Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets.

ABSTRACT: Saliva, a non-invasive, self-collected liquid biopsy, holds promise for early gastric cancer (GC) screening. This study aims to assess the potential of saliva as a proxy for malignant gastric transformation and its diagnostic value through transcriptomic profiling. Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. Tissue-based models were validated on independent tissue datasets, and subsequently applied to saliva. Additionally, an independent saliva-derived model was created and evaluated using sensitivity, specificity, accuracy, area under the curve (AUC), and likelihood ratio (LR) metrics. Tissue-derived models demonstrated excellent performance, with

SUBMITTER: Lopes C 

PROVIDER: S-EPMC12116728 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature