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    This study used transcriptome-wide association studies to identify 109 lung cancer risk genes, including novel discoveries unrelated to smoking, offering new insights into lung cancer biology and potential drug targets.

    Area of Science:

    • Genetics
    • Oncology
    • Bioinformatics

    Background:

    • Genome-wide association studies (GWASs) have identified numerous lung cancer loci, but causal genes remain largely unknown.
    • Identifying these genes is crucial for understanding lung cancer development and for therapeutic advancements.

    Purpose of the Study:

    • To conduct a lung tissue-specific transcriptome-wide association study (TWAS) to identify novel lung cancer risk genes.
    • To explore the biological mechanisms and potential therapeutic targets associated with these genes.

    Main Methods:

    • Constructed gene expression prediction models using normal lung tissue data (Vanderbilt Thoracic Biorepository and GTEx).
    • Applied these models to a large lung cancer GWAS meta-analysis (55,174 cases, 1,294,174 controls).
    • Performed smoking-conditional analysis, cell-type-specific colocalization, Mendelian randomization, and in vitro functional validation.

    Main Results:

    • Identified 109 unique lung cancer risk genes, with 71 being novel discoveries and 13 in novel loci.
    • Discovered 52 genes unrelated to smoking behavior and identified cell-type-specific colocalization for seven genes.
    • Found 17 genes targeted by 58 drugs and validated three genes through in vitro experiments.

    Conclusions:

    • This TWAS identified novel candidate risk genes for lung cancer and its subtypes.
    • The findings provide insights into lung cancer biology, including smoking-independent pathways and cell-type specificities.
    • The study highlights potential causal genes with translational utility for drug development and therapeutic strategies.