- Email: [email protected]
Comparison of hair medulla from lymph node metastasis and non-lymph node metastasis gastric cancer patients using synchrotron radiation infrared microspectroscopy
Infrared Physics and Technology 104 (2020) 103147
Contents lists available at ScienceDirect
Infrared Physics & Technology journal homepage: www.elsevier.com/locate/infrared
Comparison of hair medulla from lymph node metastasis and non-lymph node metastasis gastric cancer patients using synchrotron radiation infrared microspectroscopy Chao Lia,b,c,1, Qipeng Wua,1, Ling Zonga, Yuan Zhoua, Xin Wanga,
T
⁎
a
School of Basic Medical Sciences, Anhui Medical University, Hefei, Anhui 230032, China The Second Affiliated Hospital, Anhui Medical University, Hefei, Anhui 230601, China c National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui 230029, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Gastric cancer Lymph node metastasis SR-IR microspectroscopy Hair medulla Discriminant analysis
Lymph node metastasis often occurs in gastric cancer, hence it is important to explore new methods for diagnosing lymph node metastasis before surgery. In this study, synchrotron radiation infrared (SR-IR) microspectroscopy was used to compare hair medulla from lymph node metastasis and non-metastasis gastric cancer patients. Peak-area ratios indicated that significant alterations might occur in lipids, proteins and nucleic acids after lymph node metastasis. Discriminant analysis was implemented on the basis of peak-area ratios and obtained the following results: the sensitivity was 80.56%, the specificity was 82.76%, the positive predictive value was 85.29% and the accuracy was 81.54%. This study showed that biomacro-molecules in hair medulla were influenced by lymph node metastasis, indicating that IR spectra of hair medulla may be potentially used to diagnose lymph node metastasis before gastric cancer surgery.
1. Introduction In recent years, the incidence and mortality of gastric cancer remain very high worldwide due to environmental factors and Helicobacter pylori infection, etc. [1]. Although the techniques of surgery, radiotherapy and chemotherapy have made remarkable progress, five years survival rate of gastric cancer patients is still less than 30% [2]. Incomplete lymph node dissection is an important reason for low survival rate, which will cause metastasis after surgery as well as influencing the efficacy of radiotherapy and chemotherapy [3,4]. Abdominal lymph node metastasis is the typical biological characteristics of gastric cancer. According to the status of lymph node metastasis, surgeons will select reasonable surgical method and provide scientific guidance for postoperative treatment. At present, imaging examination methods are commonly used for diagnosing lymph node metastasis before surgery, however, they have limitations as below: CT (computed tomography), EUS (endoscopic ultrasonography) and MRI (magnetic resonance imaging) lack the uniform diagnostic criteria, which may lead to higher misdiagnosis rate [5]. PET/CT (positron emission tomography/CT) is expensive, which limits its clinical
application [6]. Pathological examination is “the gold standard” for diagnosing lymph node metastasis during surgery, but it wastes time and increases patients’ risk. FTIR spectroscopy can reflect vibrational characteristics of biomacro molecules such as nucleic acids, proteins and lipids, etc., hence it has been applied to investigate cancers and other diseases [7–11]. In recent years, FTIR spectroscopy has been used to investigate lymph node metastasis of gastric cancer [12,13], rectal cancer [14], thyroid cancer [15,16]. These studies prove that FTIR spectroscopy is feasible to intraoperative diagnosis of cancer lymph node metastasis, moreover, they provide a basis for FTIR spectroscopic study of cancer lymph node metastasis before surgery. Hair can markedly reflect human health status [17], which is easy and non- invasive to collect compared with samples from surgery or endoscopy. Now IR spectra of hair samples have already used in cancer research [18,19]. SR-IR microspectroscopy combines the advantages of synchrotron radiation source, FTIR spectroscopy and IR microscopy [20]. SR-IR microspectroscopy can analyze a micro-region such as hair medulla (~10 μm width) and gain IR spectra with high S-N ratio at diffraction limit spatial resolution [21]. In this work, we compared hair
Abbreviations: FTIR, Fourier transform infrared; IR, infrared; SR, synchrotron radiation; PCA, principle component analysis ⁎ Corresponding author at: School of Basic Medical Sciences, Anhui Medical University, Hefei, Anhui 230032, China. E-mail address: [email protected] (X. Wang). 1 Both contributed equally to this work. https://doi.org/10.1016/j.infrared.2019.103147 Received 16 October 2019; Received in revised form 28 November 2019; Accepted 28 November 2019 Available online 29 November 2019 1350-4495/ © 2019 Elsevier B.V. All rights reserved.
