Investigation Of The Relationship Between Smoking And Lung Cancer Based On Proteomics And Metabolomics

Objective and SenseLung cancer is the malignant tumors occur in the bronchial epithelium, generally refers to a tumor of the lung parenchyma. In recent years, the global incidence of lung cancer increased10-30times in men and3-8times in women. In men, lung cancer is the most common cancer in the world, and it has highest malignant mortality; in women, lung cancer is the fourth most common cancer worldwide, and has the second highest malignant mortality. And smoking is closely related with lung cancer.80%of the lung cancer in men and50%of the lung cancer in women are associated with smoking. While China, a big country of production and consumption of tobacco, has a very large number of smokers, and this number will continue to rise in the next few years. It is also important to notice that patients who had an impaired lung function because of a long-term history of smoking may not tolerate surgeries. Current lung cancer diagnosis mainly relies on computed tomography (CT) and CT-mediated biopsy. However, this method is not suitable for screening for lung cancer as it costs a lot of time and money, and it could not discover the small lesion of lung cancer (<2mm). Biomarkers like CEA can only be used for monitoring the relapse and metastasis due to their less sensitivity and specificity. Currently, there are no easy-to-use way to screen for lung cancer with high sensitivity and specificity, especially in the smokers (high-risk population). The purpose of this study is to comprehensively study the relationship between smoking and lung cancer utilizing systems biology. Proteomic research on smoking exposure rat models was performed in order to reveal the protein changes in serum, and to evaluate the overall effect of smoking. Look for protein and metabolites that serve as a bridge between smoking and lung cancer by comparing the protein and metabolic profiles in lung cancer patients and health control with or without smoking histories. Bioinformatics was applied to find biomarkers in serum and urine as a screening tool with high sensitivity and specificity for lung cancer in smokers and non-smokers, and to established corresponding predictive models.Materials and MethodsIn this study, short-term and long-term smoking exposure rat models were established. For each rat model,60rats were randomly assigned to control group, low and high toxicity groups. Rats were exposed to one cigarette per day per rat for3days in short-term exposure model and for90days in long-term exposure model. Rat serum samples were processed in CM10chip and examined using SELDI-TOF-MS1, and the raw mass spectrometry data were analyzed by ZJU-PDAS. Protein matches for selected protein peaks were found in Swiss-Prot database using TagIdent tool. Meanwhile,59serum samples of patients with lung cancer (34patients with previous history of smoking,20patients with no previous history of smoking) and103serum samples of healthy control (45cases with previous history of smoking,58cases with no previous history of smoking) were collected.49urine samples of lung cancer (31of which have a history of smoking) and79urine samples of health control (36of which have a history of smoking) were collected. Serum samples and urine samples were from the same population, part of the urine samples were excluded due to an excessively high creatinine value. Serum samples were processed with weak cation exchange (WCX) magnetic beads and then analyzed by MALDI-TOF-MS, while urine samples were analyzed by GC/MS. ZJU-PDAS was used for analysis of the data obtained in proteomics and metabolomics.ResultsIn rat smoking exposure models, a total of189protein peaks were identify in the short-term exposure model and225protein peaks in the long-term exposure model. A p-value of less than0.05and a fold change greater than1.2were selected as cutoff value. Combined with the shape of peak and cluster between samples,8differentially expressed protein peaks were found. One protein peak with a mass over charge ratio (m/z) of3151was observed in short-term exposure model, and the rest seven peaks were observed in long-term exposure model, and there mass over charge ratio are3502,3546,5653,5854,7233,7419and8005. Possible protein matches were found in Swiss-Prot database using TagIdent tool. And pituitary adenylate cyclase-activating polypeptide (PACAP) is a match for peak3151(highly expressed in low and high toxicity groups), while metastasis-suppressor KISS-1is a match for peak5653(low expressed in low and high toxicity groups). Previous studies showed that elevated PACAP is related with ischemic heart disease and non-small cell lung cancer, and decreased KISS-1levels can