and HBeAg(-) patients - World Journal of Gastroenterology

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A B Abundance TIC: 6-8.D\data.ms 3 000 000 TIC: 6-8-2.D\data.ms (*) 2 800 000 TIC: 6-8-3.D\data.ms (*) 2 600 000 2 400 000 2 200 000 2 000 000 1 800 000 1 600 000 1 400 000 1 200 000 1 000 000 800 000 600 000 400 000 200 000 0 15.00 20.00 25.00 30.00 35.00 40.00 t /min the marker metabolite intensities as variables (lactic acid, butanoic acid, propanoic acid, glycerol, pyrimidine, butanedioic acid, malic acid, citric acid, hexadecanoic acid and uric acid). The PCA scores plot showed that the normal group and cancer group (non-metastasis group and metastasis group) samples were scattered into different regions (Figure 4A). ROC analysis, which was performed using the values determined by the first two components of the PCA model, confirmed the robustness of the PCA model. These first two components could present the ma- WJG|www.wjgnet.com Abundance 3 500 000 3 000 000 2 500 000 2 000 000 1 500 000 1 000 000 500 000 0 28.00 29.00 30.00 31.00 32.00 33.00 34.00 35.00 36.00 Figure 3 The overlay chromatograms of three parallel samples. A: The total ion chromatograms (TICs) of gas chromatography/mass spectrometry analysis; B: Enlarged part of TIC from 28 to 36 min; C: One peak enlarged. Table 2 Marker metabolites found in normal and cancer groups Metabolites Retention time P value 1 TIC: 6-8.D\data.ms TIC: 6-8-2.D\data.ms (*) TIC: 6-8-3.D\data.ms (*) t /min A (normal) B (cancer 2 ) R 3 Lactic acid 7.196 2.4 × 10 -5 79.24 ± 6.1 187.04 ± 71.99 1.36 Butanoic acid 9.412 0.000 16.79 ± 0.52 27.33 ± 4.98 0.63 Propanoic acid 14.293 0.000 60.58 ± 9.79 147.77 ± 15.3 1.43 Glycerol 12.511 0.000 147 ± 8.98 269.13 ± 50.31 0.83 Pyrimidine 14.705 0.000 61.68 ± 8.05 163.11 ± 12.23 1.64 Butanedioic acid 13.933 0.1 × 10 -5 161.51 ± 5.85 267.89 ± 54.64 0.66 Malic acid 19.301 0.000 10.7 ± 1.91 32.15 ± 1.16 2.00 Citric acid 28.768 1.4 × 10 -4 1291.89 ± 364.74 2164.74 ± 529.58 0.68 Hexadecanoic acid 34.612 4.17 × 10 -4 1347.84 ± 304.67 2066.57 ± 437.28 0.53 Uric acid 35.691 0.000 172.2 ± 17.03 214.52 ± 7.74 0.25 1 P values were calculated based on Student t test (significance at P < 0.05); 2 Cancer group included the non-metastasis group and the metastasis group; 3 R value was calculated from the arithmetic mean values of each group. R = (B-A)/A. R with a positive value indicates a relatively higher concentration in cancer group while a negative value means a relatively lower concentration as compared with the normal group. Table 3 Metabolic differences in the two groups Metabolites Retention time P value 1 Hu JD et al . Urinary metabolomic profile and gastric cancer A (non-metastasis) B (metastasis) R 2 Alanine 8.105 0.000 173.75 ± 39.59 19.28 ± 10.63 -0.89 Butanoic acid 9.412 0.000 32.09 ± 1.00 22.58 ± 0.72 -0.30 Glycerol 12.511 0.003 303.23 ± 26.16 235.04 ± 45.64 -0.22 Butanedioic acid 13.933 0.1 × 10 -5 216.36 ± 2.63 319.43 ± 17.89 0.48 L-proline 20.497 0.000 184.99 ± 10.26 117.78 ± 7.05 -0.36 L-threonic acid 20.909 2.28 × 10 -4 284.94 ± 46.47 181.48 ± 37.25 -0.36 Myo-inositol 29.032 0.000 33.08 ± 3.58 114.8 ± 2.20 2.47 1 P values were calculated based on Student t test (significance at P < 0.05); 2 R value was calculated from the arithmetic mean values of each group. R = (B-A)/ A. R with a positive value indicates a relatively higher concentration in metastasis group while a negative value means a relatively lower concentration as compared with the non-metastasis group. jority of all significantly different metabolites among the groups (the percentage is 82.7%). Area under the curve (AUC) value of this PCA model was 1.00 (Figure 4B), which demonstrated a good diagnostic value for gastric cancer. In addition, another PCA model for gastric cancer metastasis constructed by seven marker metabolites (alanine, butanoic acid, glycerol, L-threonic acid, L-proline, butanedioic acid and myo-inositol) could differentiate between the non-metastasis group and the metastasis group (Figure 5A). This PCA model was also validated by 731 February 14, 2011|Volume 17|Issue 6| C