Infrared Physics and Technology 104 (2020) 103147
C. Li, et al.
of Anhui Medical University. These patients did not undergo surgery, chemotherapy or radiotherapy. Hair samples were collected (~10 mm length from scalp) before operation with the exclusion of dyed or grey hair. According to postoperative pathological results, hair samples were divided into lymph node metastasis group (36 cases) and non-lymph node metastasis group (29 cases) (Table 1). After collection, hair samples were wiped with distilled water and ethanol (95%) in turn before vacuum drying. OCT-embedded hair samples were frozen to −25 °C, and then they were cut by a freezing microtome to 5 μm thick sections. The sections were transferred onto 1 mm thick BaF2 slides (IR transparent material) and then stored at room temperature in a vaccum desiccator.
Table 1 General characteristics of gastric cancer patients.
Male Female Age
Non-lymph node metastasis group
Lymph node metastasis group
19 10 61.34 ± 5.25
23 13 62.28 ± 8.16
Note: There was no significant difference in sex and age between non-lymph node metastasis group and lymph node metastasis group
2.2. SR-IR microspectroscopy measurements and data processing Hair samples were measured at BL01B beamline at National Synchrotron Radiation Laboratory (Hefei, China). IR spectra were acquired in transmission mode between 4000 cm−1 and 800 cm−1 by using a FTIR spectrometer (Bruker VERTEX 70v) coupled to an IR microscope (Bruker Hyperion 3000). The parameters were 5 μm × 5 μm aperture, 4 cm−1 spectral resolution and 256 co-added scans. OPUS5.5 software was used to cut (3640–1000 cm−1), baseline correct (64 points rubberband) and min-max normalize all IR spectra. Peak-area ratios were processed by Origin6.0 software. Data were presented as mean ± SD and analyzed by independent t-test. PCA and discriminant analysis were done by using Unscrambler X10.4 software and SPSS16.0 software respectively. 3. Results and discussion Fig. 1. The average IR spectra of hair medulla from lymph node metastasis and non- lymph node metastasis gastric cancer patients.
3.1. Analysis of IR spectra Fig. 1 showed the average IR spectra of hair medulla from nonlymph node metastasis group and lymph node metastasis group. The typical bands for non-lymph node metastasis group included 3285 cm−1 (amide A), 2923 cm−1 and 2884 cm−1 (C-H stretching), 1651 cm−1 (amide I), 1547 cm−1 (amide II), 1455 cm−1 (CH3 bending), 1243 cm−1 (PO2− asymmetric stretching) and 1114 cm−1 (PO-C symmetric stretching) [22–25]. The typical bands for lymph node metastasis group included 3286 cm−1 (amide A), 2924 cm−1 and 2885 cm−1 (C-H stretching), 1650 cm−1 (amide I), 1548 cm−1 (amide II), 1455 cm−1 (CH3 bending), 1243 cm−1 (PO2− asymmetric stretching) and 1115 cm−1 (P-O-C symmetric stretching) [22–25]. No significant difference was existed between the average IR spectra of non-lymph node metastasis group and lymph node metastasis group.
Table 2 Statistic analysis of peak-area ratios.
A3286/A2877 A1455/A2877 A3286/A1244 A2924/A2877 A3286/A1455
Non-lymph node metastasis
Lymph node metastasis
P
50.6929 ± 17.7680 1.3645 ± 0.4263 101.9154 ± 21.6017 2.1341 ± 0.9333 36.9871 ± 5.8967
35.3215 ± 11.4360 1.0379 ± 0.2452 86.5818 ± 14.8921 1.6905 ± 0.7228 33.4990 ± 4.3292
< 0.01 < 0.01 < 0.01 < 0.05 < 0.01
Table 3 Discriminant analysis results. Pathology results
Non-lymph node metastasis Lymph node metastasis
IR spectroscopic results Non-lymph node metastasis
Lymph node metastasis
24 7
5 29
3.2. Analysis of peak-area ratios We measured A3286 (baseline: 3560–3129 cm−1), A2924 (baseline: 2990–2910 cm−1), A2877 (baseline: 2900–2840 cm−1), A1455 (baseline: 1480–1430 cm−1), A1244 (baseline: 1260–1220 cm−1). Then we calculated peak-area ratios (A3286/A2877, A1455/A2877, A3286/ A1244, A2924/A2877 and A3286/A1455) and obtained the results in Table 2 (Note: A represented peak area). A3286/A2877 and A3286/ A1455 described changes in relative content of proteins to lipids, A3286/A1244 described changes in relative content of proteins to nucleic acids, A1455/A2877 and A2924/A2877 showed structural changes of lipids. The average value of A3286/A2877 was 50.6929 for non-lymph node metastasis group while 35.3215 for lymph node metastasis group. The average value of A1455/A2877 was 1.3645 for non-lymph node metastasis group while 1.0379 for lymph node metastasis group. The average value of A3286/A1244 was 101.9154 for non-lymph node metastasis group while 86.5818 for lymph node metastasis group. The average value of A2924/A2877 was 2.1341 for non-lymph node metastasis group while 1.6905 for lymph node metastasis group. The
medulla from lymph node metastasis and non-metastasis gastric cancer patients by using SR-IR microspectroscopy. The aim of this study was to investigate the application of hair medulla IR spectra in preoperative diagnosis of gastric cancer lymph node metastasis.
