Summary of project PR001730
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001730. The data can be accessed directly via it's Project DOI: 10.21228/M8371M This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
| Project ID: | PR001730 |
| Project DOI: | doi: 10.21228/M8371M |
| Project Title: | A nested case-control study of untargeted plasma metabolomics and lung cancer risk among never-smoking women in Shanghai Women’s Health Study |
| Project Summary: | Background: The etiology of lung cancer among never smokers has not been fully elucidated despite 15% of cases in men and 53% in women worldwide are not smoking-related. Metabolomics provides a snapshot of dynamic biochemical activities, including those found to be driving tumor formation and progression. This study used untargeted metabolomics with network analysis to agnostically identify network modules and independent metabolites in pre-diagnostic blood samples among never-smokers to further understand the pathogenesis of lung cancer. Methods and Findings: Within the prospective Shanghai Women’s Health Study, we conducted a nested case-control study of 395 never-smoking incident lung cancer cases and 395 never-smoking controls matched on age. We performed liquid chromatography high-resolution mass spectrometry to quantify 20,348 metabolic features in plasma. We agnostically constructed 28 network modules using a weighted correlation network analysis approach and assessed associations for network modules and individual metabolites with lung cancer using conditional logistic regression models, adjusting for covariates. We accounted for multiple testing using a false discovery rate (FDR) < 0.20. We identified a network module of 122 metabolic features enriched in lysophosphatidylethanolamines that was associated with all lung cancer combined (p = 0.001, FDR = 0.028) and lung adenocarcinoma (p = 0.002, FDR = 0.056) and another network module of 440 metabolic features that was associated with lung adenocarcinoma (p = 0.014, FDR = 0.196). Metabolic features were enriched in pathways associated with cell growth and proliferation, including oxidative stress, bile acid biosynthesis, and metabolism of nucleic acids, carbohydrates, and amino acids, including 1-carbon compounds. Conclusions: Our prospective study suggests that untargeted plasma metabolomics in pre-diagnostic samples could provide new insights into the etiology of lung cancer in never-smokers. Replication and further characterization of these associations are warranted. |
| Institute: | Emory University |
| Department: | Gangarosa Department of Environmental Health |
| Laboratory: | Comprehensive Laboratory for Untargeted Exposome Science |
| Last Name: | Walker |
| First Name: | Douglas |
| Address: | 1518 Clifton Rd, CNR 7025, Atlanta, GA 30322 |
| Email: | Douglas.walker@emory.edu |
| Phone: | (404) 727-6123 |
| Funding Source: | This work is partly supported by National Natural Science Foundation of China (NSFC 91643203) and the National Institutes of Health/National Cancer Institute (HHSN261201500229P). |
| Publications: | A nested case-control study of untargeted plasma metabolomics and lung cancer risk among never-smoking women in Shanghai Women’s Health Study. In review |
| Contributors: | Mohammad L Rahman, Xiao-Ou Shu, Douglas Walker, Dean P Jones, Wei Hu, Bu-tian Ji, Batel Blechter, Jason YY Wong, Qiuyin Cai, Gong Yang, Yu-Tang Gao, Wei Zheng, Nathaniel Rothman, Qing Lan |
Summary of all studies in project PR001730
| Study ID | Study Title | Species | Institute | Analysis(* : Contains Untargted data) | Release Date | Version | Samples | Download(* : Contains raw data) |
|---|---|---|---|---|---|---|---|---|
| ST002773 | A nested case-control study of untargeted plasma metabolomics and lung cancer risk among never-smoking women in Shanghai Women’s Health Study | Homo sapiens | Emory University | MS* | 2024-02-28 | 1 | 1152 | Uploaded data (277.5G)* |
