Magic angle spinning NMR spectroscopic metabolic profiling of gall bladder tissues for differentiating malignant from benign disease

被引:10
作者
Bharti, Santosh Kumar [1 ]
Behari, Anu [2 ]
Kapoor, Vinay Kumar [2 ]
Kumari, Niraj [3 ]
Krishnani, Narendra [3 ]
Roy, Raja [1 ]
机构
[1] Sanjay Gandhi Postgrad Inst Med Sci Campus, Ctr Biomed Magnet Resonance, Lucknow 226014, Uttar Pradesh, India
[2] Sanjay Gandhi Post Grad Inst Med Sci, Dept Surg Gastroenterol, Lucknow 226014, Uttar Pradesh, India
[3] Sanjay Gandhi Post Grad Inst Med Sci, Dept Pathol, Lucknow, Uttar Pradesh, India
关键词
HR-MAS NMR spectroscopy; Gall bladder cancer (GBC); Xantho-granulomatous cholecystitis (XGC); Chronic cholecystitis (CC); Metabolic profiling; Metabolomics; BREAST-CANCER PATIENTS; XANTHOGRANULOMATOUS CHOLECYSTITIS; GALLBLADDER ADENOCARCINOMA; PRENEOPLASTIC LESIONS; CHOLINE METABOLISM; COLORECTAL-CANCER; CARCINOMA; QUANTIFICATION; METABONOMICS; BRAIN;
D O I
10.1007/s11306-012-0431-7
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Gall bladder tissue specimens obtained from 112 patients were examined by high resolution magic angle spinning (HR-MAS) NMR spectroscopy. Fifty one metabolites were identified by combination of one and two-dimensional NMR spectra. To our knowledge, this is the first report on metabolic profiling of gall bladder tissues using HR-MAS NMR spectroscopy. Metabolic profiles were evaluated for differentiation between benign Chronic Cholecystitis (CC, n = 66) and xantho-granulomatous cholecystitis (XGC, n = 21) and malignant gall bladder cancer (GBC, n = 25). Increase in choline containing compounds, amino acids, taurine, nucleotides and lactate as common metabolites were observed in malignant tissues whereas lipid content was found low as compared to benign tissues. Principal component analysis obtained from the NMR data showed clear distinction between CC and GBC tissue specimens; however, 27 % of XGC tissues were classified with GBC. The partial least square discriminant analysis (PLS-DA) multivariate analysis between benign (CC, XGC) and malignant (GBC) on the training data set (CC; n = 51, XGC; n = 15, GBC; n = 19 tissues specimens) provided 100 % sensitivity and 94.12 % specificity. This PLS-DA model when executed on the spectra of unknown tissue specimens (CC; n = 15, XGC; n = 6, GBC; n = 6) classified them into the three histological categories with more than 95 % of diagnostic accuracy. Non-invasive in vivo MRS technique may be used in future to differentiate between benign (CC and XGC) and malignant (GBC) gall bladder diseases.
引用
收藏
页码:101 / 118
页数:18
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