2. Materials and methods 2.1. Collection and preparation of hair samples This study was approved by Biomedical Ethics Committee of Anhui Medical University (Ref. No.20160222). The patients’ informed consent were obtained while collecting hair samples. Sixty five gastric cancer patients’ hair samples were provided by The Second Affiliated Hospital 2
Infrared Physics and Technology 104 (2020) 103147
C. Li, et al.
Fig. 2. (A) PCA in the 3560–2800 cm−1 region of IR spectra (B) PCA loading plot.
stretching) [30]. For PC2, the positive peak was located at 3327 cm−1 (N-H asymmetric stretching) [31] while the negative peaks were located at 2921 cm−1 (CH2 asymmetric stretching) [32], 2892 cm−1 (C-H stretching) [30] and 2852 cm−1 (CH2 symmetric stretching) [33]. The bands around 2921 cm−1, 2898 cm−1, 2892 cm−1 and 2852 cm−1 were mainly related to lipids while the bands around 3327 cm−1 and 3304 cm−1 were mainly related to proteins. Hence the results suggested that the proteins and lipids content/structure in hair medulla might change largely due to lymph node metastasis of gastric cancer.
average value of A3286/A1455 was 36.9871 for non-lymph node metastasis group while 33.4990 for lymph node metastasis group. Moreover, independent t-test showed that P values were all lower than 0.05, indicating that significant changes took place in lipids, proteins and nucleic acids due to lymph node metastasis [26]. 3.3. Discriminant analysis We carried out discriminant analysis on the basis of A3286/A2877, A3286/A1455, A3286/A1244 and A1455/A2877, from which we gained two following equations:
4. Conclusion
Y1 = −22.7938 ∗ X1 + 25.5225 ∗ X2 + 0.0557 ∗ X3 + 830.9833
Gastric cancer is often accompanied by abdominal lymph node metastasis. Preoperative diagnosis of lymph node metastasis is helpful to make schemes for surgery and postoperative therapy scientifically. This study explored the feasibility of using IR spectra of hair medulla in diagnosing lymph node metastasis of gastric cancer before operation. Peak-area ratios such as A3286/A2877, A1455/A2877, A3286/A1244, A2924/A2877 and A3286/A1455 were significantly different between non-lymph node metastasis group and lymph node metastasis group, indicating obvious changes of proteins, lipids and nucleic acids due to lymph node metastasis. Based on these ratios, we obtained 80.56% sensitivity, 82.76% specificity, 85.29% positive predictive value and 81.54% accuracy through discriminant analysis. We also carried out PCA, but its effect was not as good as discriminant analysis. Our study showed that lymph node metastasis caused biomacromolecular changes in gastric cancer patients’ hair medulla, moreover, indicating that IR spectra of hair medulla may be potentially used for diagnosing lymph node metastasis before operation.
∗ X 4 − 464.7175 Y2 = −22.7222 ∗ X1 + 25.3503 ∗ X2 + 0.0343 ∗ X3 + 826.6156 ∗ X 4 − 454.4713 Y1 signified non-lymph node metastasis while Y2 signified lymph node metastasis. X1, X2, X3 and X4 indicated A3286/A2877, A3286/ A1455, A3286/A1244 and A1455/A2877 separately. Four variable values were used as input data to calculate. The case would be categorized as non-lymph node metastasis group if Y1 > Y2 while it would be categorized as lymph node metastasis group if Y1 < Y2 [27]. Table 3 showed the comparison between SR-IR microspectroscopic diagnosis and histopathologic diagnosis (known as “the gold standard”). Then we calculated the sensitivity, the specificity, the positive predictive value and the accuracy as below [27].
The sensitivity = 29/36 ∗ 100% = 80.56% The specificity = 24/29 ∗ 100% = 82.76%
Declaration of Competing Interest
The positive predictive value = 29/(29 + 5) ∗ 100% = 85.29% The authors declared that there is no conflict of interest.
