ATdb

ATdb compiles and integrates curated data linking autophagy-related genes and molecular annotations across 25 tumor types to support analysis of autophagy's roles in tumorigenesis, progression, and metastasis.

Key Features:

• Tumor–gene associations: Connects 25 tumor types with 137 genes implicated in autophagy-related pathways.
• Population stratification: Includes 219 population filters for stratified analyses across subpopulations.
• Hazard ratio trend plots: Provides 2,650 hazard ratio trend plots for association and outcome trend inspection.
• MicroRNA interactions: Documents 658 interacting microRNAs linked to autophagy genes.
• Long non-coding RNA interactions: Contains 266 interacting long non-coding RNAs associated with autophagy genes.
• Post-translational modifications: Records 155 post-translational modification entries relevant to autophagy proteins.
• DNA methylation: Integrates 298 DNA methylation records related to autophagy-associated loci.
• Preclinical and clinical resources: Catalogs 331 animal models and 70 clinical trials connected to autophagy and cancer studies.
• Regulator prediction: Enables prediction of autophagy gene regulators in context-dependent manners across precise subpopulations and tumor subtypes.
• Tumor clustering by lncRNAs: Supports clustering of tumors into distinct groups based on gene-related long non-coding RNAs.

Scientific Applications:

• Functional characterization: Investigating autophagy gene contributions to tumorigenesis, progression, and metastasis across tumor types.
• Context-specific regulator discovery: Predicting autophagy gene regulators within defined subpopulations and tumor subtypes.
• Tumor subtype stratification: Defining tumor clusters by gene-related long non-coding RNAs to explore functional implications.
• Integrative molecular analyses: Combining genomic, epigenomic (DNA methylation), post-translational modification, and non-coding RNA data for mechanistic studies.
• Translational links: Relating gene- and model-level annotations to animal models and clinical trials for preclinical and clinical research contexts.

Methodology:

Integrates curated molecular and experimental data types; generates hazard ratio trend plots; performs context-dependent prediction of autophagy gene regulators across subpopulations and tumor subtypes; clusters tumors based on gene-related long non-coding RNAs.