Dataset Information

Differential mRNA and long noncoding RNA expression profiles in pediatric B-cell acute lymphoblastic leukemia patients.

ABSTRACT:

Background

Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides (nt) that are involved in the pathogenesis and development of various cancers including B cell acute lymphoblastic leukemia (B-ALL). To determine the potential roles of lncRNAs involved in pathogenesis of B-ALL, we analyzed the expression profile of lncRNAs and mRNAs in B-ALL, respectively, and constructed lncRNAs/mRNAs interaction network.

Methods

We performed RNA sequencing of 10 non-leukemic blood disease donors and 10 B-ALL patients for Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Interactions among mRNAs were predicted using the STRING database. Quantitative real time PCR (qRT-PCR) was performed to verify the RNA-seq data of lncRNAs and mRNAs. Potential functions of subtype-specific lncRNAs were determined by using coexpression-based analysis on distally (trans-pattern) located protein-coding genes.

Results

A total of 1813 differentially expressed transcripts (DETs) and 2203 lncRNAs were identified. Moreover, 10 dysregulated lncRNAs and 10 mRNAs were randomly selected, and further assessed by RT-qPCR in vitro. Go and KEGG analysis demonstrated that the differentially expressed mRNAs were most closely associated with myeloid leukocyte activation and in transcriptional misregulation in cancer, respectively. In addition, co-expression analysis demonstrated that these lncRNAs, including MSTRG.27994.3, MSTRG.21740.1, ENST00000456341, MSTRG.14224.1 and MSTRG.20153.1, may mediate the pathogenesis and development of B-ALL via lncRNA-mRNA network interactions.

Conclusions

These results showed that several mRNAs and lncRNAs are aberrantly expressed in the bone marrow of B-ALL patients and play potential roles in B-ALL development, and be useful for diagnostic and/or prognostic purposes in pediatric B-ALL.

Data availability

The datasets used during our study are available through HARVARD Dataverse Persistent ID doi: https://doi.org/10.7910/DVN/LK9T4Z .

SUBMITTER: Xia J

PROVIDER: S-EPMC8722040 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeOptimized strategies for risk classification are essential to tailor therapy for patients with biologically distinctive disease. Risk classification in pediatric acute myeloid leukemia (pAML) relies on detection of translocations and gene mutations. Long noncoding RNA (lncRNA) transcripts have been shown to associate with and mediate malignant phenotypes in acute myeloid leukemia (AML) but have not been comprehensively evaluated in pAML.MethodsTo identify lncRNA transcripts associated with outcomes, we evaluated the annotated lncRNA landscape by transcript sequencing of 1,298 pediatric and 96 adult AML specimens. Upregulated lncRNAs identified in the pAML training set were used to establish a regularized Cox regression model of event-free survival (EFS), yielding a 37 lncRNA signature (lncScore). Discretized lncScores were correlated with initial and postinduction treatment outcomes using Cox proportional hazards models in validation sets. Predictive model performance was compared with standard stratification methods by concordance analysis.ResultsTraining set cases with positive lncScores had 5-year EFS and overall survival rates of 26.7% and 42.7%, respectively, compared with 56.9% and 76.3% with negative lncScores (hazard ratio, 2.48 and 3.16; P < .001). Pediatric validation cohorts and an adult AML group yielded comparable results in magnitude and significance. lncScore remained independently prognostic in multivariable models, including key factors used in preinduction and postinduction risk stratification. Subgroup analysis suggested that lncScores provide additional outcome information in heterogeneous subgroups currently classified as indeterminate risk. Concordance analysis showed that lncScore adds to overall classification accuracy with at least comparable predictive performance to current stratification methods that rely on multiple assays.ConclusionInclusion of the lncScore enhances predictive power of traditional cytogenetic and mutation-defined stratification in pAML with potential, as a single assay, to replace these complex stratification schemes with comparable predictive accuracy.