Investigating the molecular signatures and pathways indicative of air pollution toxicity in lung carcinogenesis using single- and multi-omics analyses of genomics, proteomics, and metabolomics

Project Summary Air pollution is a known environmental risk factor for lung cancer, but the detailed mechanisms driving air pollution-induced carcinogenesis and the key molecular events involved remained underexplored. Understanding the underlying molecular mechanisms and pathways is vital for the development of targeted

preventive and therapeutic strategies for lung cancer induced by air pollution. Our previous metabolomics study findings have suggested the important roles of amino acids and peptides (the building blocks of proteins) in modifying the association between air pollution exposure and lung cancer risk. Comprehensive proteomics

analysis will provide critical insights into how these biomolecules are perturbed in air pollution-induced lung cancer. Although high-throughput single omics approaches have shown significant potential in revealing biological responses to air pollution exposures and lung cancer development, they often overlook

interconnections among omics layers. Multi-omics integration can offer a more holistic view of underlying mechanisms. Moreover, omics-based risk prediction models have emerged as promising tools to identify individuals at high risk of lung cancer, but their development and application are still lacking.

During the F99 phase, I will focus on investigating key molecular signatures and pathways underlying air pollution toxicity in lung carcinogenesis. In Aim 1.1, I will determine the potential mediation role of proteins in the causal pathway from air pollution exposure to lung cancer. Using an advanced proteomics analysis with Meet-

In-The-Middle and high-dimensional mediation approaches, I will comprehensively evaluate the protein profiles to understand their involvement in mediating the etiology of air pollution-induced lung cancer. In Aim 1.2, I will conduct innovative multi-omics integration across proteomics, genomics, and metabolomics to identify a highly

correlated molecular network linking air pollution toxicity with elevated lung cancer risk. Using a posteriori integration and a priori integration, I expect to identify a consistent molecular network, consisting of novel and closely related omics signals including single nucleotide polymorphisms, proteins, and metabolites, that unveils

air pollution's role in lung cancer development. Although low-dose computed tomography is the standard screening for lung cancer, it's primarily recommended for heavy former and current smokers. Notably, approximately 45% of lung cancer cases occur in light smokers and never-smokers falling outside the recommendation guidelines. Transitioning to my K00 phase at a world-

class cancer research institute, I will focus on developing omics-based risk prediction models to enhance lung cancer risk stratification by smoking status. In Aim 2.1, I will develop genomics, proteomics, and metabolomics risk scores in ever- and never-smokers separately and evaluate the associations of individual and combined risk

scores with lung cancer risk. In Aim 2.2, I will evaluate the predictive performance of individual and combined omics-based risk scores for lung cancer risk by smoking status.