Identification of potential key genes associated with osteosarcoma based on integrated bioinformatics analyses

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Osteosarcoma is characterized by high metastasis and poor patient outcomes.
• Interindividual variation and heterogeneity in osteosarcoma complicate traditional population-level gene expression analysis.
• A novel strategy is needed to identify universally aberrant genes in osteosarcoma for better understanding of tumorigenesis.

Purpose of the Study:

• To identify genes with consistently aberrant expression across diverse osteosarcoma samples.
• To improve the understanding of osteosarcoma pathogenesis through the identification of universal differentially expressed genes (DEGs).
• To explore the functional roles and potential therapeutic relevance of these universally dysregulated genes.

Main Methods:

• Utilized the RankComp algorithm to identify DEGs by comparing gene expression ordering in normal versus osteosarcoma tissues.
• Calculated gene dysregulation frequency across multiple datasets to define universal DEGs (frequency > 80%).
• Performed coexpression, pathway enrichment, and protein-protein interaction network analyses on identified universal DEGs.

Main Results:

• Identified 51 universal DEGs from 188 osteosarcoma samples across four datasets.
• Discovered 4 universally upregulated and 47 universally downregulated genes.
• Found that genes differentially coexpressed with universal DEGs were enriched in 46 cancer-related pathways, indicating functional significance in tumorigenesis.

Conclusions:

• The identified 51 universal DEGs represent commonly aberrant genes in osteosarcoma.
• These genes are involved in critical cancer-related functions and pathways.
• These universally dysregulated genes hold potential as important targets for osteosarcoma diagnosis and therapy.