A Regression factor scores 2 for analysis 1 B 1.0 Sensitivity Hu JD et al . Urinary metabolomic profile and gastric cancer 2.00000 1.00000 0.00000 -1.00000 -2.00000 -2.00000 -1.00000 0.00000 1.00000 0.8 0.6 0.4 0.2 0.0 receiver operating characteristic (ROC) analysis (AUC = 1.00, Figure 5B). DISCUSSION The first two principal components Regression factor scores 1 for analysis 1 ROC curve AUC = 1.00 0.0 0.2 0.4 0.6 0.8 1.0 1-specificity In this study, we investigated urinary metabolite profiling using GC/MS. This was assessed non-invasively by measuring two voxels (tumor and healthy controls). We have discriminated the gastric cancer model mice from their healthy controls in a PCA analysis of GC-MS urinary metabolite spectra. Moreover, we could also discriminate the gastric cancer metastasis model mice from the non-metastasis model mice by GC-MS and PCA of urinary metabolites. Some marker metabolites were worth investigating in the future. Compared with the normal group, the level of lactic acid was higher in the cancer group urine. It could be explained that glucose is often converted into lactic acid in cancer cells, which is known as the “Warburg effect”, and cancer cells have a higher rate of aerobic glycolysis [31] . The levels of butanedioic acid, malic acid and citric acid, intermediates of tricarboxylic acid (TCA) cycle, were also found to be higher in the gastric cancer mice. The abnormalities WJG|www.wjgnet.com Group Normal Cancer Figure 4 Principal component analysis model and receiver operating characteristic curve for gastric cancer. A: Principal component analysis (PCA) scores plot of gastric tumor specimens from control specimens based on 10 marker metabolites. The PCA scores plot showed different samples (normal group, cancer group including non-metastasis group and metastasis group) were scattered into different regions; B: Receiver operating characteristic (ROC) analysis was performed using the values determined by the first two components. Area under the curve (AUC) = 1.00. A Regression factor scores 2 for analysis 1 B 1.0 Sensitivity 3.00000 2.00000 1.00000 0.00000 -1.00000 -2.00000 0.8 0.6 0.4 0.2 0.0 ROC curve The first two principal components Group -1.00000 0.00000 1.00000 Regression factor scores 1 for analysis 1 AUC = 1.00 0.0 0.2 0.4 0.6 0.8 1.0 1-specificity Non-metastasis Metastasis Figure 5 Principal component analysis model and receiver operating characteristic curve for gastric cancer metastasis. A: Principal component analysis (PCA) scores plot of non-metastasis group and metastasis group based on 7 marker metabolites. The PCA scores plot showed the samples from non-metastasis group and metastasis group were scattered into two different regions; B: Receiver operating characteristic (ROC) analysis was performed using the values determined by the first two components. Area under the curve (AUC) = 1.00. of these metabolite expressions demonstrated a close correlation of TCA cycle with gastric cancer morbidity along with disordered aerobic respiration and mitochondrial functions. The disorder of aerobic respiration (mainly TCA cycle) and the impairment of mitochondrial enzymes have been reported in other malignancies including colorectal cancer, pheochromocytoma and paraganglioma [22,32,33] . Uric acid, the final metabolite of purines, at enhanced level in cancer mice urine, suggests the abnormalities of purine metabolism in gastric cancer [34] . In our study, the significantly higher levels of glycerol and hexadecanoic acid in cancer than in normal groups were interpreted as increased adipocyte lipolysis in cancer and enhanced expression and function of adipocyte hormone-sensitive lipase (HSL) [35] . Cancer metastasis could be considered as an essential prognostic factor [36] . Figure 5A shows the new constructed tumor metastatic model by seven marker metabolites for the non-metastasis group and the metastasis group. This PCA model was also validated by ROC analysis (AUC = 1.00, Figure 5B). Seven metabolites in this model are capable of predicting the gastric cancer metastasis. Compared with the non-metastasis group, levels of alanine 732 February 14, 2011|Volume 17|Issue 6|