The accuracy = (24 + 29)/65 ∗ 100% = 81.54% Acknowledgements 3.4. Principle component analysis (PCA)
The authors are grateful for the supports from National Natural Science Foundation of China (Grant No. U1632148) and BL01B beamline of National Synchrotron Radiation Laboratory. The authors also thanks Dr. Zeming Qi and Dr. Chuansheng Hu for their kind help in SRIR microspectroscopy measurements.
PCA can transform multivariable into fewer variables for analysis without information loss [28]. PCA was employed to analyze IR spectra in the regions of 3560–2800 cm−1, 1700–1000 cm−1, 3560–1000 cm−1 and so on. It was not ideal for PCA method in differentiating lymph node metastasis from non-metastasis. Among these, PCA in the 3560–2800 cm−1 region achieved the best result. Fig. 2A showed the score plots of PC1 (69% of total variance) versus PC2 (19% of total variance). Fig. 2B showed PCA loading plot. For PC1, the peaks were located at 3304 cm−1 (amide A) [29] and 2898 cm−1 (CH3 symmetric
References [1] F. Bray, J. Ferly, I. Soerjomataram, R.L. Siegel, L.A. Torre, A. Jemal, Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36
3
Infrared Physics and Technology 104 (2020) 103147
C. Li, et al.
2917–2921. [17] K. Jan, K. Jan, H. Vladimír, Hair elemental analysis for forensic science using nuclear and related analytical methods, Foren. Chem. 7 (2018) 65–74. [18] D.J. Lyman, J.M. Wijelath, Fourier transform infrared attenuated total reflection analysis of human hair: comparison of hair from breast cancer patients with hair from healthy subjects, Appl. Spectrosc. 59 (2005) 26–32. [19] X. Wang, Z.M. Qi, X.C. Liu, S.Y. Wang, C.X. Li, G. Liu, Y. Xiong, T.T. Li, J.Q. Tao, Y.C. Tian, The comparison of hair from gastric cancer patients and from healthy persons studied by infrared microspectroscopy and imaging using synchrotron radiation, Cancer Epidemiol. 34 (2010) 453–456. [20] S.J. Ling, Y.F. Huang, L. Huang, Z.Z. Shao, X. Chen, Application of synchrotron FTIR microspectroscopy and mapping in analytical chemistry, Prog. Chem. 25 (2013) 821–831. [21] P. Yu, Synchrotron IR microspectroscopy for protein structure analysis: potential and questions, Spectroscopy 20 (2006) 229–251. [22] L.D. Giambattista, P. Grimaldi, S. Gaudenzi, D. Pozzi, M. Grandi, S. Morrone, I. Silvestri, A.C. Castellano, UVB radiation induced effects on cells studied by FTIR spectroscopy, Eur. Biophys. J. 39 (2010) 929–934. [23] N. Elmadany, E. Khalil, L. Vaccari, G. Birarda, I. Yousef, R. Abu-Dahab, Antiproliferative activity of the combination of doxorubicin/quercetin on MCF7 breast cancer cell line: a combined study using colorimetric assay and synchrotron infrared microspectroscopy, Infrared Phys. Tech. 95 (2018) 141–147. [24] C. Aksoy, F. Severcan, Infrared spectroscopy and imaging in stem cells and aging research, Methods in Molecular Biology, Humana Press, 2018, , https://doi.org/10. 1007/7651_2018_119. [25] H. Liu, Q.L. Su, Q.P. Wu, W.J. Fang, D.C. Yang, W. Zheng, X. Wang, FTIR spectroscopic study on apoptosis of lung cancer cell line A549 induced by arsenic trioxide, Infrared Phys. Tech. 93 (2018) 340–345. [26] K. Yano, S. Ohoshima, Y. Gotou, K. Kumaido, T. Moriguchi, H. Katayama, Direct measurement of human lung cancerous and noncancerous tissues by Fourier transform infrared microscopy: can an infrared microscope be used as a clinical tool? Anal. Biochem. 