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

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• Lung cancer exhibits significant intratumoral heterogeneity, complicating treatment strategies.
• Existing resources lack comprehensive integration of multi-omics data for lung cancer research.

Purpose of the Study:

• To develop LOSTdb, a novel database system for lung cancer research.
• To integrate multi-omics data and metadata for molecular subtype annotation.
• To provide a comprehensive resource for understanding lung cancer heterogeneity and advancing precision medicine.

Main Methods:

• Collected and curated 295 multi-omics datasets (RNA-seq, genomics, proteomics, methylation, scRNA-seq).
• Integrated over 10,000 metadata entries from clinical specimens, mouse models, and cell lines (34,393 samples total).
• Annotated samples with classical and NMF-derived meta-program (MP) subtypes; developed cross-searching and analysis tools.

Main Results:

• LOSTdb integrates diverse omics data (bulk and single-cell) with extensive metadata.
• The database enables cross-searching, visualization, and analysis of lung cancer molecular subtypes.
• Includes tools for integrated analysis, significance testing, and target prediction.

Conclusions:

• LOSTdb serves as a valuable, user-friendly resource for lung cancer precision medicine.
• Facilitates deeper understanding of lung cancer molecular subtypes and heterogeneity.
• Empowers researchers with integrated multi-omics data for advanced analyses.