Clinical and Cross-Domain Validation of an LLM-Guided, Literature-Based Gene Prioritization Framework

Background: We previously published a literature based pipeline for sepsis gene prioritization (PS3 and candidate genes) using an LLM enabled retrieval and judging framework. Here, we extend that work to ask whether these prioritized genes show independent clinical validity and whether the same strategy generalizes to a "drug/obesity/infection" setting. Methods: Using the original LLM guided workflow, we evaluated PS3 and the Candidate set in two new settings. First, we tested 28 day mortality prediction in the independent VANISH sepsis trial, benchmarking PS3 and Candidate against two established immune signatures the Severe or Mild (SoM) signature and Immune Health Metric (IHM) under a uniform logistic regression framework with clinical covariates. Second, we applied the same genome wide screening and tiered judging pipeline to GLP 1/obesity/infection biology centered on semaglutide, comparing Tier 1 and Tier 2 gene sets to STEP trial serum proteomics at gene and Hallmark pathway levels. In parallel, we fine tuned an open weight GPT OSS 20B model on curated sepsis justifications to obtain a domain aware LLM as judge, and compared its scoring behavior with the base model on semaglutide Tier 2 genes. Results: In the full VANISH cohort, PS3 and the Candidate set showed moderate discrimination, whereas SoM remained the strongest single predictor of 28 day mortality. In the Critical/High APACHE II subgroup, PS3 achieved ROC and precision recall performance comparable to, or slightly better than, SoM despite its smaller, knowledge derived composition, indicating that literature prioritized genes capture mortality relevant immune dysregulation under severe illness. In the semaglutide case study, gene level overlap between LLM prioritized genes and differentially abundant serum proteins was modest, but Tier 1 genes recapitulated the main semaglutide responsive metabolic programs from STEP and highlighted additional immune metabolic pathways relevant to infection, with discordances largely explained by serum proteome coverage. The fine tuned judge remained moderately concordant with the base GPT OSS across mechanistic themes, preserving overall ranking while inducing systematic, biologically interpretable shifts in immune and infection related scores. Conclusions : An LLM guided, literature based gene prioritization framework yields compact gene sets that show independent sepsis mortality signal and pathway level concordance in a semaglutide/obesity/infection setting, while a sepsis aware LLM as judge provides domain specific refinements without overturning core rankings. Together, these findings support knowledge grounded, LLM derived gene sets and judges as interpretable components for probing immune dysregulation across diseases and therapies.