":"2023-03-28 23:59:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3706537,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1934531/v2/f6fe09d2-abf1-4fbc-99a6-e88f461473cf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CD47 is correlated with immune infiltration and is a prognostic biomarker in colorectal cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eExacerbated by the aging of the general population, tumors are becoming an increasingly serious threat to human health. Colorectal cancer (CRC) is the third most common type of cancer and the second leading cause of cancer-related deaths worldwide, with 1.8 million newly diagnosed cases and approximately 881,000 deaths worldwide in 2018[1]. The main strategies for colorectal cancer treatment are surgery, radiation therapy (or chemoradiation), and chemotherapeutic treatment. However, these treatments do not result in satisfactory therapeutic outcomes. \u0026nbsp;Immunotherapy using programmed death-1 (PD-1) inhibitors, have proven promising new treatments. Similar to PD-1 inhibitors, targeting the cluster of differentiation 47 (CD47) is a novel immunotherapeutic strategy for treating human cancers.\u003c/p\u003e\n\u003cp\u003eCD47 is a cell surface molecule that inhibits the phagocytosis of cells that express it by binding to its receptor, signal-regulatory protein alpha (SIRP\u0026alpha;), on macrophages, and other immune cells. Thus, CD47 is an innate immune checkpoint and a promising diagnostic and therapeutic target[2]. CD47 is expressed at different levels in neoplastic and normal cells. Correspondingly, high CD47 expression is associated with clinical prognosis in patients with non-small cell lung cancer[3], melanoma[4], and oral squamous cell carcinoma[5]. However, the critical role of CD47 in colorectal cancer and its association with tumor immune infiltration remains unclear.\u003c/p\u003e\n\u003cp\u003eAs in previous biological information analyses[6], CD47 has been identified as an immune checkpoint gene. However, this study did not focus on immune cells for further analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, to evaluate the expression, characteristics, and function of CD47, we integrated bioinformatic methods to explore the CD47 gene in CRC. In particular, the clinical features, diagnostic characteristic, immune infiltration properties, and prognostic values of CD47 in CRC were determined.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eData acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe collected gene expression data from 698 colon and rectal cancer (COADREAD) patients from the Cancer Genome Atlas (TCGA, https://www.cancer.gov/). RNA sequencing (RNA-seq) data with insufficient clinical information were excluded from this analysis, and third-generation high-throughput sequencing (HTSeq) data transformation into transcripts per million (TPM) and filtration were applied before further analysis. According to the median expression level of CD47, the cells were separated into high and low expression groups. TCGA-COADREAD data were used as a testing dataset.\u003c/p\u003e\n\u003cp\u003eThe gene expression profile GSE25070 of colorectal cancer patients was downloaded from the Gene Expression Omnibus (GEO) database[7] and used as the validation set. GSE25070 dataset samples were derived from humans (Homo sapiens), based on the GPL570 platform, and included both tumor and matched adjacent normal tissue samples.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of differentially expressed genes (DEGs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential expression analysis of TCGA-COADREAD over-expression and under-expression count data was performed using the R package DESeq2[8]. The cut-off threshold for differentially expressed genes was |log fold change (FC)| \u0026ge; 1 and an adjusted P-value \u0026lt; 0.05. Genes with logFC \u0026ge;1 and P-value \u0026lt; 0.05 were considered upregulated genes, and those with logFC \u0026le; -1 and P-value \u0026lt; 0.05 were considered downregulated genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein-protein interaction (PPI) network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Search Tool for the Retrieval of Interacting Genes (STRING) database focuses on known and predicted PPI networks[9](https://cn.string-db.org/). Cytoscape software[10]was used to analyze PPI networks and the interactions of the candidate DEG encoding proteins in CRC. Using the plugin Cytohubba[11]in Cytoscape software, the top 20 hub genes were identified from the PPI network. The PPI network was visualized and analyzed using the NetworkAnalyst[12]platform. We imported the top 20 hub genes into NetworkAnalyst (confidence score cut-off of 900) and visualized the key gene interaction networks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of mRNA-TF, mRNA-RBP, mRNA-Drug, and mRNA-miRNA network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST)[13](https://www.grnpedia.org/trrust/), a manually curated database of human transcriptional regulatory networks, contains not only the target gene of transcription factors but also potential interactions between transcription factors. Post-transcriptional regulation(POSTAR)3[14](http://111.198.139.65/) is a database that uses public large-scale crosslinking immunoprecipitation (CLIP)-seq data and external functional genome annotations, which provides binding sites for RNA-binding proteins, their functional variants, and regulatory mechanisms. The starBase database[15]contains more than 700 CLIP-seq datasets, current degradome data, multiple types of omics data assisted miRNA target prediction, many visualizations of miRNA target interfaces, and analyzed interactions between lncRNA, circRNA, protein and mRNA, or ceRNA mechanism of 23 species.