287 (2000) 218–225. [27] X.Q. Zhang, Y.Z. Xu, Y.F. Zhang, L.X. Wang, C.S. Hou, X.S. Zhou, X.F. Ling, Z. Xu, Intraoperative detection of thyroid carcinoma by Fourier transform infrared spectrometry, J. Surg. Res. 171 (2011) 650–656. [28] J. Liu, Q. Huang, Screening of astaxanthin-hyperproducing haematococcus pluvialis using Fourier transform infrared (FT-IR) and Raman microspectroscopy, Appl. Spectrosc. 70 (2016) 1639–1648. [29] X. Wang, Z.M. Qi, S.Y. Wang, G. Liu, H.L. Gao, Y.C. Tian, The study of a single BGC823 cell using Fourier transform infrared microspectroscopic imaging, Spectrochim. Acta A 79 (2011) 1660–1662. [30] A.C.S. Talari, M.A.G. Martinez, Z. Movasaghi, S. Rehman, I.U. Rehman, Advances in Fourier transform infrared (FTIR) spectroscopy of biological tissues, Appl. Spectrosc. Rev. 52 (2017) 456–506. [31] L.P. Choo, J.R. Mansfield, N. Pizzi, R.L. Somorjai, M. Jackson, W.C. Halliday, H.H. Mantsch, Infrared spectra of human central nervous system tissue: diagnosis of Alzheimer’s disease by multivariate analyses, Biospectroscopy 1 (1995) 141–148. [32] L.B. Mostaco-Guidolin, L.S. Murakami, M.R. Batistuti, A. Nomizo, L. Bachmann, Molecular and chemical characterization by Fourier transform infrared spectroscopy of human breast cancer cells with estrogen receptor expressed and not expressed, Spectroscopy 24 (2010) 501–510. [33] H. Fabian, M. Jackson, L. Murphy, P.H. Watson, I. Fichtner, H.H. Mantsch, A comparative infrared spectroscopic study of human breast tumors and breast tumor cell xenografts, Biospectroscopy 1 (1995) 37–45.
cancers in 185 countries, CA-Cancer J. Clin. 68 (2018) 394–424. [2] M. Venerito, A. Link, T. Rokkas, P. Malfertheiner, Gastric cancer-clinical and epidemiological aspects, Helicobacter 21 (2016) 39–44. [3] S.H. Noh, S.R. Park, H.K. Yang, Adjuvant capecitabine plus oxaliplatin for gastric cancer after D2 gastrectomy (classic): 5-year follow-up of an open-label, randomized phase 3 trial, Lancet Oncol. 15 (2014) 1389–1396. [4] S. Ito, T. Sano, J. Mizusawa, D. Takahari, H. Katayama, H. Katai, Y. Kawashima, T. Kinoshita, M. Terashima, A. Nashimoto, M. Nakamori, H. Onaya, M. Sasako, A phase II study of preoperative chemotherapy with docetaxel, cisplatin, and S-1 followed by gastrectomy with D2 plus para-aortic lymph node dissection for gastric cancer with extensive lymph node metastasis: JCOG1002, Gastric Cancer 20 (2017) 322–331. [5] T. Heye, C. Kuntz, M. Düx, J. Encke, M. Palmowski, F. Autschbach, F. Volke, G.W. Kauffmann, L. Grenacher, CT and endoscopic ultrasound in comparison to endoluminal MRI: preliminary results in staging gastric carcinoma, Eur. J. Radiol. 70 (2009) 336–341. [6] E. Redondo-cerezo, J.G. Martínezcara, R. Jiménezrosales, F. Valverdelópez, A. Caballero-mateos, P. Jérvez-puente, J.L. Ariza-Fernández, M. Úbeda-Muñoz, M. López-de-Hierro, J. de Teresa, Endoscopic ultrasound in gastric cancer staging before and after neoadjuvant chemotherapy. a comparison with PET-CT in a clinical series, United Euro. Gastroent. 5 (2017) 641–647. [7] A.A. Bunaciu, V.D. Hong, H.Y. Aboul-Enein, Applications of FTIR spectrophoto-metry in cancer diagnostics, Crit. Rev. Anal. Chem. 45 (2015) 156–165. [8] A.A. Bunaciu, H.Y. Aboul-Enein, S. Fleschin, Vibrational spectroscopy in clinical analysis, Appl. Spectrosc. Rev. 50 (2015) 176–191. [9] Adeeba, A.J. Siddiqui, S.T.Z. Sherazi, S. Ahmed, M.I. Choudhary, S.G. Atta-UrRahmam, Musharraf, A comparative profiling of oral cancer patients and high risk niswar users using FTIR and chemometric analysis, Spectrochim. Acta A 203 (2018) 177–184. [10] A. Derenne, O. Vandersleyen, E. Goormaghtigh, Lipid quantification method using FTIR spectroscopy applied on cancer cell extracts, Biochem. Biophys. Acta 2014 (1841) 1200–1209. [11] M.J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H.J. Butler, K.M. Dorling, P.R. Fielden, S.W. Fogarty, N.J. Fullwood, K.A. Heys, C. Hughes, P. Lasch, P.L. Martin-Hirsch, B. Obinaju, G.D. Sockalingum, J. Sulé-Suso, R.J. Strong, M.J. Walsh, B.R. Wood, P. Gardiner, F.L. Martin, Using Fourier transform IR spectroscopy to analyze biological materials, Nat. Protoc. 