Analyzing the disease module associated with osteosarcoma via a network- and pathway-based approach.

Area of Science:

• Oncology
• Genetics
• Bioinformatics

Background:

• Osteosarcoma is the most frequent primary bone cancer in children and adolescents.
• Understanding the genetic underpinnings of osteosarcoma is crucial for improved diagnosis and treatment.

Purpose of the Study:

• To identify a novel osteosarcoma-related gene module (OSM) by integrating genome-wide association studies (GWAS) data with human protein-protein interaction (PPI) networks.
• To pinpoint potential diagnostic and prognostic biomarkers for osteosarcoma.

Main Methods:

• Utilized a dataset of osteosarcoma somatic mutations from dbGaP.
• Integrated mutation P-values into a human PPI network using the dmGWAS R package.
• Performed topological analysis on the identified OSM to find key genes.

Main Results:

• Identified an OSM comprising 201 genes (OS genes) and 268 interactions, associated with immune response, signal transduction, and cell activity.
• Discovered 11 potential diagnostic biomarkers, including APP, HSP90B1, SUZ12, and IKZF1.
• Validated APP, HSP90B1, SUZ12, and IKZF1 as osteosarcoma survival-related genes using The Cancer Genome Atlas (TCGA) data.

Conclusions:

• The identified OSM and key genes offer insights into osteosarcoma pathogenesis.
• Potential biomarkers were highlighted for improving osteosarcoma diagnosis and treatment strategies.
• The employed bioinformatics methodology can be adapted for studying other complex diseases.