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe miRDB[16]is an online database for miRNA target prediction and provides information on the miRNA of humans, mice, and other species. miRWalk[17]is a comprehensive atlas of predicted miRNA target databases of conserved human, mouse, rat, dog, cow, and many other species. The Drug Gene Interaction Database (DGIdb)[18]is a database of integrated drug-gene interaction information and association information of known or potential drugs with genes.\u003c/p\u003e\n\u003cp\u003eTo demonstrate the relationship between CD47 and other target genes, we predicted CD47 transcription factors using the TRRUST database. In addition, we utilized the starBase and POSTAR3 databases to predict CD47 RNA Binding proteins. The CD47 miRNA was predicted using three miRNA target prediction databases: miRWalk, miRDB, and starBase. DGIdb was used to predict potentially effective therapeutic targets for CD47 and was visualized using Cytoscape software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) \u0026nbsp; pathways enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO[19]analysis included biological process (BP), molecular function (MF), and cellular component (CC). KEGG[20]is a widely used database that collects information on genomes, biological pathways, diseases, and drugs. GO terms and KEGG pathway enrichment analyses of CD47 were performed using clusterProfiler[21]A false discovery rate (FDR) threshold \u0026lt;\u0026thinsp;0.05 was considered statistically significant. The differentially expressed genes were screened with an adj P-value \u0026lt; 0.05 and a Q-value \u0026lt; 0.05. P-values were corrected using the Benjamini and Hochberg (BH) method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGSEA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGSEA[22]is a method that determines whether an a priori defined set of genes shows statistically significant expression differences between two biological states. In this study, the groups were divided according to median CD47 expression in the colorectal cancer database. The R package \u0026ldquo;clusterProfiler\u0026rdquo; was used to perform GSEA between the CD47 high- and low-expression groups in the COADREAD patients. GSEA was carried out using the following parameters (seed = 2020, calculated number = 1000, gene number per gene data = 10\u0026ndash;500). P-values were corrected using the Benjamini and Hochberg (BH) method. The reference gene set used in GSEA was obtained from the Molecular Signatures Database (c2.all.v6.2. symbols.gmt)[23]. Enrichment was considered significant if FDR \u0026lt; 0.25 and P \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemistry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein expression patterns in normal human and tumor tissues were used to generate an expression map using data from The Human Protein Atlas[24](https://www.proteinatlas.org/). Next, we used HPA databases, to compare CRC and normal tissue CD47 protein expression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome and methylation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cBio Cancer Genomics Portal (cBioPortal) is an open-access Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data[25]TCGA data for clinical information, gene expression, and DNA methylation were visualized using MEXPRESS data[26](http://mexpress.be/). To explore CD47 mutations, genetic alteration of CD47 was analyzed in cBioPortal and further analyzed for CD47 mutation and expression in pan-cancer (TCGA). MEXPRESS was used to explore DNA variations and precise locations of mutations in the CD47 gene in colon and rectal cancer, analyzed CD47 methylation in pan-cancer, and evaluate relationships between multiple clinical variables and CD47 expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune infiltration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmune cells, inflammatory cells, fibroblasts, interstitial tissues, cytokines, and chemokines are important components of the immune microenvironment. Immune infiltration analysis is critically important for understanding, predicting, and treating diseases. Single-sample gene set enrichment analysis (ssGSEA) was performed using the gene set variation analysis (GSVA)[27]R package to evaluate CD47 immune infiltration. Immune cell infiltration was visualized. Statistical significance was set at P \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using R statistical software (v3.6.3). The relationship between CD47 expression and clinicopathological features was analyzed using the t-test. The Kaplan\u0026ndash;Meier method was used to estimate overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS). Univariate and multivariate Cox proportional-hazard models were used for CD47 expression of survival and other clinical characteristics (age, T stage, M stage, N stage, residual tumor, and carcinoembryonic antigen(CEA) level). The cutoff value was set as the median value of CD47 expression level. In all tests, statistical significance was set at P \u0026lt; 0.05. CD47 high- and low-expression groups of OS, PFI, and DSS were estimated using the Kaplan\u0026ndash;Meier method and two-sided log-rank test.