Integrative analysis of CRISPR screening and gene expression data identifies a three-gene prognostic model associated with immune microenvironment in neuroblastoma.
AI interpretation is pending for this paper.
Open original publication →What the AI sees
Not AI summarized yet.
Research significance
Pending deeper interpretation.
Source abstract
BACKGROUND: Neuroblastoma is a heterogeneous pediatric tumor with variable clinical outcomes. Current prognostic markers are insufficient to predict patient survival accurately, necessitating the identification of novel biomarkers and therapeutic targets. This study aimed to develop a robust prognostic model by integrating CRISPR screening data and transcriptomic profiles, and to explore its correlation with the tumor immune microenvironment. METHODS: We integrated Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening data from the DepMap database (version 24Q2) and gene expression profiles from neuroblastoma patients to identify key genes associated with neuroblastoma prognosis. Essential genes with Computational Evaluation of RNAi Essentiality Scores (CERES) scores less than -1 in at least 80% of 34 neuroblastoma cell lines were intersected with differentially expressed genes (|logFC| >2, P<0.05) from the National Genomics Data Center (NGDC) dataset (accession code HRA002064), resulting in 43 overlapping genes. Random forest analysis and multivariate Cox regression were conducted on the GSE49710 training set (n=498) to construct a prognostic model. The model was externally validated using the E-MTAB-8248 dataset (n=223). Immune infiltration and immunotherapy response were assessed using Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE), Microenvironment Cell Populations counter (MCPcounter), Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT), immunophenoscore (IPS), and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms. RESULTS: A three-gene prognostic model comprising PKMYT1, CDT1, and NCAPG was established. Patients were stratified into high-risk and low-risk groups based on the median RiskScore of 9.514526. In the training set, high-risk patients exhibited significantly poorer overall survival compared to low-risk patients (log-rank test, P<0.001). The model outperformed traditional clinical factors and demonstrated consistent prognostic value in the external validation cohort. High-risk patients showed lower immune cell infiltration, higher TIDE scores, and lower IPS values, suggesting an immunosuppressive microenvironment and reduced likelihood of responding to immunotherapy. In contrast, low-risk patients had higher immune infiltration and a predicted immunotherapy response rate of 70% versus 36% in the high-risk group. CONCLUSIONS: The three-gene prognostic model effectively stratifies neuroblastoma patients by survival risk and correlates with immune microenvironment characteristics. This model has potential clinical utility for prognosis prediction and guiding personalized immunotherapy strategies in neuroblastoma.