南方医科大学学报
南方醫科大學學報
남방의과대학학보
JOURNAL OF SOUTHERN MEDICAL UNIVERSITY
2015年
5期
763-766
,共4页
张琳%李玲%孔海瑞%曾方银
張琳%李玲%孔海瑞%曾方銀
장림%리령%공해서%증방은
肾细胞癌%代谢组学%GC-MS%PCA%OPLS-DA
腎細胞癌%代謝組學%GC-MS%PCA%OPLS-DA
신세포암%대사조학%GC-MS%PCA%OPLS-DA
renal cell carcinoma%metabolomics%gas chromatography-mass spectrometry%principal component analysis%orthogonal partial least-squares discriminant analysis
目的：对肾细胞癌（renal cell cancer, RCC）患者的尿液进行代谢组学分析，建立数据模型并筛选特征代谢标志物。方法采用气相色谱质谱联用技术（gas chromatography mass spectrometry, GC-MS）分析27例RCC患者，26例泌尿系其它肿瘤患者及26例健康人的尿液，利用SIMCA-P+12.0.1.0软件进行主成分分析（principal component analysis, PCA）及正交偏最小二乘法-判别分析（orthogonal partial least-squares discriminant analysis,0PLS-DA），并筛选特征代谢产物。结果构建了PCA（R2X=0.846， Q2=0.575）和OPLS-DA（R2X=0.736，R2Y=0.974，Q2Y=0.897）模型，筛选出14种差异代谢产物，主要是有机酸、马尿酸、色氨酸及其降解产物，其中戊酸、丙二酸、戊二酸、己二酸、吲哚乙酸、氨基喹啉、喹啉及色氨酸在RCC患者尿液中的含量显著高于正常人（P<0.01），同时RCC组的尿液中戊酸、苯丙氨酸、6-甲氧基-硝基喹啉的含量显著高于泌尿系其它肿瘤患者（P<0.01）。结论基于GC-MS的代谢组学方法可以区分RCC患者，筛选出的代谢产物可能是RCC的诊断标志物，可进行深入研究。
目的：對腎細胞癌（renal cell cancer, RCC）患者的尿液進行代謝組學分析，建立數據模型併篩選特徵代謝標誌物。方法採用氣相色譜質譜聯用技術（gas chromatography mass spectrometry, GC-MS）分析27例RCC患者，26例泌尿繫其它腫瘤患者及26例健康人的尿液，利用SIMCA-P+12.0.1.0軟件進行主成分分析（principal component analysis, PCA）及正交偏最小二乘法-判彆分析（orthogonal partial least-squares discriminant analysis,0PLS-DA），併篩選特徵代謝產物。結果構建瞭PCA（R2X=0.846， Q2=0.575）和OPLS-DA（R2X=0.736，R2Y=0.974，Q2Y=0.897）模型，篩選齣14種差異代謝產物，主要是有機痠、馬尿痠、色氨痠及其降解產物，其中戊痠、丙二痠、戊二痠、己二痠、吲哚乙痠、氨基喹啉、喹啉及色氨痠在RCC患者尿液中的含量顯著高于正常人（P<0.01），同時RCC組的尿液中戊痠、苯丙氨痠、6-甲氧基-硝基喹啉的含量顯著高于泌尿繫其它腫瘤患者（P<0.01）。結論基于GC-MS的代謝組學方法可以區分RCC患者，篩選齣的代謝產物可能是RCC的診斷標誌物，可進行深入研究。
목적：대신세포암（renal cell cancer, RCC）환자적뇨액진행대사조학분석，건립수거모형병사선특정대사표지물。방법채용기상색보질보련용기술（gas chromatography mass spectrometry, GC-MS）분석27례RCC환자，26례비뇨계기타종류환자급26례건강인적뇨액，이용SIMCA-P+12.0.1.0연건진행주성분분석（principal component analysis, PCA）급정교편최소이승법-판별분석（orthogonal partial least-squares discriminant analysis,0PLS-DA），병사선특정대사산물。결과구건료PCA（R2X=0.846， Q2=0.575）화OPLS-DA（R2X=0.736，R2Y=0.974，Q2Y=0.897）모형，사선출14충차이대사산물，주요시유궤산、마뇨산、색안산급기강해산물，기중무산、병이산、무이산、기이산、신타을산、안기규람、규람급색안산재RCC환자뇨액중적함량현저고우정상인（P<0.01），동시RCC조적뇨액중무산、분병안산、6-갑양기-초기규람적함량현저고우비뇨계기타종류환자（P<0.01）。결론기우GC-MS적대사조학방법가이구분RCC환자，사선출적대사산물가능시RCC적진단표지물，가진행심입연구。
Objective To identify the biomarkers of renal cell cancer (RCC) through urine metabolic analysis. Methods Urine samples of 27 RCC patients, 26 patients with other urinary cancers and 26 healthy volunteers were examined with gas chromatography-mass spectrometry (GC-MS). SIMCA-P+12.0.1.0 software was used for principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) to screen for the differential metabolites. Results PCA (R2X=0.846, Q2=0.575) and OPLS-DA (R2X=0.736, R2Y=0.974, Q2Y=0.897) model were established for the RCC patients and control subjects. Fourteen metabolites were selected as the characteristic metabolites, including pentanoic acid, malonic acid, glutaric acid, adipic acid, amino quinoline, quinoline, indole acetic acid, and tryptophan, whose levels in the urine were significantly higher in the RCC patients than in the normal subjects (P<0.01); the RCC patients showed significantly higher urine contents of pentanoic acid, phenylalanine, and 6-methoxy-nitro quinoline than those with other urinary tumors (P<0.01). Conclusion The urine metabolites identified based on GC-MS analysis can distinguish RCC patients from patients with other urinary cancers and healthy subjects, suggesting their potential as diagnostic markers for RCC.
