| Literature DB >> 35433462 |
Xiao Yan1, Binbin Lai1, Xuyan Zhou2, Shujun Yang3, Qunfang Ge1, Miao Zhou1, Cong Shi4, Zhijuan Xu1, Guifang Ouyang1.
Abstract
Myelodysplastic syndrome (MDS) can lead to the development of peripheral blood cytopenia and abnormal cell morphology. MDS has the potential to evolve into AML and can lead to reduced survival. CD47, a member of the immunoglobulin family, is one molecule that is overexpressed in a variety of cancer cells and is associated with clinical features and poor prognosis in a variety of malignancies. In this study, we analyzed the expression and function of CD47 in MDS and AML, and further analyzed its role in other tumors. Our analysis revealed significantly low CD47 expression in MDS and significantly high expression in AML. Further analysis of the function or pathway of CD47 from different perspectives identified a relationship to the immune response, cell growth, and other related functions or pathways. The relationship between CD47 and other tumors was analyzed from four aspects: DNA methyltransferase, TMB, MSI, and tumor cell stemness. Changes in gene expression levels have a known association with aberrant DNA methylation, and this methylation is the main mechanism of tumor suppressor gene silencing and clonal variation during the evolution of MDS to AML. Taken together, our findings support the hypothesis that the differential expression of CD47 might be related to the transformation of MDS to AML.Entities:
Keywords: AML; DNA methylation; cd47; expression; mds
Year: 2022 PMID: 35433462 PMCID: PMC9008711 DOI: 10.3389/fonc.2022.872999
Source DB: PubMed Journal: Front Oncol ISSN: 2234-943X Impact factor: 6.244
Figure 1The expression and mutation of CD47. (A, B) CD47 expression analysis in MDS and normal control samples with datasets GSE30196 and GSE19610; (C, D) CD47 expression in AML and normal control samples using the GSE24395 and GSE30029 data sets. *P < 0.05, **P < 0.01, ***P < 0.001. (E) expression level of CD47 in AML and MDS. (F) Oncoplot shows the somatic landscape of acute myeloid leukemia. (G) Variant classification of CD47 mutations.
Figure 2Results of analysis of variance. (A) analysis of variance for the MDS dataset GSE30195; (B) analysis of variance for the AML dataset GSE30029; (C) Venn diagram for the overlapping genes of the two sets of DEGs.
Figure 3Selection of key gene modules. (A) PPI network of overlapping genes; (B) gene modules with the highest tightness were analyzed by MCODE plug-in (C); functional clustering network map of overlapping genes; (D) correlation analysis of CD47 with 5 key genes in the target database (sgol2 is also called sgO2). *P < 0.05, **P < 0.01.
Figure 4Gene interaction network and enrichment analysis. (A) Construction of a PPI network with 21 genes centered on CD47; (B) Top 10 GO functional terms; (C) Top 10 KEGG pathways.
Figure 5GSEA analysis of CD47 Hallmark and KEGG pathways. (A) Hallmark and (B) KEGG pathways of CD47 in MDS tissues from the GSE30195 dataset, and (C) hallmark and (D) KEGG pathways of CD47 in AML tissues from the GSE30029 dataset.