Dataset Information

Development of a poor-prognostic-mutations derived immune prognostic model for acute myeloid leukemia.

ABSTRACT: Leukemia cell-intrinsic somatic mutations and cytogenetic abnormalities have been used to define risk categories in acute myeloid leukemia (AML). In addition, since the immune microenvironment might influence prognosis and somatic mutations have been demonstrated to modulate the immune microenvironment in AML, there is need for developing and evaluating an immune prognostic model (IPM) derived from mutations associated with poor prognosis. Based on AML cases with intermediate and adverse-cytogenetic risk in the Cancer Genome Atlas (TCGA) database, 64 immune-related differentially expressed genes (DEGs) among patients with RUNX1, TP53, or ASXL1 mutations and patients without these mutations were identified. After Cox proportional hazards analysis, an IPM composed of PYCARD and PEAR1 genes was constructed. IPM defined high-risk (IPM-HR) independently predicted lower 2-year overall survival (OS) rates in both patients with intermediate and adverse-cytogenetic risks and non-M3 patients in the TCGA AML cohort. The poor prognostic impact of IPM-HR on OS was further validated by GSE71014, 37642, and 10358 downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, IPM-HR was remarkably associated with higher proportions of CD8+?T cells and regulatory T cells (Tregs), lower proportions of eosinophils, and higher expression of the checkpoint molecules CTLA-4, PD-1, and LAG3 in the TCGA non-M3 AML cohort. In summary, we developed and validated an IPM derived from mutations related with poor prognosis in AML, which would provide new biomarkers for patient stratification and personalized immunotherapy.

SUBMITTER: Dao FT

PROVIDER: S-EPMC7921432 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Development of a poor-prognostic-mutations derived immune prognostic model for acute myeloid leukemia.

Dao Feng-Ting FT Wang Jun J Yang Lu L Qin Ya-Zhen YZ

Scientific reports 20210301 1

Leukemia cell-intrinsic somatic mutations and cytogenetic abnormalities have been used to define risk categories in acute myeloid leukemia (AML). In addition, since the immune microenvironment might influence prognosis and somatic mutations have been demonstrated to modulate the immune microenvironment in AML, there is need for developing and evaluating an immune prognostic model (IPM) derived from mutations associated with poor prognosis. Based on AML cases with intermediate and adverse-cytogen ...[more]

PMID: 33649342

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Project description:Acute myeloid leukemia with normal karyotype (NK-AML) is a group of diseases with high heterogeneity and immunological processes are significantly associated with its initiation and development. The implication of the immunogenomic landscape in the prognosis of patients with NK-AML has remained largely elusive. In the present study, the expression profiles of immune-related genes (IRGs) were examined and their association with overall survival (OS) was determined in 60 patients with NK-AML from The Cancer Genome Atlas dataset and 104 patients from the Gene Expression Omnibus (GEO) dataset no. GSE71014. Univariate Cox regression analysis was used to identify 42 and 203 IRGs in the two respective cohorts, which were significantly associated with OS in NK-AML. A risk model was constructed based on the regression coefficient and expression values of nine survival-associated IRGs shared between the two datasets [zinc finger CCCH-type containing, antiviral 1 like; transferrin receptor; suppressor of cytokine signaling 1; ELAV like RNA binding protein 1; roundabout guidance receptor 3; unc-93 homolog B1, Toll-like receptor signaling regulator; protein tyrosine phosphatase non-receptor type 6; interleukin 2 receptor subunit alpha (IL2RA) and IL3RA]. Using this risk model, patients with NK-AML may be divided into high- and low-risk groups in prognostic predictions. The area under the receiver operating characteristic curve for predicting OS was 0.793. The prognostic role of this risk model was successfully verified in another independent cohort (GEO dataset no. GSE71014). The prognostic risk score was positively associated with age and fms related receptor tyrosine kinase 3 mutation and correlated with infiltration by T regulatory cells. In conclusion, the results of the present study provided an IRG score model for prognostic stratification of adult patients with NK-AML, as well as further insight into the implication of IRGs in NK-AML that may lead to the development of novel immunotherapy approaches for this disease.

Project description:BackgroundAcute myeloid leukemia (AML) is a clonal malignant disease with poor prognosis and a low overall survival rate. Although many studies on the treatment and detection of AML have been conducted, the molecular mechanism of AML development and progression has not been fully elucidated. The present study was designed to pursuit the molecular mechanism of AML using a comprehensive bioinformatics analysis, and build an applicable model to predict the survival probability of AML patients in clinical use.MethodsTo simplify the complicated regulatory networks, we performed the gene co-expression and PPI network based on WGCNA and STRING database using modularization design. Two machine learning methods, A least absolute shrinkage and selector operation (LASSO) algorithm and support vector machine-recursive feature elimination (SVM-RFE), were used to filter the common hub genes by five-fold cross-validation. The candidate hub genes were used to build the predictive model of AML by the cox-proportional hazards analysis, and validated in The Cancer Genome Atlas (TCGA) cohort and ohsu cohort, which were reliable in the experimental verification by qRT-PCR and western blotting in mRNA and protein levels.ResultsThree hub genes, FLT3, CD177 and TTPAL were used to build a clinically applicable model to predict the survival probability of AML patients and divided them into high and low groups. To compare the survival ability of the model with the classical clinical features, we generated the nomogram. The model displayed the most risk points contrast to other clinical characteristics, which was compatible with the data of cox multivariate regression.ConclusionThis study reveal the novel molecular mechanism of AML, and construct a clinical model significantly related to AML patient prognosis. We showed the integrated roles of critical pathways, hub genes associated, which provide potential targets and new research ideas for the treatment and early detection of AML.

Project description:BackgroundAcute myeloid leukemia (AML) is one of the most common hematological malignancies and accounts for about 20% of childhood leukemias. Currently, immunotherapy is one of the recommended treatment schemes for recurrent AML patients to improve their survival rates. Nonetheless, low remission and high mortality rates are observed in recurrent AML and challenge the prognosis of AML patients. To address this problem, we aimed to establish and verify a reliable prognostic risk model using immune-related genes to improve the prognostic evaluation and recommendation for personalized treatment of AML.MethodsTranscriptome data and clinical data were acquired from the TARGET database while immune genes were sourced from InnateDB and ImmPort Shared databases. The mRNA expression profile matrix of immune genes from 62 normal samples and 1408 AML cases was extracted from the transcriptome data and subjected to differential expression (DE) analysis. The entire cohort of DE immune genes was randomly divided into the test group and training group. The prognostic model associated with immune genes was constructed using the training group. The test group and entire cohort were employed for model validation. Lastly, we analyzed the potential clinical application of the model and its association with immune cell infiltration.ResultsIn total, 751 DE immune genes were differentially regulated, including 552 upregulated and 199 downregulated. Based on these DE genes, we developed and validated a prognostic risk model composed of seven immune genes, GDF1, TPM2, IL1R1, PSMD4, IL5RA, DHCR24, and IL12RB2. This model is able to predict the 5-year survival rate more accurately compared with age, gender, and risk stratification. Further analysis showed that CD8+ T-cell contents and neutrophil infiltration decreased but macrophage infiltration increased as the risk score increased.ConclusionsA seven-immune gene model of AML was developed and validated. We propose this model as an independent prognostic variable able to estimate the 5-year survival rate. In addition, the model can also reflect the immune microenvironment of AML patients.

Dataset Information

Development of a poor-prognostic-mutations derived immune prognostic model for acute myeloid leukemia.

Publications

Development of a poor-prognostic-mutations derived immune prognostic model for acute myeloid leukemia.

Similar Datasets

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