Construction of a prognostic model of acute myeloid leukemia associated with immunogenic cell death.

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.

Bioinformatics Analysis of the Key Genes and Pathways in Multiple Myeloma.

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.