Enhancing cancer classification accuracy with a self-attention network using panel capture sequencing data

Cancer classification is pivotal for precision oncology, yet traditional methods struggle with the molecular heterogeneity of tumors. Our study introduces a self-attention based Conv1D machine learning network designed for panel capture sequencing data, which is more commonly used in clinical settings. Combining clinical capture sequencing data and The Cancer Genome Atlas data, we achieved an overall classification accuracy of over 90%, with precision rates reaching 100% for cervical and gastric cancers. Additionally, recall rates were highest at 95.79% for gastric cancer and lowest at 77.46% for cervical cancer, demonstrating robust performance across various cancer types. The model identified key genes such as C3orf36, JHY, and TASP1, showing significant differences in mutation counts across cancers. High-impact gene enrichment analysis highlighted critical pathways like acute myeloid leukemia and adipocytokine signaling. This approach not only significantly improves the precision of cancer classification, demonstrating the potential for clinical application, but also enhances our understanding of cancer biology.