Dataset Information

Development and validation of a 10-gene prognostic signature for acute myeloid leukaemia.

ABSTRACT: Acute myeloid leukaemia (AML) is the most common type of adult acute leukaemia and has a poor prognosis. Thus, optimal risk stratification is of greatest importance for reasonable choice of treatment and prognostic evaluation. For our study, a total of 1707 samples of AML patients from three public databases were divided into meta-training, meta-testing and validation sets. The meta-training set was used to build risk prediction model, and the other four data sets were employed for validation. By log-rank test and univariate COX regression analysis as well as LASSO-COX, AML patients were divided into high-risk and low-risk groups based on AML risk score (AMLRS) which was constituted by 10 survival-related genes. In meta-training, meta-testing and validation sets, the patient in the low-risk group all had a significantly longer OS (overall survival) than those in the high-risk group (P < .001), and the area under ROC curve (AUC) by time-dependent ROC was 0.5854-0.7905 for 1 year, 0.6652-0.8066 for 3 years and 0.6622-0.8034 for 5 years. Multivariate COX regression analysis indicated that AMLRS was an independent prognostic factor in four data sets. Nomogram combining the AMLRS and two clinical parameters performed well in predicting 1-year, 3-year and 5-year OS. Finally, we created a web-based prognostic model to predict the prognosis of AML patients (https://tcgi.shinyapps.io/amlrs_nomogram/).

SUBMITTER: Yang Z

PROVIDER: S-EPMC7176885 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Development and validation of a 10-gene prognostic signature for acute myeloid leukaemia.

Yang Zuyi Z Shang Jun J Li Ning N Zhang Liang L Tang Tingting T Tian Guoyan G Chen Xiaohui X

Journal of cellular and molecular medicine 20200309 8

Acute myeloid leukaemia (AML) is the most common type of adult acute leukaemia and has a poor prognosis. Thus, optimal risk stratification is of greatest importance for reasonable choice of treatment and prognostic evaluation. For our study, a total of 1707 samples of AML patients from three public databases were divided into meta-training, meta-testing and validation sets. The meta-training set was used to build risk prediction model, and the other four data sets were employed for validation. B ...[more]

PMID: 32150667

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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:Risk classification in pediatric acute myeloid leukemia (P-AML) is crucial for personalizing treatments. Thus, we aimed to establish a risk-stratification tool for P-AML patients and eventually guide individual treatment. A total of 256 P-AML patients with accredited mRNA-seq data from the TARGET database were divided into training and internal validation datasets. A gene-expression-based prognostic score was constructed for overall survival (OS), by using univariate Cox analysis, LASSO regression analysis, Kaplan-Meier (K-M) survival, and multivariate Cox analysis. A P-AML-5G prognostic score bioinformatically derived from expression levels of 5 genes (ZNF775, RNFT1, CRNDE, COL23A1, and TTC38), clustered P-AML patients in training dataset into high-risk group (above optimal cut-off) with shorter OS, and low-risk group (below optimal cut-off) with longer OS (p < 0.0001). Meanwhile, similar results were obtained in internal validation dataset (p = 0.005), combination dataset (p < 0.001), two treatment sub-groups (p < 0.05), intermediate-risk group defined with the Children's Oncology Group (COG) (p < 0.05) and an external Japanese P-AML dataset (p = 0.005). The model was further validated in the COG study AAML1031(p = 0.001), and based on transcriptomic analysis of 943 pediatric patients and 70 normal bone marrow samples from this dataset, two genes in the model demonstrated significant differential expression between the groups [all log2(foldchange) > 3, p < 0.001]. Independent of other prognostic factors, the P-AML-5G groups presented the highest concordance-index values in training dataset, chemo-therapy only treatment subgroups of the training and internal validation datasets, and whole genome-sequencing subgroup of the combined dataset, outperforming two Children's Oncology Group (COG) risk stratification systems, 2022 European LeukemiaNet (ELN) risk classification tool and two leukemic stem cell expression-based models. The 5-gene prognostic model generated by a single assay can further refine the current COG risk stratification system that relies on numerous tests and may have the potential for the risk judgment and identification of the high-risk pediatric AML patients receiving chemo-therapy only treatment.

Dataset Information

Development and validation of a 10-gene prognostic signature for acute myeloid leukaemia.

Publications

Development and validation of a 10-gene prognostic signature for acute myeloid leukaemia.

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