9 (2014) 1771–1791. [12] Y.K. Bai, L.W. Yu, L. Zhang, J. Fu, H. Leng, X.J. Yang, J.Q. Ma, X.J. Li, X.J. Li, Q. Zhu, Y.F. Zhang, X.F. Ling, W.L. Cao, Research on application of Fourier transform infrared spectrometry in the diagnosis of lymph node metastasis in gastric cancer, Spectrosc. Spect. Anal. 35 (2015) 599–602. [13] Q.H. Yin, H.K. Li, B. Ye, X.Q. Zhou, C.S. Li, X.F. Zeng, X.T. Zeng, Fourier transform infrared spectroscopy study of lymph node metastasis in early gastric cancer, J. Gannan Med. Univ. 36 (2016) 367–369. [14] D. Liu, B. Song, X.J. Sun, Y.Z. Xu, Y.B. Long, J.K. Du, J.B. Zheng, B. Liu, Research of Fourier transform infrared (FTIR) spectroscopy with multivariate analysis as novel diagnostic tool for metastatic rectal cancer lymph nodes, Spectrosc. Spect. Anal. 37 (2017) 2028–2031. [15] Y. Liu, Y. Xu, Y. Liu, Y. Zhang, D. Wang, D. Xiu, Z. Xu, X. Zhou, J. Wu, X. Ling, Detection of cervical metastatic lymph nodes in papillary thyroid carcinoma by Fourier transform infrared spectroscopy, Brit. J. Surg. 98 (2011) 380–384. [16] Y.Q. Liu, M.J. Gao, Y.Z. Xu, Q.G. Sun, X.Q. Zhang, Z. Xu, Y.F. Zhang, J.G. Wu, X.S. Zhou, X.F. Ling, Investigation on Fourier transform infrared spectrum of metastatic lymph node in thyroid cancer, Spectrosc. Spect. Anal. 29 (2009)
4
Contents lists available at ScienceDirect
Infrared Physics & Technology journal homepage: www.elsevier.com/locate/infrared
Comparison of hair medulla from lymph node metastasis and non-lymph node metastasis gastric cancer patients using synchrotron radiation infrared microspectroscopy Chao Lia,b,c,1, Qipeng Wua,1, Ling Zonga, Yuan Zhoua, Xin Wanga,
T
⁎
a
School of Basic Medical Sciences, Anhui Medical University, Hefei, Anhui 230032, China The Second Affiliated Hospital, Anhui Medical University, Hefei, Anhui 230601, China c National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui 230029, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Gastric cancer Lymph node metastasis SR-IR microspectroscopy Hair medulla Discriminant analysis
Lymph node metastasis often occurs in gastric cancer, hence it is important to explore new methods for diagnosing lymph node metastasis before surgery. In this study, synchrotron radiation infrared (SR-IR) microspectroscopy was used to compare hair medulla from lymph node metastasis and non-metastasis gastric cancer patients. Peak-area ratios indicated that significant alterations might occur in lipids, proteins and nucleic acids after lymph node metastasis. Discriminant analysis was implemented on the basis of peak-area ratios and obtained the following results: the sensitivity was 80.56%, the specificity was 82.76%, the positive predictive value was 85.29% and the accuracy was 81.54%. This study showed that biomacro-molecules in hair medulla were influenced by lymph node metastasis, indicating that IR spectra of hair medulla may be potentially used to diagnose lymph node metastasis before gastric cancer surgery.
