BACKGROUND:Immunogenic cell death (ICD)is a kind of regulatory cell death, which causes a series of antigen-specific adaptive immune responses by generating and emitting some danger signals or damage-associated molecular patterns (DAMPs). At present, little is known about the prognostic value of ICD and its related processes in acute myeloid leukemia (AML). The aim of the study was to explore the relationship between ICD and tumor immune microenvironment changes in AML.RESEARCH DESIGN & METHODS:In the study, AML samples were divided into two groups by consensus clustering analysis, and then gene enrichment analysis and GSEA analysis were performed on the ICD high expression group. Furthermore, CIBERSORT was used to analyze the tumor microenvironment and immune characteristics of AML. Finally, a prognostic model related to ICD was constructed by using univariate and multivariate regression analysis.RESULTS:ICD was divided into two groups according to the level of ICD gene expression. The ICD high expression group was associated with good clinical results and high levels of immune cell infiltration.CONCLUSIONS:The study constructed and verified the prognostic characteristics of AML related to ICD, which has important value in predicting the overall survival time of AML patients.

Objective:To explore the key genes related to the development, progression and prognosis of acute myeloid leukemia (AML) based on bioinformatics, and to analyze their functions.Methods:The chip expression profile GSE84881 data set of AML patients including 19 AML samples and 4 normal tissue samples was downloaded from the gene expression omnibus (GEO) database. GEO online tool GEO2R was used to screen the differentially expressed genes (DEG). The DAVID online database was used to make gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of DEG. The STRING online database was used to analyze the protein interaction (PPI) network of DEG, and the key genes were screened by using the Cytoscape software. The weighted gene co-expression network analysis (WGCNA) was used to build co-expressed network and obtain the central genes.LC-Bio online platform was used to construct Venn diagram and the key genes and central genes in PPI were crossed to finally obtain the true key genes. RNA-seq datasets GSE2191 and GSE90062 of human tissues were downloaded from GEO database to verify the screened key genes. Kaplan-Meier method was used to analyze the effects of key genes on the overall survival (OS) of AML based on the data of GEPIA database.Results:A total of 247 DEG were identified in GSE84881 data set, including 112 up-regulated genes and 135 down-regulated genes. According to the results of GO enrichment analysis, 247 DEG were mainly enriched in the regulation of signal transduction and cell proliferation in the biological process (BP); the cell composition (CC) revealed that these genes were mainly involved in the cytoplasm and exosomes; the molecular function (MF) analysis showed that these genes were mainly enriched in protein binding and calcium binding. Further KEGG pathway enrichment analysis showed that these 247 DEG were mainly involved in NOD-like receptor signal pathway and interleukin 17 (IL-17) signal pathway. And then the 12 key genes were obtained from PPI. WGCNA software was used to screen 13 central genes from GSE84881 dataset and finally 1 real key gene EGF was obtained after taking intersection. Kaplan-Meier method showed that OS time of AML patients in EGF high expression group was decreased than that in EGF low expression group, and the difference was statistically significant( P = 0.044). Conclusions:EGF may be an important diagnosis and treatment target of AML and may become a potential biomarker for clinical treatment and prognosis prediction of AML.

Objective:To study the differentially expressed genes between multiple myeloma and healthy whole blood samples by bioinformatics analysis, find out the key genes involved in the occurrence, development and prognosis of multiple myeloma, and analyze and predict their functions.Methods:The gene chip data GSE146649 was downloaded from the GEO expression database. The gene chip data GSE146649 was analyzed by R language to obtain the genes with different expression in multiple myeloma and healthy samples, and the cluster analysis heat map was constructed. At the same time, the protein-protein interaction (PPI) networks of these DEGs were established by STRING and Cytoscape software. The gene co-expression module was constructed by weighted correlation network analysis (WGCNA). The hub genes were identified from key gene and central gene. TCGA database was used to analyze the expression of differentially expressed genes in patients with multiple myeloma. Finally, the expression level of TNFSF11 in whole blood samples from patients with multiple myeloma was analyzed by RT qPCR.Results:We identified four genes (TNFSF11, FGF2, SGMS2, IGFBP7) as hub genes of multiple myeloma. Then, TCGA database was used to analyze the survival of TNFSF11, FGF2, SGMS2 and IGFBP7 in patients with multiple myeloma. Finally, the expression level of TNFSF11 in whole blood samples from patients with multiple myeloma was analyzed by RT qPCR.Conclusion:The study suggests that TNFSF11, FGF2, SGMS2 and IGFBP7 are important research targets to explore the pathogenesis, diagnosis and treatment of multiple myeloma.