A multi-omics approach for biomarker discovery in neuroblastoma: a network-based framework

Area of Science:

• Oncology
• Bioinformatics
• Genetics

Background:

• Neuroblastoma (NB) is a primary cause of childhood cancer mortality.
• MYCN amplification is a key genetic driver in NB, yet difficult to target effectively.
• Understanding the NB molecular interactome is crucial for improving treatment strategies.

Purpose of the Study:

• To develop an integrated computational framework for analyzing NB multi-omics data.
• To identify essential genes and microRNAs (miRNAs) implicated in NB development and progression.
• To discover novel candidate biomarkers for NB prognosis and diagnosis.

Main Methods:

• Integrated three levels of high-throughput NB data: mRNA-seq, miRNA-seq, and methylation array.
• Applied Similarity Network Fusion (SNF) and ranked SNF methods to identify key genes and miRNAs.
• Constructed a regulatory network of transcription factors (TFs), miRNAs, and target genes.

Main Results:

• Identified a regulatory network revealing interactions between TFs, miRNAs, and target genes.
• Discovered ten candidate biomarkers: three TFs and seven miRNAs.
• Four biomarkers are previously studied in NB; others have roles in different cancers, suggesting potential NB involvement.

Conclusions:

• The integrated computational framework successfully identified potential NB biomarkers.
• Candidate biomarkers show promise for NB diagnosis and prognosis.
• Analyzing the cellular interactome is a viable strategy for uncovering NB vulnerabilities and optimizing therapies.