1. Introduction In recent years, the incidence and mortality of gastric cancer remain very high worldwide due to environmental factors and Helicobacter pylori infection, etc. [1]. Although the techniques of surgery, radiotherapy and chemotherapy have made remarkable progress, five years survival rate of gastric cancer patients is still less than 30% [2]. Incomplete lymph node dissection is an important reason for low survival rate, which will cause metastasis after surgery as well as influencing the efficacy of radiotherapy and chemotherapy [3,4]. Abdominal lymph node metastasis is the typical biological characteristics of gastric cancer. According to the status of lymph node metastasis, surgeons will select reasonable surgical method and provide scientific guidance for postoperative treatment. At present, imaging examination methods are commonly used for diagnosing lymph node metastasis before surgery, however, they have limitations as below: CT (computed tomography), EUS (endoscopic ultrasonography) and MRI (magnetic resonance imaging) lack the uniform diagnostic criteria, which may lead to higher misdiagnosis rate [5]. PET/CT (positron emission tomography/CT) is expensive, which limits its clinical
application [6]. Pathological examination is “the gold standard” for diagnosing lymph node metastasis during surgery, but it wastes time and increases patients’ risk. FTIR spectroscopy can reflect vibrational characteristics of biomacro molecules such as nucleic acids, proteins and lipids, etc., hence it has been applied to investigate cancers and other diseases [7–11]. In recent years, FTIR spectroscopy has been used to investigate lymph node metastasis of gastric cancer [12,13], rectal cancer [14], thyroid cancer [15,16]. These studies prove that FTIR spectroscopy is feasible to intraoperative diagnosis of cancer lymph node metastasis, moreover, they provide a basis for FTIR spectroscopic study of cancer lymph node metastasis before surgery. Hair can markedly reflect human health status [17], which is easy and non- invasive to collect compared with samples from surgery or endoscopy. Now IR spectra of hair samples have already used in cancer research [18,19]. SR-IR microspectroscopy combines the advantages of synchrotron radiation source, FTIR spectroscopy and IR microscopy [20]. SR-IR microspectroscopy can analyze a micro-region such as hair medulla (~10 μm width) and gain IR spectra with high S-N ratio at diffraction limit spatial resolution [21]. In this work, we compared hair
Abbreviations: FTIR, Fourier transform infrared; IR, infrared; SR, synchrotron radiation; PCA, principle component analysis ⁎ Corresponding author at: School of Basic Medical Sciences, Anhui Medical University, Hefei, Anhui 230032, China. E-mail address: [email protected] (X. Wang). 1 Both contributed equally to this work. https://doi.org/10.1016/j.infrared.2019.103147 Received 16 October 2019; Received in revised form 28 November 2019; Accepted 28 November 2019 Available online 29 November 2019 1350-4495/ © 2019 Elsevier B.V. All rights reserved.
Infrared Physics and Technology 104 (2020) 103147
C. Li, et al.
of Anhui Medical University. These patients did not undergo surgery, chemotherapy or radiotherapy. Hair samples were collected (~10 mm length from scalp) before operation with the exclusion of dyed or grey hair. According to postoperative pathological results, hair samples were divided into lymph node metastasis group (36 cases) and non-lymph node metastasis group (29 cases) (Table 1). After collection, hair samples were wiped with distilled water and ethanol (95%) in turn before vacuum drying. OCT-embedded hair samples were frozen to −25 °C, and then they were cut by a freezing microtome to 5 μm thick sections. The sections were transferred onto 1 mm thick BaF2 slides (IR transparent material) and then stored at room temperature in a vaccum desiccator.
Table 1 General characteristics of gastric cancer patients.
Male Female Age
Non-lymph node metastasis group
Lymph node metastasis group
19 10 61.34 ± 5.25
23 13 62.28 ± 8.16
Note: There was no significant difference in sex and age between non-lymph node metastasis group and lymph node metastasis group
2.2. SR-IR microspectroscopy measurements and data processing Hair samples were measured at BL01B beamline at National Synchrotron Radiation Laboratory (Hefei, China). IR spectra were acquired in transmission mode between 4000 cm−1 and 800 cm−1 by using a FTIR spectrometer (Bruker VERTEX 70v) coupled to an IR microscope (Bruker Hyperion 3000). The parameters were 5 μm × 5 μm aperture, 4 cm−1 spectral resolution and 256 co-added scans. OPUS5.5 software was used to cut (3640–1000 cm−1), baseline correct (64 points rubberband) and min-max normalize all IR spectra. Peak-area ratios were processed by Origin6.0 software. Data were presented as mean ± SD and analyzed by independent t-test. PCA and discriminant analysis were done by using Unscrambler X10.4 software and SPSS16.0 software respectively. 3. Results and discussion Fig. 1. The average IR spectra of hair medulla from lymph node metastasis and non- lymph node metastasis gastric cancer patients.
3.1. Analysis of IR spectra Fig. 1 showed the average IR spectra of hair medulla from nonlymph node metastasis group and lymph node metastasis group. The typical bands for non-lymph node metastasis group included 3285 cm−1 (amide A), 2923 cm−1 and 2884 cm−1 (C-H stretching), 1651 cm−1 (amide I), 1547 cm−1 (amide II), 1455 cm−1 (CH3 bending), 1243 cm−1 (PO2− asymmetric stretching) and 1114 cm−1 (PO-C symmetric stretching) [22–25]. The typical bands for lymph node metastasis group included 3286 cm−1 (amide A), 2924 cm−1 and 2885 cm−1 (C-H stretching), 1650 cm−1 (amide I), 1548 cm−1 (amide II), 1455 cm−1 (CH3 bending), 1243 cm−1 (PO2− asymmetric stretching) and 1115 cm−1 (P-O-C symmetric stretching) [22–25]. No significant difference was existed between the average IR spectra of non-lymph node metastasis group and lymph node metastasis group.