\u003c/p\u003e\n\u003cp\u003eThe nomograms were carried out as independent predictors using multivariate analysis based on the Cox regression hazard model. Survival probabilities for individualized survival were predicted for 1-, 3-, and 5-year survival rates. Nomograms containing clinical characteristics and calibration plots were generated with the relapsing multiple sclerosis (RMS) package in R. Using the prediction model nomogram, the calibration curve contrasts observed probabilities of events using histogram assessment. Diagonal values represent the best predictions. The human discriminative ability nomogram used the concordance index (C index) and bootstrap resampling (1,000 re-samplings). The predictive accuracy was compared using the prognostic index, nomogram, and independent prognostic factors. In our study, all tests were two-sided, and P \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCD47 differential expression in pan-cancer and prognostic capacity in colorectal cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn overview of the workflow is shown in Figure 1. We analyzed the differential expression of CD47 in the 33 cancer types (Figure 2A). We found a significant difference in CD47 expression between cancerous and non-cancerous tissues in BRCA (P \u0026lt; 0.001), CESC (P = 0.009), CHOL (P \u0026lt; 0.001), COAD (P \u0026lt; 0.001), GBM (P = 0.001), HNSC (P \u0026lt; 0.001), KICH (P = 0.007), LIHC (P \u0026lt; 0.001), LUAD (P \u0026lt; 0.001), LUSC (P \u0026lt; 0.001), PRAD (P = 0.004), STAD (P \u0026lt; 0.001), THCA (P \u0026lt; 0.001), and READ (P = 0.007). However, the difference was not statistically significant for BLCA (P = 0.58), ESCA (P = 0.085), KIRC (P = 0.133), KIRP (P = 0.483), PAAD (P = 0.163), PCPG (P = 0.399), READ (P = 0.070), and UCEC (P = 0.078) (Figure 2A). Patients were stratified into high-or low-expression groups according to the median level CD47, and differential expression analysis was performed. We found a significant difference between the over-and-under-CD47 expression groups (P \u0026lt; 0.001) (Figure 2B\u0026ndash;C). The diagnostic value of CD47 in patients with COADREAD was assessed using receiver operating characteristic (ROC) curve analysis. The value for CD47 was 0.819, suggesting a potential diagnostic role for CD47 in the colon and rectal cancer (Figure 2D). Kaplan\u0026ndash;Meier survival analysis was performed to compare the OS, DSS, and PFI of CD47 in CRC. High CD47 expression was associated with an improved PFI in patients with colorectal cancer (P = 0.011). OS (P = 0.083) and DSS (P = 0.072) failed to demonstrate any significant difference in high- CD47 expression group, although there was a trend toward improved survival (Figure 2E\u0026ndash;G). We performed differential expression analysis in CD47 by comparing COADREAD patients with healthy control samples in GSE25070. CD47 expression was significantly higher in cancer patients than in healthy controls using the validation GSE25070 dataset (P = 3.1e-06) (Figure 2H). Differential gene expression analysis was performed between CD47 over-expression and under-expression groups in colorectal cancer. A total of 305 genes were identified as DEGs, including 173 upregulated and 132 downregulated genes (Figure 2I\u0026ndash;J).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of PPI networks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInteractions of differentially expressed genes between groups (high expression vs. low expression) were determined and visualized (Figure 3A\u0026ndash;B). The CytoHubba plugin in Cytoscape was performed to explore the top 20 hub genes in CRC (\u003cem\u003eAURKB, DLGAP5, CEP55, CCNA2, BIRC5, BUBIB, NUSAP1, TTK, UBE2C, KIF4A, TPX2, CDCA8, CDC20, CDK1, KIF20A, NDC80, CCNB1, KIF11, NCAPG, ASPM\u003c/em\u003e) (Figure 3C). Network analysis of the top 20 hub genes was performed using NetworkAnalyst (Figure 3D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emRNA-TF, mRNA-RBP, mRNA-Drug, and RNA-miRNA networks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the mRNA-miRNA network, 28 miRNAs were found to regulate CD47 expression (hsa-miR-15a-5p, hsa-miR-16-5p, hsa-miR-34a-5p, hsa-miR-182-5p, hsa-miR-221-3p, hsa-miR-222-3p, hsa-miR-15b-5p, hsa-miR-130a-3p, hsa-miR-141-3p, hsa-miR-195-5p, hsa-miR-200a-3p, hsa-miR-34c-5p, hsa-miR-299-3p, hsa-miR-301a-3p, hsa-miR-130b-3p, hsa-miR-383-5p, hsa-miR-330-3p, hsa-miR-326, hsa-miR-424-5p, hsa-miR-449a, hsa-miR-431-5p, hsa-miR-497-5p, hsa-miR-449b-5p, hsa-miR-425-5p, hsa-miR-454-3p, hsa-miR-340-5p, and hsa-miR-330-5p) (Figure 4A).\u003c/p\u003e\n\u003cp\u003eThe transcription factor regulation network (TF-mRNA-Network) revealed that 39 transcription factors interacted with CD47 (LMO2, GATA2, TAL1, ESR1, BRD4, FOXA1, MYB, IRF1, SMAD1, GRHL2, STAG1, POLR2A, GATA1, TCF12, GABPA, MAX, EP300, CBFB, RUNX3, SMARCA4, TFAP2C, HDAC2, RAD21, FLI1, RUNX1, SPI1, EBF1, CDK9, NR3C1, ERG, SNAI2, NFE2, FOXA2, MITF, CEBPA, MED1, TRIM28, and KDM5B) (Figure 4B).\u003c/p\u003e\n\u003cp\u003eA total of 28 RNA-binding proteins regulate CD47 expression according to the mRNA-RBP network (FMR1, FXR1, HNRNPA1, HNRNPKH, NRNPL, HNRNPU, IGF2BP2, IGF2BP3, KHDRBS1, KHDRBS2, MBNL1, MSI1, PTBP1, PUM2, QKI, SRSF1, SRSF10, SRSF9, TARDBP, TIA1, and U2AF2) (Figure 4C).