Dataset Information

Incorporation of long non-coding RNA expression profile in the 2017 ELN risk classification can improve prognostic prediction of acute myeloid leukemia patients.

ABSTRACT:

Background

Expression of long non-coding RNAs (lncRNAs) has recently been recognized as a potential prognostic marker in acute myeloid leukemia (AML). However, it remains unclear whether incorporation of the lncRNAs expression in the 2017 European LeukemiaNet (ELN) risk classification can further improve the prognostic prediction.

Methods

We enrolled 275 newly diagnosed non-M3 AML patients and randomly assigned them to the training (n = 183) and validation cohorts (n = 92). In the training cohort, we formulated a prognostic lncRNA scoring system composed of five lncRNAs with significant prognostic impact from the lncRNA expression profiling.

Findings

Higher lncRNA scores were significantly associated with older age and adverse gene mutations. Further, the higher-score patients had shorter overall and disease-free survival than lower-score patients, which were also confirmed in both internal and external validation cohorts (TCGA database). The multivariate analyses revealed the lncRNA score was an independent prognosticator in AML, irrespective of the risk based on the 2017 ELN classification. Moreover, in the 2017 ELN intermediate-risk subgroup, lncRNA scoring system could well dichotomize the patients into two groups with distinct prognosis. Within the ELN intermediate-risk subgroup, we found that allogeneic hematopoietic stem cell transplantation could provide better outcome on patients with higher lncRNA scores. Through bioinformatics approach, we identified high lncRNA scores were correlated with leukemia/hematopoietic stem cell signatures.

Interpretation

Incorporation of lncRNA scoring system in 2017 ELN classification can improve risk-stratification of AML patients and help clinical decision-making. FUND: This work was supported Ministry of Science and Technology, and Ministry of Health and Welfare of Taiwan.

SUBMITTER: Tsai CH

PROVIDER: S-EPMC6413345 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

Incorporation of long non-coding RNA expression profile in the 2017 ELN risk classification can improve prognostic prediction of acute myeloid leukemia patients.

Tsai Cheng-Hong CH Yao Chi-Yuan CY Tien Feng-Min FM Tang Jih-Luh JL Kuo Yuan-Yeh YY Chiu Yu-Chiao YC Lin Chien-Chin CC Tseng Mei-Hsuan MH Peng Yen-Ling YL Liu Ming-Chih MC Liu Chia-Wen CW Yao Ming M Lin Liang-In LI Chou Wen-Chien WC Chen Chien-Yu CY Hou Hsin-An HA Tien Hwei-Fang HF

EBioMedicine 20190117

<h4>Background</h4>Expression of long non-coding RNAs (lncRNAs) has recently been recognized as a potential prognostic marker in acute myeloid leukemia (AML). However, it remains unclear whether incorporation of the lncRNAs expression in the 2017 European LeukemiaNet (ELN) risk classification can further improve the prognostic prediction.<h4>Methods</h4>We enrolled 275 newly diagnosed non-M3 AML patients and randomly assigned them to the training (n = 183) and validation cohorts (n = 92). In the ...[more]

PMID: 30662003

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Project description:BackgroundThe recently updated European LeukemiaNet risk stratification guidelines combine cytogenetic abnormalities and genetic mutations to provide the means to triage patients with acute myeloid leukemia for optimal therapies. Despite the identification of many prognostic factors, relatively few have made their way into clinical practice.MethodsIn order to assess and improve the performance of the European LeukemiaNet guidelines, we developed novel prognostic models using the biomarkers from the guidelines, age, performance status and select transcript biomarkers. The models were developed separately for mononuclear cells and viable leukemic blasts from previously untreated acute myeloid leukemia patients (discovery cohort, N = 185) who received intensive chemotherapy. Models were validated in an independent set of similarly treated patients (validation cohort, N = 166).ResultsModels using European LeukemiaNet guidelines were significantly associated with clinical outcomes and, therefore, utilized as a baseline for comparisons. Models incorporating age and expression of select transcripts with biomarkers from European LeukemiaNet guidelines demonstrated higher area under the curve and C-statistics but did not show a substantial improvement in performance in the validation cohort. Subset analyses demonstrated that models using only the European LeukemiaNet guidelines were a better fit for younger patients (age < 55) than for older patients. Models integrating age and European LeukemiaNet guidelines visually showed more separation between risk groups in older patients. Models excluding results for ASXL1, CEBPA, RUNX1 and TP53, demonstrated that these mutations provide a limited overall contribution to risk stratification across the entire population, given the low frequency of mutations and confounding risk factors.ConclusionsWhile European LeukemiaNet guidelines remain a critical tool for triaging patients with acute myeloid leukemia, the findings illustrate the need for additional prognostic factors, including age, to improve risk stratification.

Project description:BackgroundAs a severe hematological malignancy in adults, acute myeloid leukemia (AML) is characterized by high heterogeneity and complexity. Emerging evidence highlights the importance of the tumor immune microenvironment and lipid metabolism in cancer progression. In this study, we comprehensively evaluated the expression profiles of genes related to lipid metabolism and immune modifications to develop a prognostic risk signature for AML.MethodsFirst, we extracted the mRNA expression profiles of bone marrow samples from an AML cohort from The Cancer Genome Atlas database and employed Cox regression analysis to select prognostic hub genes associated with lipid metabolism and immunity. We then constructed a prognostic signature with hub genes significantly related to survival and validated the stability and robustness of the prognostic signature using three external datasets. Gene Set Enrichment Analysis was implemented to explore the underlying biological pathways related to the risk signature. Finally, the correlation between signature, immunity, and drug sensitivity was explored.ResultsEight genes were identified from the analysis and verified in the clinical samples, including APOBEC3C, MSMO1, ATP13A2, SMPDL3B, PLA2G4A, TNFSF15, IL2RA, and HGF, to develop a risk-scoring model that effectively stratified patients with AML into low- and high-risk groups, demonstrating significant differences in survival time. The risk signature was negatively related to immune cell infiltration. Samples with AML in the low-risk group, as defined by the risk signature, were more likely to be responsive to immunotherapy, whereas those at high risk responded better to specific targeted drugs.ConclusionsThis study reveals the significant role of lipid metabolism- and immune-related genes in prognosis and demonstrated the utility of these signature genes as reliable bioinformatic indicators for predicting survival in patients with AML. The risk-scoring model based on these prognostic signature genes holds promise as a valuable tool for individualized treatment decision-making, providing valuable insights for improving patient prognosis and treatment outcomes in AML.

Dataset Information

Incorporation of long non-coding RNA expression profile in the 2017 ELN risk classification can improve prognostic prediction of acute myeloid leukemia patients.

Background

Methods

Findings

Interpretation

Publications

Incorporation of long non-coding RNA expression profile in the 2017 ELN risk classification can improve prognostic prediction of acute myeloid leukemia patients.

Similar Datasets

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