Our broad long-term goal is to develop a convenient, non-invasive, clinically-used blood biomarker test that can distinguish patients with lung cancer from patients with benign nodules for the early detection of lung cancer. Lung cancer is often diagnosed at an advanced stage. Detecting lung cancer at earlier stages could reduce mortality rates 10- to 50-fold. The current CT scan approach has difficulty distinguishing benign from malignant pulmonary nodules. Patients are frequently over-diagnosed with poor specificity and require further invasive screening, which adds both to their psychological and to their financial burden. It is urgent to develop new non-invasive methods such as identifying blood molecular biomarkers for early detection of lung cancer. Our immediate objective for this proposal is to identify blood lipid markers for the early detection of lung cancer. Lipids have numerous critical biological functions which include membrane structure, energy storage, and signal transduction. Lipids have also been implicated as playing roles in several human diseases, including lung cancer. However, studies generally have been focused on total levels of lipids in a class. Because individual species of a lipid class may have different functions, it is essential to measure their compositions. In the proposed study, we will adopt lipidomics technology which aims to quantify a cell's lipidome, identifying and quantifying individual lipid molecular species on a large scale using mass spectrometry. Our preliminary studies with lung and prostate cancer indicate this technology is robust and promising. We have identified a lipid profile difference between non-lung cancer and lung cancer plasma samples. The sensitivity and specificity of distinguishing non-cancer and lung cancer samples are over 90%. Our hypothesis is that lipidomics profiles will be different between lung cancer and non-malignant cancer plasma samples including benign pulmonary lesions and we will be able to define a lipid list as a predictive signature of lung cancer. To test this hypothesis, we propose to: 1) measure the levels of lipid species in human plasma from non-malignant and lung cancer biospecimens. 2) Mine lipid profile data to identify """"""""lipid markers"""""""" that vary reproducibly between non-malignant samples and lung cancer samples. 3) Validate and test the predictive value of the lipid markers using independent samples. Lipidomics is a rapidly developing novel technology that has not been applied to lung cancer studies. This work could lead to potentially new clinically used markers for early detection of lung cancer. Our findings may provide information that will lead to the development of novel lipid-related drugs to treat lung cancer. In the long term, linking our data with gene expression and proteomics data in lung cancer will give us a complete view of lipid metabolic pathways and networks, as well as new knowledge about their role in lung cancer development.
We seek to identify lipid biomarkers in human blood for the early detection of lung cancer using a new technology called lipidomics, which aims to measure all the lipids in a human body. We suppose the composition and amount of lipids in early-stage lung cancer patients are different from non-lung cancer patients. Based on this, we can distinguish cancer from non-cancer samples. Detecting lung cancer at earlier stages could reduce mortality rates 10- to 50-fold. The current CT scan approach has difficulty distinguishing benign from malignant pulmonary nodules. Patients are frequently over- diagnosed with poor specificity and require further invasive screening. Results from the proposed work will be of significant value to public health because we can eventually develop an invasive, accurate test tool to be clinically used for the early detection of lung cancer, which can save thousands of lives.
|Chen, Hankui; Liu, Helu; Zou, Hanqing et al. (2016) Evaluation of Plasma miR-21 and miR-152 as Diagnostic Biomarkers for Common Types of Human Cancers. J Cancer 7:490-9|
|Hu, Ling; Ai, Junmei; Long, Hui et al. (2016) Integrative microRNA and gene profiling data analysis reveals novel biomarkers and mechanisms for lung cancer. Oncotarget 7:8441-54|
|Chen, Xiaoli; Chen, Hankui; Dai, Meiyu et al. (2016) Plasma lipidomics profiling identified lipid biomarkers in distinguishing early-stage breast cancer from benign lesions. Oncotarget 7:36622-36631|
|Liu, Chang; Lu, Lili; Kong, Quan et al. (2014) Developing discriminate model and comparative analysis of differentially expressed genes and pathways for bloodstream samples of diabetes mellitus type 2. BMC Bioinformatics 15 Suppl 17:S5|
|Li, Yan; Rouhi, Omid; Chen, Hankui et al. (2014) RNA-Seq and Network Analysis Revealed Interacting Pathways in TGF-Î²-Treated Lung Cancer Cell Lines. Cancer Inform 13:129-40|
|Deng, Youping; Ai, Junmei; Guan, Xin et al. (2014) MicroRNA and messenger RNA profiling reveals new biomarkers and mechanisms for RDX induced neurotoxicity. BMC Genomics 15 Suppl 11:S1|