mRNA-lncRNA gene expression signature for predicting pediatric AML relapse
1Southern Federal University, Academy of Biology and Biotechnology, Department of Genetics, Human and Animal Genetics laboratory, 194/1 Stachki Ave, Rostov-on-Don, Russia 344090.
Abstract:
Children with acute myeloid leukemia (AML) face a relapse of the disease in 30% of all cases. AML relapse is difficult to predict, and existing risk scales are often ineffective. Using data from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET-AML) project, we defined an expression signature based on matrix RNAs (mRNAs) and long non-coding RNAs (lncRNAs) that could predict relapse in pediatric AML patients. We used a comprehensive bioinformatics analysis that included the identification of functionally significant differentially expressed genes in AML relapse, several rounds of association with relapse-free survival (RFS) mRNAs and lncRNAs selection, and evaluation of the obtained expression signatures to predict recurrence at the primary tumor level. Two mRNAs (ENSG00000149289.11 (ZC3H12C) and ENSG00000075213.11 (SEMA3A)) and one lncRNA (ENSG00000287569.1) were associated with a decreased RFS. Models including changes in the expression of ZC3H12C and ENSG00000287569.1, as well as all three markers, demonstrated very good quality and could predict the recurrence of pediatric AML.
Insights
Predicting relapse in pediatric acute myeloid leukemia (AML) is challenging. This study identified a novel gene expression signature using matrix RNAs (mRNAs) and long non-coding RNAs (lncRNAs) to accurately forecast disease recurrence in children with AML.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Pediatric acute myeloid leukemia (AML) has a significant relapse rate (30%).
- Current risk stratification methods for AML relapse are often insufficient.
- Predicting AML relapse is crucial for timely therapeutic intervention.
Purpose of the Study:
- To develop a predictive expression signature for relapse in pediatric AML patients.
- To identify specific messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) associated with relapse-free survival (RFS).
- To evaluate the efficacy of the identified signature in predicting AML recurrence.
Main Methods:
- Utilized data from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET-AML) project.
- Conducted comprehensive bioinformatics analysis, including differential gene expression analysis.
- Selected mRNAs and lncRNAs based on their association with relapse-free survival (RFS).
Main Results:
- Identified two mRNAs (ZC3H12C, SEMA3A) and one lncRNA (ENSG00000287569.1) significantly associated with decreased RFS.
- Developed predictive models using these molecular markers.
- Models incorporating ZC3H12C and the lncRNA, or all three markers, showed high predictive accuracy for pediatric AML recurrence.
Conclusions:
- A novel gene expression signature comprising ZC3H12C, SEMA3A, and a specific lncRNA can predict relapse in pediatric AML.
- This signature offers a promising tool for improved risk stratification and personalized treatment strategies.
- Further validation is warranted to integrate this signature into clinical practice for pediatric AML management.
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