Assessing GPX4 as a Blood-Based Biomarker via a LLM-engaged Gene Prioritization and Characterization Workflow

Analyzing blood transcripts for disease development is an established method, and next-generation sequencing allows for a comprehensive genomic assessment. However, clinical applications often require selecting specific genes for targeted panels, and this study focused on gathering information on the GPX4 gene from literature and transcriptome datasets, aided by large language models (LLMs). Using rigorous methodology, we systematically extracted and integrated data, identifying GPX4\u27s link to various diseases and its potential as a biomarker. Our review revealed notable differences in GPX4 expression levels across different clinical conditions, including neurological disorders, liver diseases, diabetes, and lung ailments. This was visualized using interactive plots and further confirmed by blood transcriptome datasets. The routine clinical application of GPX4 is emerging, particularly in oncology, where it correlates with adverse outcomes. This study highlights the promise of targeting GPX4 in blood to manage diseases related to oxidative stress and cancer, opening avenues for understanding and potential interventions, and setting the stage for future research and clinical applications