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Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.

ABSTRACT: This paper identifies prognosis factors for survival in patients with acute myeloid leukemia (AML) using machine learning techniques. We have integrated machine learning with feature selection methods and have compared their performances to identify the most suitable factors in assessing the survival of AML patients. Here, six data mining algorithms including Decision Tree, Random Forrest, Logistic Regression, Naive Bayes, W-Bayes Net, and Gradient Boosted Tree (GBT) are employed for the detection model and implemented using the common data mining tool RapidMiner and open-source R package. To improve the predictive ability of our model, a set of features were selected by employing multiple feature selection methods. The accuracy of classification was obtained using 10-fold cross-validation for the various combinations of the feature selection methods and machine learning algorithms. The performance of the models was assessed by various measurement indexes including accuracy, kappa, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC curve (AUC). Our results showed that GBT with an accuracy of 85.17%, AUC of 0.930, and the feature selection via the Relief algorithm has the best performance in predicting the survival rate of AML patients.

SUBMITTER: Karami K

PROVIDER: S-EPMC8294525 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAlthough it is well-known that adult and pediatric acute myeloid leukemias (AMLs) are genetically distinct diseases, they still share certain gene expression profiles. The age-related genetic heterogeneities of AMLs have been well-studied, but the common prognostic signatures and molecular mechanisms of adult and pediatric AMLs are less investigated.AimTo identify genes and pathways that are associated with both pediatric and adult AMLs and discover a gene signature for overall survival (OS) prediction.MethodsThrough mining the transcriptome profiles of The Cancer Genome Atlas (TCGA) data sets of adult cancers and The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) data of pediatric cancers, we identified genes that are commonly dysregulated in both pediatric and adult AMLs, further discovered a common gene signature, and built two risk score models for TCGA and TARGET cohorts, respectively with L 0 regularized global AUC (area under the receiver operating characteristic curve) summary maximization.ResultsWe identified 57 genes that are differentially expressed and prognostically significant in both adult and childhood AMLs. The top 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched with those 57 genes include transcriptional misregulation, focal adhesion, PI3K-Akt signaling pathway, and signaling pathways regulating pluripotency of stem cells. We further identified a 6-gene signature including genes of ADAMTS3, DNMT3B, NYNRIN, SORT1, ZFHX3, and ZG16B for risk prediction. We constructed a risk score model with one dataset (either TCGA or TARGET) and evaluated its performance with the other. The test AUCs for the risk prediction of TCGA data with a 2-year and 5-year OS cutoffs are 0.762 (P = 2.33e-13, 95% CI: 0.69-0.83) and 0.759 (P = 7.26e-08, 95% CI: 0.66-0.85), respectively, while the test AUCs of TARGET data with the same cutoffs are 0.71 (P = 3.3e-07, 95% CI: 0.62-0.79) and 0.72 (P= 5.25e-09, 95% CI: 0.65-0.80), respectively. We further stratified patients into 3 equal sized prognostic subtypes with the 6-gene risk scores. The P-values of the tertile partitions are 1.74e-07 and 3.28e-08 for the TARGET and TCGA cohorts, respectively, which are significantly better than the standard cytogenetic risk stratification of both cohorts (TARGET: P = 1.64e-06; TCGA: P = 1.79e-05). When validated with two other independent cohorts, the 6-gene risk score models remain a significant predictor for OS. Investigating the common gene expression program is significant in that we may extrapolate the findings from adults to children and avoid unnecessary pediatric clinical trials.

Project description:Background: Acute myeloid leukemia (AML) is a heterogeneous malignant disease. SLC25A1, the gene encoding mitochondrial carrier subfamily of solute carrier proteins, was reported to be overexpressed in certain solid tumors. However, its expression and value as prognostic marker has not been assessed in AML. Methods: We retrieved RNA profile and corresponding clinical data of AML patients from the Beat AML, TCGA, and TARGET databases (TARGET_AML). Patients in the TCGA cohort were well-grouped into two group based on SLC25A1 and differentially expressed genes were determined between the SLC25A1 high and low group. The expression of SLC25A1 was validated with clinical samples. The survival and apoptosis of two AML cell lines were analyzed with SLC25A1 inhibitor (CTPI-2) treatment. Cox and the least absolute shrinkage and selection operator (LASSO) regression analyses were applied to Beat AML database to identify SLC25A1-associated genes for the construction of a prognostic risk-scoring model. Survival analysis was performed by Kaplan-Meier and receiver operator characteristic curves. Results: Our analysis revealed that high expressed level of SLC25A1 in AML patients correlates with unfavorable prognosis. Moreover, SLC25A1 expression was positively associated with metabolism activity. We further demonstrated that the inhibition of SLC25A1 could inhibit the proliferation and increase the apoptosis of AML cells. In addition, a panel of SLC25A1-associated genes, was identified to construct a prognostic risk-scoring model. This SLC25A1-associated prognostic signature (SPS) is an independent risk factor with high area under curve (AUC) values of receiver operating characteristic (ROC) curves. A high SPS in leukemia patients is associated with poor survival. A Prognostic nomogram including the SPS and other clinical parameters, was constructed and its predictive efficiency was confirmed. Conclusion: We have successfully established a SPS prognostic model that predict outcome and risk stratification in AML. This risk model can be used as an independent biomarker to assess prognosis of AML.

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Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.

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