\u003c/p\u003e\n\u003cp\u003eThe prediction of drug-mRNA interaction showed that CD47 had five drug targets (ABT-510, ALX148, TTI-621, CC-90002, and Hu5F9-G4) (Figure 4D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential gene expression and functional enrichment analysis of CD47 low-and high-expression groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe also performed DEGs with GO terms and KEGG pathways to explore the biological processes, molecular functions, cellular components, biological pathways, and diseases of CD47 in colorectal cancers (Figure 5; Table 1). The analysis showed enrichment primarily in the antimicrobial humoral response, intestinal cholesterol absorption, intestinal lipid absorption biological process, blood microparticles, nucleosomes, chylomicron cellular components, taste receptor activity, and lipase inhibitor activity molecular function (Figure 5A, C). Next, we performed pathway enrichment analysis for taste transduction, alcoholism, fat digestion and absorption, systemic lupus erythematosus, cholesterol metabolism, neuroactive ligand-receptor interaction, estrogen signaling pathway, and more (Figure 5B, D).Pathway analysis are involved in fat and cholesterol metabolism. Cholesterol metabolism has been linked with the risk of colorectal cancer.These findings accord with the functions of intestinal epithelial cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferentially expressed genes between CD47 low- and high-expression groups analyzed by GSEA.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the function of CD47 in patients with CRC, we first analyzed GSEA for TCGA-COADREAD. We identified CD47 in colorectal cancer mainly enriched in MAP2K and MAPK activation, vitamin B12 metabolism, IL27 pathway, NOTCH signaling, and JAK/STAT signaling pathways from the enrichment map (Figure 6, Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical significance of CD47 in colorectal cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo confirm the clinical relevance of this study, we analyzed the differences in clinical parameters with differential expression of CD47 in colorectal cancer (Figure 7). In the neoplasm type, CD47 expression was significantly higher in colon adenocarcinoma patients than in rectal adenocarcinoma patients (P = 0.029; Figure 7A). In PFI events, CD47 expression was significantly higher in live patients than in dead patients (P = 0.018; Figure 7B). CD47 expression did not differ in the other clinical characteristics (Figure 7C\u0026ndash;F). The prognostic ability of CD47 was explored in different subgroups based on clinical features. Subgroup analysis showed that male patients with high CD47 expression had an improved overall survival (P = 0.014; Figure 8A). We did not identify any other subgroups of patients that showed differential survival (Figure 8B\u0026ndash;F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstructing a prognostic model of CD47\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify prognostic factors for overall survival, univariate and multivariate (Figure 9A\u0026ndash;B) logistic regression analyses were performed to identify the clinical parameters associated with CD47. For univariate analysis with Cox regression models, the age, T stage, N stage, M stage, residual tumor, and CEA level were noted to influence overall survival significantly (Figure 9A). Moreover, Cox proportional hazards multivariate regression analysis revealed that age, N stage, and residual tumor were independent risk factors for overall survival (Figure 9B). Prognostic factors that were significantly associated with survival in multivariate analysis were included in the nomogram to construct a prognostic model. We found that the prognostic model of CD47 predicting the probability of patients had the largest impact on 1-, 3-, and 5-year overall survival (Figure 9C). We applied decision curve analysis (DCA) and a calibration curve to assess the prognostic value of the prognostic genes. Finally, the clinical variable model combined with the clinical variable model alone was assessed using DCA (Figure 9D). From the calibration curve, the prognostic model had good predictive validity for 1-, 3-, and 5-year overall survival (Figure 9E\u0026ndash;G).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe expression of CD47 protein in colorectal cancer tissue and normal tissue estimated with immunohistochemistry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe immunohistochemical differences in CD47 genes in carcinoma and adjacent tissues in patients with CRC were obtained from the HPA database (Figure 10). The CD47 protein was highly expressed in colorectal tumor tissues and lowly expressed in normal tissue (Figure 10A\u0026ndash;B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMutation analysis of CD47 in cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCD47 mutations were identified in human cancers using the cBioPortal database. The CD47 gene has been reported to be mutated in lung squamous cell carcinoma, ovarian serous cystadenocarcinomas, and cervical squamous cell carcinoma and 32 other cancers (Figure 11A). In addition, genetic alterations included amplification and deletion of CD47 in colorectal cancer (Figure 11A). The mutation rate was 1.7% for CD47 in all cancer samples (Figure 11B). A total set of 50 mutations were found to be distributed along the whole gene and included 41 missense mutations, 2 