HLungDB

HLungDB compiles experimentally validated lung cancer-related genes, proteins, and microRNAs together with clinical, transcriptional regulation, SNP, and epigenetic data to enable study of molecular interactions and regulatory mechanisms in lung carcinogenesis.

Key Features:

• Extensive Molecular Data Integration: Contains 2,585 experimentally validated genes and 212 experimentally validated miRNAs associated with lung carcinogenesis derived from literature mining.
• Clinical Correlation: Links molecular entries to clinical information to enable correlations between genetic and epigenetic features and clinical outcomes.
• Transcriptional Regulation Insights: Includes transcription factor-binding motifs, gene promoters, and single nucleotide polymorphism (SNP) sites associated with genes.
• Epigenetic Regulation Data: Incorporates data on genes subject to epigenetic regulation relevant to lung cancer.
• Data Curation: Integrates text mining of the scientific literature with manual extraction and validation of experimentally supported associations.

Scientific Applications:

• Network analysis of molecular interactions: Investigate interactions among genes, proteins, and miRNAs implicated in lung cancer.
• Stage-specific alteration exploration: Explore genetic and epigenetic alterations that drive lung carcinogenesis at different stages.
• Therapeutic target identification: Identify potential therapeutic targets and molecular pathways involved in lung cancer progression.
• Clinical correlation for precision medicine: Correlate molecular findings with clinical data to inform diagnosis, prognosis, or personalized treatment strategies.

Methodology:

Data were obtained by text mining of scientific literature followed by manual extraction and validation of experimentally supported gene and miRNA associations.