吸烟及非吸烟肺癌患者的尿液代谢组学比较研究
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摘要
目的探讨吸烟对肺癌患者体内代谢物质变化的影响。方法收集唐山市人民医院2012年9月-2017年9月诊治的60例肺癌患者尿液样本,其中发现数据集包括吸烟史20例,无吸烟史20例,验证数据集包括吸烟史10例,无吸烟史10例。采用LC-MS法对肺癌尿液样本进行非靶向代谢组学研究,利用主成分分析法(PCA)和偏最小二乘判别分析法(PLS-DA)进行多元统计学分析。结果本研究发现有吸烟史肺癌组与无吸烟史肺癌组患者的尿液代谢图谱存在显著差异,并检测到17个差异有统计学意义(P<0.05)的代谢特征离子。与无吸烟史肺癌组患者相比,有吸烟史肺癌组患者尿液中3种代谢特征离子含量显著上升,14种代谢特征离子含量显著下降,基于KEGG代谢通路富集分析并发现吸烟会影响肺癌患者体内的酪氨酸代谢通路。此外,本研究还采用独立验证数据集对发现的17个差异有统计学意义的代谢特征离子进行验证,发现这些差异代谢特征能够较好地区分吸烟与非吸烟肺癌患者组,其预测准确度达75%,灵敏度为80%和特异性为70%。结论吸烟肺癌患者呈现出不同于非吸烟肺癌患者的尿液代谢谱图特征,尿液中多种代谢物的含量显著改变表明吸烟会影响肺癌患者体内物质代谢,这为进一步实验与临床研究提供了一定的参考依据,并为实现吸烟与非吸烟肺癌患者的个体化精准医疗提供一定的指导。
Objective To determine the effect of smoking on the changes in metabolites among lung cancer patients. Methods Totally 60 urine samples from lung cancer patients in Tangshan People's Hospital from 2012 to 2017 were collected. The discovery data set included 20 patients with smoking history and another 20 without smoking history, while independent validation data set included 10 patients with smoking history and another 10 without smoking history. Based on the LC-MS method, the untargeted metabolomics of lung cancer was studied, and the statistical analysis was performed with principal components analysis and Partial least squares discrimination analysis. Results Significant differences were found in the urine metabolic profiles between lung cancer patients with smoking history and non-smoking lung cancer patients, and 17 significant differential metabolic features were identified(P< 0.05). Compared with non-smoking lung cancer patients, 3 features in the urine of smoking lung cancer patients were increased significantly, and 14 features were decreased significantly. In addition, we delineated a 17 metabolic features panel that discriminated lung cancer patients with smoking history from patients with non-smoking history with classification accuracy at 75%(sensitivity = 0.8; specificity = 0.7). Moreover, tyrosine metabolism pathway was identified and shown to be associated with smoking factor based on KEGG pathway enrichment analysis. Conclusion Lung cancer patients with smoking history show different metabolic characteristics as compared with those of non-smoking lung cancer patients, indicating that smoking affects the metabolite abnormalities in patients with lung cancer. This study provides some guidance for further experiments and clinical studies, and is helpful in the personalized and precision treatment for smoking and non-smoking lung cancer patients.
Objective To determine the effect of smoking on the changes in metabolites among lung cancer patients. Methods Totally 60 urine samples from lung cancer patients in Tangshan People's Hospital from 2012 to 2017 were collected. The discovery data set included 20 patients with smoking history and another 20 without smoking history, while independent validation data set included 10 patients with smoking history and another 10 without smoking history. Based on the LC-MS method, the untargeted metabolomics of lung cancer was studied, and the statistical analysis was performed with principal components analysis and Partial least squares discrimination analysis. Results Significant differences were found in the urine metabolic profiles between lung cancer patients with smoking history and non-smoking lung cancer patients, and 17 significant differential metabolic features were identified(P< 0.05). Compared with non-smoking lung cancer patients, 3 features in the urine of smoking lung cancer patients were increased significantly, and 14 features were decreased significantly. In addition, we delineated a 17 metabolic features panel that discriminated lung cancer patients with smoking history from patients with non-smoking history with classification accuracy at 75%(sensitivity = 0.8; specificity = 0.7). Moreover, tyrosine metabolism pathway was identified and shown to be associated with smoking factor based on KEGG pathway enrichment analysis. Conclusion Lung cancer patients with smoking history show different metabolic characteristics as compared with those of non-smoking lung cancer patients, indicating that smoking affects the metabolite abnormalities in patients with lung cancer. This study provides some guidance for further experiments and clinical studies, and is helpful in the personalized and precision treatment for smoking and non-smoking lung cancer patients.
引文
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[2]Turner CM,Krewski D,Pope AC,et al. Long-term ambient fine particulate matter air pollution and lung cancer in a large cohort of never-smokers[J]. Am J Respir Crit Care Med,2011,184(12):1374-1381.
[3]Prosch H. Schaefer-Prokop C. Screening for lung cancer[J].Curr Opin Oncol,2014,26(2):131-137.
[4]Zhou C,Yao LD. Strategies to improve outcomes of patients with EGRF-mutant non-small cell lung cancer:review of the literature[J]. J Thorac Oncol,2016,11(2):174-186.
[5]Dziedzic R,Marjanski T,Binczyk F,et al.,Favourable outcomes in patients with early-stage non-small-cell lung cancer operated on by video-assisted thoracoscopic surgery:a propensity score-matched analysis[J]. Eur J Cardiothorac Surg,2018,54(3):547-553.