truncating mutations, 3 splice site mutations, and 4 site-directed mutations. I153M was found to be a mutational hotspot of CD47 among the 50 mutations (Figure 11C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethylation analysis of CD47 in colorectal cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the relationship between DNA methylation and the CD47 gene, we used the MEXPRESS database to detect methylation levels. Exploratory endpoints included OS, T stage, N stage, M stage, history of colorectal polyps, and sex arranged along a line of CD47 expression. CD47 expression significantly altered DNA methylation in the colon of cancer tissues compared with that in normal tissues (P = 1.943e-9; Figure 12A). Furthermore, based on probe aggregation, the first and second CpG islands were hypermethylated, whereas the other three CpG islands were hypomethylated in CD47 (Figure 12B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune infiltration analysis of CD47\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify the immune infiltration associated with CD47, ssGSEA was used to measure the CD47 infiltration levels of 25 immune cell types. Spearman\u0026rsquo;s correlation coefficients were computed for CD47 and immune cell infiltration. In the plot, a positive correlation existed between the CD47 expression level and infiltrating levels of aDC (r = 0.219, P \u0026lt; 0.001), macrophages (r = 0.247, P \u0026lt; 0.001), T helper cells (r = 0.466, P \u0026lt; 0.001), Tcm (r = 0.258, P \u0026lt; 0.001), Th1 cells (r = 0.221, P \u0026lt; 0.001), Th2 cells (r = 0.329, P \u0026lt; 0.001), CD8 T cells (r = 0.103, P = 0.009), cytotoxic cells (r = 0.136, P \u0026lt; 0.001), neutrophils (r = 0.166, P \u0026lt; 0.001), T cells (r = 0.19, P \u0026lt; 0.001), and Tgd (r = 0.197, P \u0026lt; 0.001). Moreover, a negative correlation was observed between the CD47 expression level and infiltrating levels of NK CD56 bright cells (r = -0.148), P \u0026lt; 0.001) and pDC (r = -0.132, P \u0026lt; 0.001). However, other immune cell types did not display any significant correlation with CD47 expression (Figure 13A\u0026ndash;G).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCD47 is an innate immune checkpoint gene[2,6]involved in cell proliferation, apoptosis, and adhesion[28]. Several previous studies have focused on the interaction of the immune system and CRC. However, the exact underlying mechanisms remain unknown. In this study, we found that CD47 was highly expressed in colorectal cancer from the TCGA-COADREAD and GSE25070 datasets. Our data indicated that CD47 has relatively high sensitivity and specificity for diagnostic performance, with an AUC of 0.819. Male patients with high CD47 expression showed improved overall survival compared with female patients. The exact reason for this finding remains unclear. These results require more experimental data for validation and more support from clinical data. Accumulating evidence indicates that CD47 is highly expressed in various tumors and is associated with poor prognosis in patients[3-4]. However, in this study, CRC patients with higher CD47 expression had better PFI. The OS and DSS did not demonstrate significant differences, possibly because our study included two tumors (colon and rectal cancer). Another possible reason is that unknown factors are likely to influence survival. Further molecular and cellular studies are required to determine whether CD47 is an indicator of a good prognosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis novel study revealed the potential diagnostic and prognostic value of CD47. We thoroughly investigated the correlation between CD47 expression and the clinicopathological parameters of CRC patients. Our Study have shown that CD47 expression is higher in colon adenocarcinoma than in rectal adenocarcinoma. Univariate and multivariate assays revealed that age, N stage, and residual tumors were independent risk factors for overall survival. In a similar study, higher CD47 expression was observed in endometrial carcinoma patients with later pathological stages compared to the early stages[29]. Our results demonstrated that CRC patients with high CD47 expression were enriched in the IL27, NOTCH, and JAK/STAT signaling pathways. These pathways are linked to autoimmunity and inflammation. This finding further confirms CD47 as a immunomodulatory factor. Previous studies have revealed that the TNF-NFKB1 signaling pathway directly regulates CD47 expression in breast cancer[30]. In addition, the PI3K/Akt/mTOR signaling pathway regulates CD47 in endometrial carcinoma[29]and glioblastoma[31]. Therefore, it is particularly interesting to consider the molecular mechanisms underlying the abnormal expression of CD47 in CRC.\u003c/p\u003e\n\u003cp\u003eWe predicted five drug targets (ABT-510, ALX148, TTI-621, CC-90002, and Hu5F9-G4) in CD47. Hu5F9-G4 and CC-90002 are anti-CD47 antibodies, whereas TTI-621 and ALX148 are SIRP\u0026alpha;-Fc fusion proteins[32]. These drugs are currently being evaluated in phase I clinical trials. This would provide a further theoretical basis and potential targets for immunotherapy of CRC. In this study, we observed a positive correlation between CD47 expression and infiltrating levels of aDC, macrophages, T helper cells, Tcm, Th1 cells, Th2 cells, CD8 T cells, cytotoxic cells, neutrophils, T cells, and Tgd. Previous studies have shown that CD47/SIRP\u0026alpha; affects CD8+ T-cell proliferation and macrophage phagocytosis[32,33]. A better understanding of the mechanisms that allow tumor cells to avoid immune clearance requires further investigation.