Abstract 606P
Background
The incidence of non-tobacco related (non-smoker without second-hand smoke exposure) lung cancer remains a global health problem, thorough knowledge about the molecular features is still lacking. Our aim was to constructed a comprehensive multi-omics profiling that can enlighten the etiology and unique omics features of non-tobacco related lung adenocarcinoma (NTR-LUAD).
Methods
We performed comprehensive genomic, transcriptomic, proteomic, and metabolomic analysis in 87 pairs of tumor and normal tissue in NTR-LUAD patients. We detect the significantly mutated genes (SMGs) and copy number variation (CNV) by mutation significance with covariates (MutsigCV) and GISTIC2.0 software. We also collected the clinical and pathological features to integrate multi-omics analysis. The differential expression about genes and proteins between different groups were identified by limma package. We used argparser to run different metabolin analysis.
Results
At gene level, after excluding the effects of gene length and background mutation rate, we identified 43 SMGs (P<0.005), top gene was EGFR (68%). Mutational signature analysis results showed that the signature aging-related mutational signature was most enrich in NTR-LUAD. A total of 5026 differentially expressed genes (logFC>1 or logFC<-1, P adjust<0.05), 2626 differentially expressed protein (logFC>1 or logFC<-1, P adjust<0.05) and 199 differential metabolites (FC>2 or FC<0.5, VIP>1, P<0.05) were found in differentially analysis between cancer and paracancer tissues. We compared gene expression level and protein expression level between different stages and found that genes and proteins varied with stage were all enriched in cytoskeleton in muscle cells, ECM-receptor interaction, protein digestion and absorption and focal adhesion (P adjust<0.05). All differentially metabolites were enrich in purine metabolism (FDR=0.007).
Conclusions
This multi-omic molecular architecture may help develop strategies for management of NTR-LUAD.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Fujian Province science and technology innovation joint fund project (2019Y9022).
Disclosure
All authors have declared no conflicts of interest.