Multiomics integration prioritizes potential drug targets for lung cancer

BACKGROUND: To provide more treatment options for patients with advanced lung cancer, this study aims to identify new therapeutic drug targets via the integration and analysis of multi-omics datasets. METHODS: In this study, we performed MR and co-localization analysis to identify candidate drug targets associated with lung cancer by leveraging gene expression quantitative trait loci (eQTLs) data from the eQTLGen consortium, and validated these findings orthogonally using protein expression trait loci (pQTL) data. In addition, we performed survival analysis, genetic diagnostic analysis, and differential expression analysis to further test their correlation with lung cancer. Summary-level data from ILCCO and FinnGen R9 were used as discovery datasets, and the TRICL consortium was used as an independent replication cohort. Together, we applied these methods to assess the causal relationship between these putative gene targets and lung cancer risk, and to determine whether they are associated with patient morbidity and prognosis. FINDING: Among 2,645 candidate genes with robust cis-eQTL instruments, we prioritized four genes (SERPING1, THBS3, FLT4 and CHEK1) that showed strong causal associations with lung cancer risk and high posterior probabilities of co-localization (PP.H4 > 0.80) between eQTL and lung cancer GWAS signals. Genetically higher expression of SERPING1, THBS3 and FLT4 was associated with an increased risk of lung cancer, whereas higher CHEK1 expression was associated with a reduced risk. All four putative gene targets were consistently validated across independent cohorts. INTERPRETATION: This multi-omics MR framework identifies genes whose genetically predicted expression is associated with increased or decreased lung cancer risk and may help prioritize candidate targets for future lung cancer drug development.