Lead Researcher(s): Arlan Vincent John V. German & Demelo M. Lao
Status: Published
Abstract: Lung cancer is one of the most lethal diseases worldwide. The discovery of its discriminatory biomarkers is crucial to enhance precision diagnosis/prognosis and lower its early detection costs. The study sampled 12 GEO series from the GEO database containing human gene expressions related to lung cancer diagnosis, sub-typing, prognosis, and biomarker identification. These healthy control and lung cancer samples were used to train, fine-tune, and evaluate the designed “DeepGene” model which performed remarkably (AUC = 0.99) compared to the top-performing baseline model (AUC = 0.98) in classifying the samples accordingly. By utilizing DeepSHAP feature importance with the DeepGene model, a novel biomarker discovery method was proposed allowing for 13 discriminatory biomarkers to be identified; four (4) of which are novel (i.e., CCDC141, and Affymetrix probes 238891_at, AFFX-r2-Bs-dap-3_at, AFFX-ThrX-5_at); while the remaining nine (9) biomarkers are validated by existing studies. These 13 biomarkers attained an AUC = 0.99 based on the testing dataset. For future works, biomarker validation of the four novel biomarkers mentioned is recommended.
Keywords:
- Lung cancer
- Biomarker identification
- Deep learning