# 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

> **NIH NIH F99** · EMORY UNIVERSITY · 2024 · $50,474

## Abstract

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 can...

## Key facts

- **NIH application ID:** 10990367
- **Project number:** 1F99CA294242-01
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Ziyin Tang
- **Activity code:** F99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $50,474
- **Award type:** 1
- **Project period:** 2024-09-01 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10990367

## Citation

> US National Institutes of Health, RePORTER application 10990367, 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 (1F99CA294242-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10990367. Licensed CC0.

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