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World Journal of Gastroenterology W
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Robert JL Fraser, Daw Park Jacob Ge
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Italy Donato F Altomare, Bari Piero
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Fernando Azpiroz, Barcelona Ramon B
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S Contents EDITORIAL REVIEW ORIGINA
Page 11 and 12: Contents ACKNOWLEDGMENTS APPENDIX A
Page 13 and 14: Jahn KA et al . Colon cancer and li
Page 23 and 24: Săftoiu A. Imaging techniques in E
Page 29 and 30: Ghimire P et al . Primary GI lympho
Page 33: Ghimire P et al . Primary GI lympho
Page 39: Online Submissions: http://www.wjgn
Page 42 and 43: Table 2 Comparison of hepatitis B e
Page 45 and 46: Utama A et al . HBV core promoter m
Page 47 and 48: Utama A et al . HBV core promoter m
Page 49 and 50: Motoki T et al . Glutamine depletio
Page 51 and 52: Table 2 Nutrient composition of mou
Page 53 and 54: A Motoki T et al . Glutamine deplet
Page 55 and 56: A Counts B % of sub-G0 cell populat
Page 57 and 58: Motoki T et al . Glutamine depletio
Page 59 and 60: Hu JD et al . Urinary metabolomic p
Page 61: Abundance/× 10 6 Abundance/× 10 6
Page 65 and 66: Hu JD et al . Urinary metabolomic p
Page 67: Fu JF et al . NAFLD and metabolic s
Page 70 and 71: developing hypertension (OR: 2.18,
Page 72 and 73: obese and exhibited higher insulin
Page 74 and 75: Online Submissions: http://www.wjgn
Page 77 and 78: Roberts SE et al . Perinatal factor
Page 79 and 80: Roberts SE et al . Perinatal factor
Page 83 and 84: Oksaharju A et al . Probiotics regu
Page 91: Online Submissions: http://www.wjgn
Page 96 and 97: Factors associated with improved su
Page 101 and 102: Koehler-Santos P et al . Identifica
Page 103 and 104: Koehler-Santos P et al . Identifica
Page 109: Goenka MK et al . Capsule endoscopy
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Tatsuki M et al . Hypermagnesemia a
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Online Submissions: http://www.wjgn
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Chang CC et al . Parity and gastric
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Chang CC et al . Parity and gastric
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Zhang XQ et al . EEA model of guine
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Table 3 Measurement of common bile
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Zhang XQ et al . EEA model of guine
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Zhao R et al . Gene expression anal
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Zhao R et al . Gene expression anal
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Qiu HB et al . HBV infection and li
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Qiu HB et al . HBV infection and li
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Shen H et al . K-ras and BRAF mutat
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Instructions to authors Indexed and
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Instructions to authors uniform leg
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Instructions to authors Language ev
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