LAUSR

e20514 Background: To develop the diagnostic signatures for lung cancer (LC) by gut microbiome and urine metabolomics profiling with multi-omics approach. Methods: We surveyed 145 Fecal Samples and 120 Urine Samples in a cohort containing 109 LC patients and 50 healthy individuals. The total cohort had been separated into testing set and validation set. The testing set comprised 47 patients and 20 healthy individuals (8 first-degree relatives and 12 non-relatives), while the validation set had 48 patients and 30 healthy individuals (14 first-degree relatives and 16 non-relatives). The urine research set comprised 100 patients and 18 first-degree relatives as healthy individuals. Gut microbiota was analyzed through the 16S ribosomal RNA (rRNA) gene sequencing and shotgun metagenomics. Urine untargeted metabolomics was analyzed by liquid chromatography–mass spectrometry (LC–MS) metabolomic analysis. Results: LC patients had a significant shift of intestinal microbiota composition and functional genes distribution compared with healthy individuals. Diagnostic model had been achieved by combining gut microbiome and urine metabolomics profiling, the area under curve (AUC) of the testing set was 0.9997. The model performed reasonably well in the validation set with the AUC of 0.9769. We also found a significant decrease of Prevotella in LA-NSCLC patients and it was negatively correlated with multiple functions in KEGG level 2 in healthy individuals. Conclusions: The current study employs a multi-omics approach, analyses the fecal microbiome and urine metabolites from LC patients and healthy individuals/relatives using a standardized pipeline, and identified disease-associated microbiome shifts across datasets using statistical analysis. We inferred a microbiome-metabolite catalog and its molecular pathway and functional link to host targets, which could be further utilized to aid lung cancer diagnosis and treatment decision.