|
|
|
|
|
|
Research of Fourier Transform Infrared (FTIR) Spectroscopy with Multivariate Analysis as Novel Diagnostic Tool for Metastatic Rectal Cancer Lymph Nodes |
LIU Dong1,SONG Bin1, SUN Xue-jun2*, XU Yi-zhuang3*, LONG Yan-bin1, DU Jun-kai2, ZHENG Jian-bao2, LIU Bin1, DUAN Xiang-long1, WANG Jian-hua1, LIU Si-da1, MAO Zhi-jun1, ZHANG Yuan-fu3, WU Jin-guang3 |
1. Second Department of General Surgery, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
2. Department of General Surgery, First Affiliated Hospital of Medical College of Xi’an Jiaotong University, Xi’an 710061, China
3. College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China |
|
|
Abstract The aim of the research is to estimate Fourier transform infrared spectroscopy (FTIR) as a novel diagnostic tool for rectal cancer metastatic lymph nodes. 160 freshly normal and malignant lymph nodes were collected from 80 rectal cancer patients for spectrum analysis. FTIR spectra were generated from tissues samples using infrared wavenumber from 4 000 to 1 000 cm-1 region. The ratios of spectral intensity and relative intensity ratios (I/1 460) were calculated. Principal component analysis (PCA) and Fisher’s Linear discriminant analysis (LDA) were applied to distinguish the malignant from normal. From 4 000 to 1 700 cm-1, principal components 1 (PC1) was 3 260 cm-1 and PC2 was 1 740 cm-1; from 1 700 to 1 000 cm-1, PC1 was 1 640 cm-1 ab PC2 was 1 080 cm-1. Four prominent significant wavenumbers at 1 080 , 1 640, 1 740 and 3 260 cm-1 separated cancer spectra from normal spectra into two distinct groups using PCA. T test associated with relative intensity ratios (I1 080/I1 460, I1 640/I1 460, I3 260/I1 460, I1 740/I1 460) revealed that these wavenumbers were also able to distinguish cancer and normal lymph nodes spectra. The bands 3 260, 1 640,1 550, 1 080 and 1 740 cm-1, associated with Protein, nucleic acid and lipid. Compared with normal lymph nodes tissues, malignat lymph nodes significant increased (p<0.05),but 1 740 cm-1 decreased. Six parameters (wavenumbers 1 080 and P1 300 cm-1, relative intensity ratios I1 080/I1 460, I1 640/I1 460, I3 260/I1 460, I1 740/I1 460) were selected as independent factors to perform discriminant functions. The sensitivity for PCA/LDA mode in diagnosing lymph nodes was 87.5% by discriminant analysis. The results demonstrate that FTIR is as a useful technique for detection malignant and normal lymph nodes, FTIR may be applied in clinical as a noninvasive, high sensitivity and specificity method for lymph nodes diagnosis.
|
Received: 2016-01-17
Accepted: 2016-05-16
|
|
Corresponding Authors:
SUN Xue-jun, XU Yi-zhuang
E-mail: sunxy@mail.xjtu.edu.cn;xyz@pku.edu.cn
|
|
[1] Lips D J, Koebrugge B, Liefers G J, et al. BMC Surg., 2011, 11: 11.
[2] Alaiyan B, Ilyayev N, Stojadinovic A, et al. BMC Cancer, 2013, 13(1): 196.
[3] LUO Bi-rong, LIU Gang, SHI You-ming, et al(罗庇荣, 刘 刚, 时有明, 等). Infrared Technology(红外技术), 2009, 31(1) : 39.
[4] LIU Ya-qi, SANG Chang-ye, XU Yi-zhuang,et al(刘亚奇, 桑畅野, 徐怡庄, 等). Chmical Journal of Chinese Universities-Chinese(高等学校化学学报), 2013, 34(10):2279.
[5] Lewis P D, Lewis K E, Ghosal R, et al, BMC Cancer, 2010 ,10: 640.
[6] Dong L, Sun X, Chao Z, et al. Spectrochimica Acta A, Mol. Biomol. Spectrosc., 2014, 122: 288.
[7] Gao Y F, Huo X W, Dong L, et al. Mol. Med. Rep., 2015, 11: 2585.
[8] BAI Yue-kui, YU Li-wei, ZHANG Le, et al(白月奎, 余力伟, 张 乐 等). Spectrocopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(3): 599.
[9] Zwielly A, Mordechai S, Sinielnikov I, et al. Med. Phys., 2010, 37(3): 1047.
[10] Bogomolny E, Huleihel M, Salman A, et al. Analyst, 2010, 135(8): 1934.
[11] Loh Z H, Sia B Y, Heng P W S, et al. AAPS Pharm. Sci. Tech., 2011, 12(4): 1374. |
[1] |
GUO Ya-fei1, CAO Qiang1, YE Lei-lei1, ZHANG Cheng-yuan1, KOU Ren-bo1, WANG Jun-mei1, GUO Mei1, 2*. Double Index Sequence Analysis of FTIR and Anti-Inflammatory Spectrum Effect Relationship of Rheum Tanguticum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 188-196. |
[2] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[4] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[5] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
[6] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[7] |
JIA Zong-chao1, WANG Zi-jian1, LI Xue-ying1, 2*, QIU Hui-min1, HOU Guang-li1, FAN Ping-ping1*. Marine Sediment Particle Size Classification Based on the Fusion of
Principal Component Analysis and Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3075-3080. |
[8] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[9] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
[10] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
[11] |
CAO Qian, MA Xiang-cai, BAI Chun-yan, SU Na, CUI Qing-bin. Research on Multispectral Dimension Reduction Method Based on Weight Function Composed of Spectral Color Difference[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2679-2686. |
[12] |
TIAN Ze-qi1, WANG Zhi-yong1, YAO Jian-guo1, GUO Xu1, LI Hong-dou1, GUO Wen-mu1, SHI Zhi-xiang2, ZHAO Cun-liang1, LIU Bang-jun1*. Quantitative FTIR Characterization of Chemical Structures of Highly Metamorphic Coals in a Magma Contact Zone[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2747-2754. |
[13] |
ZHANG Xiao-xu1, LIN Xiao-xian3, ZHANG Dan2, ZHANG Qi1, YIN Xue-feng2, YIN Jia-lu3, 4, ZHANG Wei-yue4, LI Yi-xuan1, WANG Dong-liang3, 4*, SUN Ya-nan1*. Study on the Analysis of the Relationship Between Functional Factors and Intestinal Flora in Freshly Stewed Bird's Nest Based on Fourier Transform Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2452-2457. |
[14] |
ZHANG Zi-hao1, GUO Fei3, 4, WU Kun-ze1, YANG Xin-yu2, XU Zhen1*. Performance Evaluation of the Deep Forest 2021 (DF21) Model in
Retrieving Soil Cadmium Concentration Using Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2638-2643. |
[15] |
CHEN Wan-jun1, XU Yuan-jie2, LU Zhi-yun3, QI Jin-hua3, WANG Yi-zhi1*. Discriminating Leaf Litters of Six Dominant Tree Species in the Mts. Ailaoshan Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2119-2123. |
|
|
|
|