\u003c/p\u003e\n\u003cp\u003eHowever, this study has some limitations. First, the statistical power might be low because of the small sample size available using the TCGA-COADREAD dataset. Second, even though bioinformatic analysis is a powerful tool, further experimental validation of these findings is needed at the organismal, molecular, and cellular levels.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, high CD47 expression was an indicator of a better PFI in CRC making it a potential prognostic marker. In addition, CD47 expression was positively correlated with infiltration of multiple types of immune cells. CD47 is a prominent new target for cancer immunotherapy. Further research is required to improve our understanding of the biological impact of CD47.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all those specialists not as co-authors in this study for professional comments and critical thoughts on the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChuanshu Cai and Feng Dong conceived and designed the study. All authors participated in the acquisition, analysis, and interpretation of data; Peirong Wang, Chunlin Ke and Minmin Shen drafted the manuscript. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLeading Project Foundation of Science and Technology,Fujian Province(No.2021Y0015); Joint Funds for the Innovation of Science and Technology,Fujian Province(No.2019Y9015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets(TCGA-COADREAD/GSE25070)for this study can be found in the TCGA and GEO databases(https://www.cancer.gov/; http://www.ncbi.nlm.nih.gov/geo).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBaidoun F, Elshiwy K, Elkeraie Y, et al. 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DGIdb 2.0: mining clinically relevant drug-gene interactions. \u003cem\u003eNucleic Acids Res\u0026nbsp;\u003c/em\u003e2016; 44(D1):D1036-44; doi: 10.1093/nar/gkv1165.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYu G. Gene Ontology Semantic Similarity Analysis Using GOSemSim. \u003cem\u003eMethods Mol Biol\u0026nbsp;\u003c/em\u003e2020;2117:207-215; doi: 10.1007/978-1-0716-0301-7_11.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. \u0026nbsp;\u003cem\u003eNucleic Acids Res\u0026nbsp;\u003c/em\u003e2000; 28(1):27-30; doi: 10.1093/nar/28.1.27.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYu G, Wang LG, Han Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters. \u003cem\u003eOMICS\u0026nbsp;\u003c/em\u003e2012; 16(5):284-7; doi: 10.1089/omi.2011.0118.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSubramanian A, Tamayo P, Mootha VK, et al. 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Lewis Y antigen modified CD47 is an independent risk factor for poor prognosis and promotes early ovarian cancer metastasis. \u003cem\u003eAm J Cancer Res\u0026nbsp;\u003c/em\u003e2015; 5(9):2777-87.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLiu Y, Chang Y, He X, et al. CD47 Enhances Cell Viability and Migration Ability but Inhibits Apoptosis in Endometrial Carcinoma Cells via the PI3K/Akt/mTOR Signaling Pathway. \u003cem\u003eFront Oncol\u0026nbsp;\u003c/em\u003e2020; 10:1525; doi: 10.3389/fonc.2020.01525.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBetancur PA, Abraham BJ, Yiu YY, et al. A CD47-associated super-enhancer links pro-inflammatory signalling to CD47 upregulation in breast cancer. \u003cem\u003eNat Commun\u0026nbsp;\u003c/em\u003e2017; 8:14802; doi: 10.1038/ncomms14802.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLiu X, Wu X, Wang Y, et al. CD47 Promotes Human Glioblastoma Invasion Through Activation of the PI3K/Akt Pathway. \u003cem\u003eOncol Res\u0026nbsp;\u003c/em\u003e2019; 27(4):415-422; doi: 10.3727/096504018X15155538502359.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhang W, Huang Q, Xiao W, et al. Advances in Anti-Tumor Treatments Targeting the CD47/SIR\u0026alpha;\u0026nbsp;Axis. \u003cem\u003eFront Immunol\u0026nbsp;\u003c/em\u003e2020; 11:18; doi: 10.3389/fimmu.2020.00018.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTseng D, Volkmer JP, Willingham SB, et al. Anti-CD47 antibody-mediated phagocytosis of cancer by macrophages primes an effective antitumor T-cell response. \u003cem\u003eProc Natl Acad Sci U S A\u0026nbsp;\u003c/em\u003e2013; 110(27):11103-8; doi: 10.1073/pnas.1305569110. \u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. GO terms and KEGG pathways enrichment analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eONTOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003ep.adjust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003eqvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003eGO:0019730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003eantimicrobial humoral response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e1.33E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e3.94E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003e3.45E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003eGO:0030299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003eintestinal cholesterol absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e2.83E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e4.18E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003e3.66E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003eGO:0098856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003eintestinal lipid absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e6.84E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e4.27E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003e3.75E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003eGO:0072562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003eblood microparticle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e2.39E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e7.34E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003e6.26E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003eGO:0000786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003enucleosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e2.91E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e4.47E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003e3.81E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003eGO:0042627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003echylomicron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e5.87E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e4.65E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003e3.97E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003eGO:0033038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003ebitter taste receptor activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e6.77E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e3.18E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003e2.62E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003eGO:0008527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003etaste receptor activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e5.50E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e1.29E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003e1.07E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003eGO:0055102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003elipase inhibitor activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e3.21E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e0.000424773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003e0.000350093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003ehsa04742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003eTaste transduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e1.26E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e2.56E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003e2.39E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003ehsa05034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003eAlcoholism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e1.05E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e0.001065995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003e0.000994966\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003ehsa04975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003eFat digestion and absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e6.31E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e0.00364268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003e0.00339996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003ehsa05322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003eSystemic lupus erythematosus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e7.18E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e0.00364268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003e0.00339996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003ehsa04979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003eCholesterol metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e0.00014988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e0.006085135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003e0.00567967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003ehsa04080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003eNeuroactive ligand-receptor interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e0.000427394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e0.014460157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003e0.013496646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\"\u003e\n \u003cp\u003ehsa04915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.247833622183705%\"\u003e\n \u003cp\u003eEstrogen signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e0.001844776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.344887348353552%\"\u003e\n \u003cp\u003e0.053498491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.95840554592721%\"\u003e\n \u003cp\u003e0.049933774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Gene set enrichment analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.763888888888886%\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.972222222222221%\"\u003e\n \u003cp\u003eenrichmentScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.847222222222221%\"\u003e\n \u003cp\u003eNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003ep.adjust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003eqvalues\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.763888888888886%\"\u003e\n \u003cp\u003eREACTOME_MAP2K_AND_MAPK_ACTIVATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.972222222222221%\"\u003e\n \u003cp\u003e-0.505997347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.847222222222221%\"\u003e\n \u003cp\u003e-1.851395301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e0.001934236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e0.024247073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e0.019871738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.763888888888886%\"\u003e\n \u003cp\u003eWP_VITAMIN_B12_METABOLISM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.972222222222221%\"\u003e\n \u003cp\u003e-0.59238713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.847222222222221%\"\u003e\n \u003cp\u003e-2.287913317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e0.001937984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e0.024247073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e0.019871738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.763888888888886%\"\u003e\n \u003cp\u003ePID_IL27_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.972222222222221%\"\u003e\n \u003cp\u003e0.615702095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.847222222222221%\"\u003e\n \u003cp\u003e2.032879809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e0.001956947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e0.024247073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e0.019871738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.763888888888886%\"\u003e\n \u003cp\u003eWP_NOTCH_SIGNALING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.972222222222221%\"\u003e\n \u003cp\u003e-0.476515138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.847222222222221%\"\u003e\n \u003cp\u003e-1.797433284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e0.003868472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e0.038477455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e0.031534277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.763888888888886%\"\u003e\n \u003cp\u003eKEGG_JAK_STAT_SIGNALING_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.972222222222221%\"\u003e\n \u003cp\u003e0.360016559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.847222222222221%\"\u003e\n \u003cp\u003e1.689798021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e0.004149378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e0.038581076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e0.031619199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CD47, Colorectal cancer, Immune infiltration, Prognostic biomarker, Clinicopathological characteristics","lastPublishedDoi":"10.21203/rs.3.rs-1934531/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1934531/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eColorectal cancer (CRC) is a common malignant neoplasm, and the cluster of differentiation 47 (CD47) is an innate immune checkpoint and promising diagnostic and therapeutic target. We comprehensively examined the potential prognostic value, clinicopathological characteristics, and immune infiltration associated with CD47 in CRC patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn total, 305 differentially expressed genes (DEGs) were identified. The receiver operating characteristic (ROC) curve analysis of CD47 suggested an area under the ROC curve of 0.819. Kaplan–Meier survival analysis indicated that CRC with high CD47 expression had a better prognosis in the progression-free interval (PFI; P = 0.011). Five drug targets (ABT-510, ALX148, TTI-621, CC-90002, and Hu5F9-G4) were identified for CD47. A positive correlation existed between CD47 expression and infiltrating levels of aDC, macrophages, T helper cells, Tcm, Th1 cells, Th2 cells, CD8 T cells, cytotoxic cells, neutrophils, T cells, and Tgd. In the neoplasm type, CD47 expression was higher in colon adenocarcinoma patients than in rectal adenocarcinoma patients (P = 0.029). In PFI events, CD47 expression was higher in live patients than in dead patients (P = 0.018). Male patients with high CD47 expression showed improved overall survival compared with female patients (P = 0.014). CD47 protein was highly expressed in colorectal tumor tissue and lowly expressed in normal tissues in the Human Protein Atlas(HPA). Methylation analysis of CD47 in CRC revealed that the first and second CpG islands were hypermethylated, whereas the third CpG island was hypomethylated. Genetic alterations in CRC included amplification and deletion of CD47 in colorectal cancer. I153M was found to be a mutational hotspot for CD47.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eCD47 is correlated with immune infiltration and is a prognostic biomarker in colorectal cancer.\u003c/p\u003e","manuscriptTitle":"CD47 is correlated with immune infiltration and is a prognostic biomarker in colorectal cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2023-02-14 15:17:05","doi":"10.21203/rs.3.rs-1934531/v2","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}},{"code":1,"date":"2022-08-12 20:02:22","doi":"10.21203/rs.3.rs-1934531/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":"736ff9b4-4dd0-4bb7-b7c0-4a82074af5c0","owner":[],"postedDate":"February 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-03-28T23:59:17+00:00","versionOfRecord":[],"versionCreatedAt":"2023-02-14 15:17:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-1934531","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1934531","identity":"rs-1934531","version":["v2"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}