LOSTdb: a manually curated multi-omics database for lung cancer research

Lung cancer is one of the most prevalent malignant tumors with high morbidity and mortality rates worldwide. Extensive multi-omics analyses have revealed significant intratumoral heterogeneity even within the same histopathological subtype. However, a database that systematically integrates multi-omics data for lung cancer research has long been lacking. Here, we developed LOSTdb, a molecular subtype annotation system for lung cancer that integrates multi-omics data and metadata. LOSTdb comprises 295 multi-omics datasets, including bulk RNA-seq, genomic, proteomic, methylation, and scRNA-seq data, with over 10,000 manually curated metadata entries. This resource encompasses high-quality clinical specimens, mouse models, and cell lines, totaling 34,393 samples and more than 1.2 million single cells. Each omics sample was annotated with both literature-based classical subtypes and NMF-derived meta-program (MP) subtypes. The platform supports cross-searching of omics and metadata at the gene and dataset levels, offers multiple visualization and analysis methods, and includes five tool modules, enabling functions such as integrated analysis, significance analysis between metadata as well as between genes and metadata, and target prediction for lung cancer molecular subtypes, serving as an essential tool for lung cancer precision medicine. LOSTdb is a user-friendly interactive database freely accessible at http://lostdbcancer.com:8080 .