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| Fourier Transform Infrared Spectroscopy Analysis of Pleural Mesothelioma and Tuberculous Pleurisy |
| QIU Lu1, 2, ZHAO Yi3, YANG Sheng-jie3, LIAO Chang-hong1, YU Cheng-min3, REN Zhong-hua3, ZHANG Ye-pin3, GAO Shun-yu1, 2, WANG Zhen-ji1, 2, YANG Hai-yan1, 2 |
1. Department of Chemistry and Life Science, Chuxiong Normal University, Chuxiong 675000, China
2. Institute for Bio-resources Development and Utilization in Central Yunnan Plateau，Chuxiong 675000, China
3. The People’s Hospital of Chuxiong Yi Autonomous Prefecture, Chuxiong 675000, China |
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Abstract In this study, with Fourier transform infrared (FTIR) spectroscopy, the structure and content of biological macromolecules in epithelial pleural mesothelioma, fibrous pleural mesothelioma, tuberculous pleurisy, and normal pleural tissue is analyzed. It is found that the FTIR spectra of these four kinds of pleural tissue are similar; however, there are some obvious differences. There is a significant difference in the infrared spectral data between the four pleural tissues (p<0.001), which indicates significant changes in the structure and content of biological macromolecules, namely: (1) pleural mesothelioma protein amides Ⅰ and Ⅱ: peak intensities of nucleic acid at 1 232 cm-1 and lipids at 2 922 cm-1 are significantly higher than those in normal pleural tissue; In the fibrous pleural mesothelioma, peak intensity of protein amides Ⅰ and Ⅱ, peak intensity closely related to nucleic acid at 1 078 cm-1, and peak intensity related to lipids at 2 922 cm-1 and at 2 854 cm-1 are significantly higher than those in epithelial mesothelioma (p<0.05); tuberculous pleurisy protein amides Ⅰ and Ⅱ, nucleic acid peak intensity at 1 232 and 1 078 cm-1 increases slightly, but there are no significant differences compared with normal pleural tissue (p>0.05), peak intensity related to lipid content at 2 922 and 2 854 cm-1 is notably higher than that in normal pleural tissue (p<0.01) and pleural mesothelioma (p<0.05). (2) The relative peak intensity of proteins, nucleic acids, lipids I1 641/I2 922, I1 641/I1 232, I1 232/I1 078, I1 078/I1 546, I1 078/I2 854, I2 922/I1 232, I1 458/I1 400 can effectively enlarge the differences between the four types of pleural tissue, which has better effect than peak intensity; it can be used as an optimization index for diagnosis of pleural mesothelioma. (3) Peak intensity at 1 078 cm-1 of nucleic acid molecule phosphodiester bond C—C/C—O in epithelial pleural mesothelioma and peak intensity at 2 854 cm-1 of lipid are significantly lower than that of fibrous pleural mesothelioma and normal pleural tissue (p<0.05), which shows that the phosphodiester bond of epithelial pleural mesothelioma is seriously fractured, DNA damage is serious, and membrane lipid peroxidation is significant. This indicates that the epithelial pleural mesothelioma deteriorates more seriously than fibrous pleural mesothelioma. (4) FTIR can effectively distinguish fibrous pleural mesothelioma, epithelial pleural mesothelioma, tuberculous pleurisy and normal pleural tissue, and it provides reliable data for the early and rapid diagnosis of pleural mesothelioma and tuberculous pleurisy.
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Received: 2016-08-03
Accepted: 2016-12-29
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[1] Krug L M, Kindler H L, Calvert H, et al. Lancet Oncol.，2015, 16(4): 447.
[2] Pass H I, Levin S M, Harbut M R, et al. New Engl. J. Med.，2012, 367(15): 1417.
[3] Guilbert M, Eklouh-Molinier C, Wehbe K, et al. J. Biomed. Opt.，2014, 19(11)：111612.
[4] Derenne A, Van Hemelryck V, Lamoral-Theys D, et al. BBA-Mol. Basis Dis., 2013, 1832(1): 46.
[5] Grossfeld G D，Carroll P R. Epidemiol. Rev.，2015, 23(1): 173.
[6] Mu X, Kon M, Ergin A, et al. Analyst, 2015, 140(7): 2449.
[7] Zhang L, Fu J, Leng H, et al. Spectrosc. Spect. Anal., 2015, 35(3): 599.
[8] Mu X, Kon M, Ergin A, et al. Analyst, 2015, 140(7): 2449.
[9] Jusman Y, Isa N A M, Ng S C, et al. Procedia Comp. Sci.，2015, 59: 133.
[10] Sun Z J, Liu B，Zhao Y F. Dentomaxillofac. Rad.，2014.
[11] Sharma M, Crosbie J C, Puskar L，et al. Biology., 2013, 89(2): 79.
[12] Tyagi G, Jangir D K, Singh P，et al. DNA and Cell Boil.，2010, 29(11): 693.
[13] Derenne A, Vandersleyen O, Goormaghtigh E. BBA-Mol. Cell Biol. L，2014, 1841(8): 1200.
[14] Mehrotra R, Tyagi G, Jangir D K, et al. J. Ovarian. Res.，2010, 3: 27.
[15] Careche M, Carmona P，Sánchez-Alonso I. Food and Bioprocess Tech.，2015, 8(1): 112.
[16] Supply P, Marceau M, Mangenot S, et al. Nat. Genet.，2013, 45(2): 172.
[17] Stoop E J, Bitter W，Van der Sar A M. Trends Microbiol.，2012, 20(10): 477. |
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