Prognostic Value and Immune Characterization of Genes Associated with Childhood Acute Leukemia applying Single-Cell RNA Sequencing.

Abstract

Introduction: Childhood acute lymphoblastic leukemia (cALL), the most common pediatric hematologic malignancy, arises primarily from B-cell origin and is strongly associated with immune dysfunction. This article integrated single-cell and bulk transcriptomic data to identify key B-cell subsets and cALL-related molecules as biomarkers.

Methods: Single-cell RNA sequencing (scRNA-seq) Data from 2 pre-B high hyperdiploid (HHD) ALL patients and 3 healthy pediatric bone marrow samples (GSE132509) were utilized for cell clustering using the Seurat package. Functional enrichment, pseudo-time trajectory, and cell-cell communication analyses were performed using clusterProfiler, Monocle2, and CellChat R packages, respectively. Bulk RNA-seq data of 511 cALL samples in the TARGET-ALL-P2 cohort were used to construct a prognostic model via Cox and LASSO regression. Immune infiltration differences between different risk groups were analyzed using ESTIMATE, MCP-counter, and CIBERSORT algorithms.

Results: The scRNA-seq analysis identified five cell subpopulations, with B cells demonstrating significant enrichment in cALL samples. Notably, the C2 subset was associated with cell proliferation. Ligand-receptor analysis revealed key interactions involving B cell C2. Four marker genes (CENPF, IGLL1, ANP32E, and PSMA2) were identified to build a risk model. Low-risk patients showed better survival, while high-risk patients had higher ESTIMATE scores.

Discussion: This study examined the key role of B cells in cALL, constructed a risk model with strong prognostic predictive ability applying multi-omics analysis, and primarily explored its potential mechanism in immune regulation.

Conclusion: This study revealed the critical role of B cells in cALL, and the prognostic model showed a high prediction accuracy, providing a potential target for individualized treatment of cALL.