Identification Of Immune Subtypes And Construction Of Risk Model Of Acute Myeloid Leukemia Based On Tumor Immune Microenvironment

Background:Acute myeloid leukemia is a hematological malignancy caused by abnormal proliferation and differentiation of immature myeloid precursor cells.Treatment options for AML patients have been limited over the past half century,and the therapeutic strategy of chemotherapy based on cytarabine+anthracyclines combined with hematopoietic stem cell transplantation has not changed significantly.At present,the cure rate of adult patients under the age of 60 is 35-40%,while the cure rate of patients over the age of 60 is only 5-15%.Therefore,it is urgent to find more effective treatment.Tumor microenvironment refers to the surrounding microenvironment of tumor cells.It is a system with complex components,and has become a hot topic in the field of tumor immunotherapy in recent years.The infiltrated immune cells in the tumor microenvironment participate in and constitute the tumor immune microenvironment,playing a key role in tumor occurrence and various biological behavior changes.Studies have shown that the immune microenvironment of patients with AML has changed significantly,thus aggravating the development of the disease.However,to date,the tumor immune microenvironment in patients with AML has not been fully elucidated.Therefore,through comprehensive analysis of the heterogeneity and complexity of the immune microenvironmnent in patients with AML,different tumor immunophenotypes may be identified,which may also provide a certain reference value in guiding the selection of treatment options for patients.Objective:The aim of our study was to investigate the changes of immune microenvironment in acute myeloid leukemia by bioinformatics analysis.Based on the changes in the immune microenvironment of AML,we screened out the key prognostic genes that can affect the survival time of patients with acute myeloid leukemia,providing a new theoretical and scientific basis for the search for therapeutic targets of acute myeloid leukemia.To establish a new tumor microenvironment-related prognostic risk model to provide guidance for patients to develop better treatment strategies.Contents and methods:1.First,we downloaded RNA-seq data from the TCGA-LAML cohort to quantify the infiltrating abundance of each immune cell in all AML samples by single-sample enrichment analysis.The enrichment fraction calculated by the enrichment analysis was used to represent the abundance of each immune cell infiltration.2.Then we applied unsupervised clustering analysis to classify the patients according to the abundance of immunocyte infiltration.The number and stability of clusters are determined by consensus clustering algorithm.Finally,we successfully identified the two immuno-infiltrating subtypes of AML and verified the differences in biological characteristics of the two immuno-subtypes through differential expression analysis and functional enrichment analysis3.Based on the expression values of key differentially expressed genes of different immune subtypes,we constructed a prognostic risk model to quantify the pattern of immunologically infiltrating tumor microenvironment in individuals with AML.We also determined cutoff values for risk scores,which divided patients into low-risk and high-risk groups.The high-risk group presented with immune fever subtype while the low-risk group presented with immune cold subtype.4.Finally,we explored the role of risk models in the treatment benefit of AML through drug sensitivity analysis and predictive response immunotherapy.Results:We We identified two different immune subtypes in AML patients with different prognosis and biological characteristics,which initially revealed the heterogeneity and complexity of the microenvironment of immuno-infiltrating tumors in AML patients.A prognostic model for the risk associated with immune infiltration is constructed by mathematical modeling,which is helpful for individualized prognosis prediction of AML patients.It can be used as a potential biomarker for predicting the efficacy of immunotherapy for AML in clinical practice,and helps to identify patients who respond to immunotherapy.