Original Title: Deep learning facilitated discovery of prognosis biomarkers and their ligands to improve liver cancer treatment
Journal: International journal of surgery (London, England)
DOI: 10.1097/JS9.0000000000003455
Overview
The DLCP framework improves clinical prognosis for hepatocellular carcinoma by integrating genomics, transcriptomics, and epigenetics directly with survival outcomes. Analyzing 371 patients from The Cancer Genome Atlas, the model successfully stratified the cohort into 298 low-risk and 76 high-risk individuals, demonstrating significant survival differences with a P-value below 0.001. These results were confirmed through validation in an independent cohort of 232 patients. Key molecular signatures, including mutations in EIF2B4 and HCCS, were found to occur exclusively in the high-risk group. The analysis further highlighted Rac family small GTPase 1 (RAC1) as a central biomarker, which distinguished tumor tissue from normal tissue with an area under the curve value of 0.903. Finally, the researchers utilized this computational approach to screen for potential therapeutic agents to target the identified biomarker.
Novelty
The primary innovation lies in the direct incorporation of survival outcomes as the target output within the deep learning architecture. This approach allows the model to prioritize molecular signatures functionally linked to patient longevity. Furthermore, DLCP establishes a comprehensive pipeline that bridges the gap between patient subtyping and drug discovery. While traditional bioinformatics methods often stop at identifying biomarkers, this research integrates molecular docking and a machine learning-based predictive model to identify specific small-molecule ligands for the target. The identification of KGA-1083b, a germacrane-guaiane dimer derivative, as a potent ligand for RAC1 represents a specific advancement in utilizing complex natural products for targeted cancer therapy.
Potential Clinical / Research Applications
The DLCP framework has significant potential for clinical decision-making by providing accurate risk assessments for patients with liver cancer. Clinicians could use these subtyping results to tailor adjuvant therapies or adjust surgical strategies based on a patient's specific molecular risk profile. In pharmaceutical research, the pipeline can be adapted to identify therapeutic targets and corresponding inhibitors for other aggressive malignancies. The focus on natural product dimers suggests that this method could be used to screen vast libraries of understudied chemical compounds, accelerating the development of targeted therapies. Furthermore, the identification of RAC1 as a validated prognostic marker offers a specific target for future drug development efforts aimed at inhibiting metastasis and tumor progression in hepatocellular carcinoma.
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