Integration of Bulk RNA Sequencing and Single-Cell Sequencing to Identify Prognostic Genes Associated With MCDRGs in Neuroblastoma.
The study integrates public bulk and single-cell neuroblastoma transcriptomic data, computational modeling, and RT-qPCR validation to identify five myeloid cell differentiation-related prognostic genes and an associated survival risk model with tumor-microenvironment and predicted drug-sensitivity differences.
Open original publication →What the AI sees
The study integrates public bulk and single-cell neuroblastoma transcriptomic data, computational modeling, and RT-qPCR validation to identify five myeloid cell differentiation-related prognostic genes and an associated survival risk model with tumor-microenvironment and predicted drug-sensitivity differences.
Research significance
Evidence in the record links the five-gene signature to prognosis, immune and stromal features, and computationally predicted sensitivity to entinostat and sapitinib; it may therefore support risk-adapted treatment selection or target discovery, but prospective prognostic validation and experimental or clinical confirmation of drug response are still required.
Source abstract
Neuroblastoma (NB) is a common pediatric tumor that exhibits significant clinical heterogeneity and substantial prognostic variability. However, there is still a lack of effective prognostic tools. It has been found that the differentiation of myeloid cells in the tumor microenvironment (TME) has a profound influence on tumor progression and immune regulation in neuroblastoma. But the correlation between NB and myeloid cell differentiation-related genes (MCDRGs) is still unclear. This study integrated bulk and single-cell RNA sequencing data to identify key prognostic genes associated with myeloid cell differentiation, construct and validate a risk prediction model, and thoroughly investigate the molecular mechanisms through which these genes regulate the TME and influence NB prognosis. Using bioinformatic approaches, we retrieved NB-related transcriptomic data from public databases, identified differentially expressed myeloid cell differentiation-related genes (MCDRGs), and constructed a prognostic risk model using Cox regression and machine-learning algorithms. Functional enrichment analysis, TME characterization, immune checkpoint profiling, drug sensitivity analysis, and somatic mutation analysis were performed between the high-risk group (HRG) and low-risk group (LRG). Single-cell RNA sequencing data were used to dissect the cell type-specific expression patterns of prognostic genes, which were further experimentally validated via reverse transcription quantitative polymerase chain reaction (RT-qPCR). Five key prognostic genes (FAXDC2, GP1BB, TRIB1, ETV2, and H4C12) were identified from 187 candidate genes, and a robust risk prediction model was constructed. Significant differences were observed between the HRG and LRG in terms of TME scores, immune cell infiltration, and immune checkpoint expression. Ribosome biogenesis and cell cycle-related pathways were enriched in the HRG, which displayed higher sensitivity to entinostat and sapitinib. Single-cell analysis further highlighted fibroblasts and myeloid cells as key cell subsets in which prognostic genes exhibited dynamic expression patterns. RT-qPCR further validated these gene expression trends. The prognostic model based on MCDRGs effectively predicts survival outcomes in patients with NB. This model systematically reveals the comprehensive mechanisms by which MCDRGs influence prognosis through regulating the TME, cell-cell interactions, and therapeutic responses. These findings provide a new theoretical basis and potential targets for precise risk stratification and individualized treatment strategies in NB.