Identification of Prognostic Biomarkers in Gene Expression Profile of Neuroblastoma Via Machine Learning

TL;DR

This study uses machine learning to find 11 key genes linked to neuroblastoma prognosis, offering new targets for personalized cancer treatment.

Contribution

An integrative network-based machine learning method was developed to identify novel prognostic biomarkers and their therapeutic correlations in neuroblastoma.

Findings

11 hub prognostic biomarkers were identified, including RFC3, which is strongly linked to poor prognosis.

The biomarkers correlate with chemotherapy drugs like vincristine and cyclophosphamide.

Candidate drugs like dactinomycin and bortezomib show potential therapeutic value for neuroblastoma.

Abstract

Neuroblastoma (NB) is a common pediatric solid malignancy characterized by heterogeneous clinical outcomes. The identification of predictive and interpretable prognostic biomarkers is critical for advancing precision medicine in NB. We proposed an integrative network‐based machine learning method for biomarker discovery, which employed a network smoothed t‐statistic support vector machine to select prognostic related biomarkers, and then we performed network analysis on these biomarkers to find hub genes. Later, we conducted a comprehensive analysis to integrate bulk and single‐cell RNA sequencing data to character the tumor microenvironment of prognostic state and correlated them to the discovered hub genes. This analysis identified 528 prognostic biomarkers associated with NB. Network‐based analysis further refined this set to 11 hub prognostic biomarkers for NB: AURKA, BLM, BRCA1, BRCA2, CCNA2, CHEK1, E2F1, MAD2L1, PLK1, RAD51, and RFC3. Among these genes, high RFC3 expression was significantly associated with poor prognosis, highlighting its potential as a novel prognostic biomarker in NB. Additionally, our findings revealed that these biomarkers are correlated to chemotherapy drugs, such as vincristine and cyclophosphamide. Furthermore, drug sensitivity analyses identified several candidate drugs, such as dactinomycin, bortezomib, docetaxel, and sepantronium bromide, that may hold therapeutic potential for NB treatment. This study offers novel insights to underlying NB prognosis and therapeutic targets and provides a foundation for developing personalized treatment strategies to improve clinical outcomes.

This study identified 11 core prognostic biomarkers for neuroblastoma through optimized PPI networks and machine learning, revealing their correlations with chemotherapy and the tumor microenvironment, providing new insights for precision treatment and target development.

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