be observed in malignancies with high metastatic potential.In the investigation of serum proteomics of lung cancer, the serum protein profiles of lung cancer patients and health control with smoking histories were obtained. And8differentially expressed protein peaks were found with satisfactory p value, fold changes, shape of peak and clustering. They were2485,4790,2892,6010,4878,1324,3045and4809. These8protein peaks can be regarded as potential biomarkers to screen for lung cancer in smokers. Similarly,6protein peaks were found as potential biomarkers to screen for lung cancer in non-smokers. And these peaks were2485,2706,4790,3555,2513and2549. By comparing the differentially expressed protein peaks in smokers and non-smokers, we found two protein peaks,3045and6010, which were only differentially expressed between non-smoking lung cancer patients and health control. These two peaks may be associated with lung cancer caused by smoking. Meanwhile, two predictive models with satisfactory accuracies based on proteins were eastablished to screen for lung cancer in smokers and non-smokers.In the investigation of the urinary metabolomics of lung cancer,20differential expressed endogenous metabolites were shared in the urine samples of lung cancer patients and health control, including alanine, ethanedioic acid, phosphate, glycine, succinic acid, uracil, serine, threonine,5-oxyproline, ribose, aconitic acid, citric acid, glucose, galactose, tyrosine, hexadecanoic acid, inositol, uric acid, octadecanoic acid, and pseudo uridine. Two predictive models based on metabolites in urine to screening for lung cancer in smokers and non-smokers were established using genetic algorithm and SVM (both of these were included in ZJU-PDAS), with accuracy of89.22%(lung cancer) and86.11%(health controls) for the former model, and73.33%(NSCLC) and95.35%(health control) for the latter model, respectively. Meanwhile, we found differentially expressed metabolites in urine of lung cancer in smokers and non-smokers. In smokers, ribose, glucose, ethanedioic acid, phosphate, galactose, inositol, uric acid, citric acid and aconitic acid were found to be differentially expressed. In non-smokers, inositol, ribose, galactose, glucose, uracil, and citric acid were found to be differentially expressed. These small molecule metabolites could be used as potential urine biomarkers for screening for lung cancer in smokers and non-smokers. By further comparing the differentially expressed metabolites in urine between smokers and non-smokers, we found four metabolites that were differentially expressed:ethanedioic acid, phosphate, uric acid and aconitic acid. These four metabolites are probably involved in smoking related lung cancer.Conclusions and InnovationsIn this study, we applied proteomics technology to explore the change of serum protein caused by the cigarette smoking exposure, and comprehensively evaluated the impact of the cigarette smoking. And proteomics and metabolomics were, applied to systematically and comprehensively study the relationship between the lung cancer and smoking.We found8smoking-related protein peaks in smoking exposure rat model. Among these peaks, two protein peaks,3151(highly expressed) and5653(low expressed) were identified as Pituitary adenylate cyclase-activating polypeptide (PACAP) and Metastasis-suppressor KISS-1in database. Previous studies showed that elevated PACAP is related with ischemic heart disease and non-small cell lung cancer, and decreased KISS-1levels can be observed in malignancies with high metastatic potential.In proteomics research of lung cancer,8and6protein peaks were found to screen for lung cancer in smokers and non-smokers, respectively. Moreover, we found that protein peaks3045and6010may be associated with lung cancer caused by smoking.In metabolomics research of lung cancer,9and6metabolites in urine were found to be potential biomarkers to screen for lung cancer in smokers and non-smokers, respectively. By further analyzing the data, we found that the following four metabolites: oxalic acid, phosphoric acid, uric acid and cis-aconitic acid may be associated with lung cancer caused by smoking.These proteins and small molecule metabolites provided evidence on association of smoking and lung cancer in molecule level, and also provided a basis for the mechanism study of lung cancer caused by smoking.Moreover, predictive models with sufficient sensitivity and specificity were established based on proteins and metabolites to screen for lung cancer in smokers and non-smokers, respectively. Our study provided new insight into lung cancer diagnosis, screening and monitoring. Especially, we offered a more focused and specialized predictive model to screen for lung cancer in smokers.