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Osteosarcoma and Ewing sarcoma are aggressive neoplasms with unfavourable prognoses. Despite advances in treatment, their prognosis remains poor, particularly in cases with metastases. In this context, the identification of new biomarkers and therapeutic targets is critical to improving the treatment of these diseases. This paper proposes a methodology to identify potential biomarkers using Gene Co-expression Networks (GCNs) constructed from transcriptomic data. Using High-Performance Computing (HPC) technologies, large-scale GCNs were efficiently generated and analysed, focusing on genes associated with tumour aggressiveness. Key findings include the identification of six potential biomarkers for Ewing sarcoma (COL11A1, VCAN, BUB1B, CDC20, UBE2C, AURKA) and four for osteosarcoma (NKX2-1, TAL1, GFI1, IKZF1). These genes are involved in processes such as extracellular matrix remodelling, cell cycle control, and immune modulation. The results highlight the effectiveness of combining GCNs with HPC to discover biomarkers, offering insights for improved diagnostic and therapeutic strategies.

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Gene Co-expression Network Analysis Based on GPUs for Biomarkers Discovery in Sarcomas

  • 会议论文

  • 发表日期：

    2026年

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  • Marc Ríos Cadenas,
  • Aurelio López-Fernández,
  • Dulcenombre M. Saz-Navarro,
  • Juan A. Ortega,
  • Francisco A. Gómez-Vela

摘要

Osteosarcoma and Ewing sarcoma are aggressive neoplasms with unfavourable prognoses. Despite advances in treatment, their prognosis remains poor, particularly in cases with metastases. In this context, the identification of new biomarkers and therapeutic targets is critical to improving the treatment of these diseases. This paper proposes a methodology to identify potential biomarkers using Gene Co-expression Networks (GCNs) constructed from transcriptomic data. Using High-Performance Computing (HPC) technologies, large-scale GCNs were efficiently generated and analysed, focusing on genes associated with tumour aggressiveness. Key findings include the identification of six potential biomarkers for Ewing sarcoma (COL11A1, VCAN, BUB1B, CDC20, UBE2C, AURKA) and four for osteosarcoma (NKX2-1, TAL1, GFI1, IKZF1). These genes are involved in processes such as extracellular matrix remodelling, cell cycle control, and immune modulation. The results highlight the effectiveness of combining GCNs with HPC to discover biomarkers, offering insights for improved diagnostic and therapeutic strategies.