[6]Romaszko-Wojtowicz A,Bucinski A,Doboszynska A.Impact of smoking on multiple primary cancers survival:a retrospective analysis[J]. Clin Exp Med,2018,18(3):391-397.
[7]Kameyama N,Chubachi S,Hegab AE,et al. Intermittent exposure to cigarette smoke increases lung tumors and the severity of emphysema more than continuous exposure[J].Am J Respir Cell Mol Biol,2018,59(2):179-188.
[8]Li T,ZhangJ,ZhangJH,etal.Nicotine-enhanced stemness and epithelial-mesenchymal transition of human umbilical cord mesenchymal stem cells promote tumor formation and growth in nude mice[J]. Oncotarget,2018,9(1):591-606.
[9]kinoshita T,Chie KS,Muramatsu R,et al. Determination of poor prognostic immune features of tumour microenvironment in non-smoking patients with lung adenocarcinoma[J]. Eur J Cancer,2017,86(15):15-27.
[10] Chatwin J,Povey A,Kennedy A,et al. The mediation of social influences on smoking cessation and awareness of the early signs of lung cancer[J]. BMC Public Health,2014,14:1043.
[11]Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine[J]. Nat Rev Drug Discov,2016,15(7):473-484.
[12]Kaddurah-DaoukR,KrishnanKR.Metabolomics:a global biochemical approach to the study of central nervous system diseases[J]. Neuropsycho Pharmacol,2009,34(1):173-186.
[13]Buas MF,Gu HW,Djukovic D,et al. Candidate serum metabolite biomarkers for differentiating gastroesophageal reflux disease,Barrett’s esophagus,and high-grade dysplasia/esophageal adenocarcinoma[J]. Metabolomics,2017,13(3):23.
[14]Roberts LD,Gerszten RE. Toward new biomarkers of cardiometabolic diseases[J]. Cell Metab,2013,18(1):43-50.
[15]Sreekumar A,Poisson LM,Rajendiran TM,et al. Metabolomic profiles delineate potential role for sarcosine inprostatecancerprogression[J].Nature,2009,457(7231):910-914.
[16]Alberice JV,Armitage EG,Garcia A,et al. Searching for urine biomarkers of bladder cancer recurrence using a liquid chromatography-mass spectrometry and capillary electrophoresis-massspectrometrymetabolomicsapproach[J]. J Chromatogr A,2013,18(13):163-170.
[17]Mathe EA,Patterson AD,Haznadar M,et al. Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer[J]. Cancer Res,2014,74(12):3259-3270.
[18]Luan H,Liu LF,Meng N,et al. LC-MS-based urinary metabolite signatures in idiopathic Parkinson’s disease[J].J Proteome Res,2015,14(1):467-478.
[19]Tautenhahn R,Patti GJ,Rinehart D,et al. XCMS Online:a web-based platform to process untargeted metabolomic data[J]. Anal Chem,2012,84(11):5035-5039.
[20]Gonzalez-DomiguezR, Castilla-Quinter oR,García-Barrera T,et al. Development of a metabolomic approach based on urine samples and direct infusion mass spectrometry[J]. Anal Biochem,2014,465:20-27.
[21]Liu Y,Yieh L,Yang T,et al. Metabolomic biosignature differentiates melancholic depressive patients from healthy controls[J]. BMC Genomics,2016,17(1):669.
[22]Kohl SM,Klein MS,Hochrein J,et al. State-of-the art data normalization methods improve NMR-based metabolomic analysis[J]. Metabolomics,2012,8(Suppl 1):146-160.
[23]XiaJ,SinelnikovIV,HanB,etal.MetaboAnalyst3.0--making metabolomics more meaningful[J]. Nucleic Acids Res,2015,43(W1):W251-257.
[24]Farshidfar F,Weljie AM,Kopciuk K,et al. Serum metabolomic profile as a means to distinguish stage of colorectal cancer[J]. Genome Med,2012,4(5):42.
[25]Korman A,Oh A,Raskind A,et al. Statistical methods in metabolomics[J]. Methods Mol Biol,2012,856:381-413.
[26]Fan Y,Li Y,Chen Y,et al. Comprehensive metabolomic characterization of coronary artery diseases[J]. J Am Coll Cardiol,2016,68(12):1281-1293.
[27]Joo KM,Kim AR,Kim SN,et al. Metabolomic analysis of amino acids and lipids in human hair altered by dyeing,perming and bleaching[J]. Exp Dermatol,2016,25(9):729-731.
[28] Lizewska B,Teul J,Kuc P,et al. Maternal plasma metabolomic profiles in spontaneous preterm birth:Preliminary results[J]. Mediators Inflam,2018,2018:9362820.
[29]Kumar N,Shahjaman M,Mollah MNH,et al. Serum and plasma metabolomic biomarkers for lung cancer[J].Bioinformation,2017,13(6):202-208.
[30]Wishart DS,Tzur D,Knox C,et al. HMDB:the Human Metabolome Database[J]. Nucleic Acids Res,2007,35(Database issue):D521-526.
[31]Billoir E,Navratil V,Blaise BJ. Sample size calculation in metabolic phenotyping studies[J]. Brief Bioinform,2015,16(5):813-819.
[32]Rodríguez DA,Alcarraz-Vizán G,Díaz-Moralli S,et al. Plasma metabolic profile in COPD patients:effects of exercise and endurance training[J]. Metabolomics,2012,8(3):508-516.
[33]Olivera D,Knall C,Boggs S,et al. Cytoskeletal modulation and tyrosine phosphorylation of tight junction proteins are associated with mainstream cigarette smoke-induced permeability of airway epithelium[J]. Exp Toxicol Pathol,2010,62(2):133-143.
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