Unveiling novel potential drug targets for lung cancer through Mendelian randomization analysis

Lung cancer (LC) is among the most prevalent cancers globally, posing a significant threat to human health. This study employed Mendelian randomization (MR) analysis to identify key drug targets for LC treatment. MR results from the inverse variance weighted (IVW) algorithm highlighted 352 expression quantitative trait loci (eQTLs) and 31 protein quantitative trait loci (pQTLs) causally associated with LC. Sensitivity and Steiger analyses confirmed that 305 eQTLs and 28 pQTLs exhibited a robust causal relationship with LC. Colocalization analysis further identified 20 eQTLs as potential drug targets for LC. Predictions were made for 257 drugs and 17 diseases, establishing a target-drug network that included PTGFR-D005557 and IREB2-C004925, among others. The drugs-diseases network revealed associations such as D007213 with Liver Cirrhosis and D013749 with Schizophrenia. Notably, the strongest binding interaction was observed between Valproic acid and eight genes (BRAT1, H2BC11, IREB2, MICAL1, MPHOSPH6, PTGFR, RHNO1, SERPING1), suggesting a significant molecular interaction. Ultimately, seven key drug targets (SERPING1, TDRD9, GBAP1, FAM241A, ZKSCAN4, ZKSCAN3, Z94721.1) were consistently identified across two MR studies and validated. These targets offer new avenues for LC treatment, highlighting their potential in therapeutic development.