Table 2 Statistic analysis of peak-area ratios.
A3286/A2877 A1455/A2877 A3286/A1244 A2924/A2877 A3286/A1455
Non-lymph node metastasis
Lymph node metastasis
P
50.6929 ± 17.7680 1.3645 ± 0.4263 101.9154 ± 21.6017 2.1341 ± 0.9333 36.9871 ± 5.8967
35.3215 ± 11.4360 1.0379 ± 0.2452 86.5818 ± 14.8921 1.6905 ± 0.7228 33.4990 ± 4.3292
< 0.01 < 0.01 < 0.01 < 0.05 < 0.01
Table 3 Discriminant analysis results. Pathology results
Non-lymph node metastasis Lymph node metastasis
IR spectroscopic results Non-lymph node metastasis
Lymph node metastasis
24 7
5 29
3.2. Analysis of peak-area ratios We measured A3286 (baseline: 3560–3129 cm−1), A2924 (baseline: 2990–2910 cm−1), A2877 (baseline: 2900–2840 cm−1), A1455 (baseline: 1480–1430 cm−1), A1244 (baseline: 1260–1220 cm−1). Then we calculated peak-area ratios (A3286/A2877, A1455/A2877, A3286/ A1244, A2924/A2877 and A3286/A1455) and obtained the results in Table 2 (Note: A represented peak area). A3286/A2877 and A3286/ A1455 described changes in relative content of proteins to lipids, A3286/A1244 described changes in relative content of proteins to nucleic acids, A1455/A2877 and A2924/A2877 showed structural changes of lipids. The average value of A3286/A2877 was 50.6929 for non-lymph node metastasis group while 35.3215 for lymph node metastasis group. The average value of A1455/A2877 was 1.3645 for non-lymph node metastasis group while 1.0379 for lymph node metastasis group. The average value of A3286/A1244 was 101.9154 for non-lymph node metastasis group while 86.5818 for lymph node metastasis group. The average value of A2924/A2877 was 2.1341 for non-lymph node metastasis group while 1.6905 for lymph node metastasis group. The
medulla from lymph node metastasis and non-metastasis gastric cancer patients by using SR-IR microspectroscopy. The aim of this study was to investigate the application of hair medulla IR spectra in preoperative diagnosis of gastric cancer lymph node metastasis.
2. Materials and methods 2.1. Collection and preparation of hair samples This study was approved by Biomedical Ethics Committee of Anhui Medical University (Ref. No.20160222). The patients’ informed consent were obtained while collecting hair samples. Sixty five gastric cancer patients’ hair samples were provided by The Second Affiliated Hospital 2
Infrared Physics and Technology 104 (2020) 103147
C. Li, et al.
Fig. 2. (A) PCA in the 3560–2800 cm−1 region of IR spectra (B) PCA loading plot.
stretching) [30]. For PC2, the positive peak was located at 3327 cm−1 (N-H asymmetric stretching) [31] while the negative peaks were located at 2921 cm−1 (CH2 asymmetric stretching) [32], 2892 cm−1 (C-H stretching) [30] and 2852 cm−1 (CH2 symmetric stretching) [33]. The bands around 2921 cm−1, 2898 cm−1, 2892 cm−1 and 2852 cm−1 were mainly related to lipids while the bands around 3327 cm−1 and 3304 cm−1 were mainly related to proteins. Hence the results suggested that the proteins and lipids content/structure in hair medulla might change largely due to lymph node metastasis of gastric cancer.
average value of A3286/A1455 was 36.9871 for non-lymph node metastasis group while 33.4990 for lymph node metastasis group. Moreover, independent t-test showed that P values were all lower than 0.05, indicating that significant changes took place in lipids, proteins and nucleic acids due to lymph node metastasis [26]. 3.3. Discriminant analysis We carried out discriminant analysis on the basis of A3286/A2877, A3286/A1455, A3286/A1244 and A1455/A2877, from which we gained two following equations:
4. Conclusion
Y1 = −22.7938 ∗ X1 + 25.5225 ∗ X2 + 0.0557 ∗ X3 + 830.9833
Gastric cancer is often accompanied by abdominal lymph node metastasis. Preoperative diagnosis of lymph node metastasis is helpful to make schemes for surgery and postoperative therapy scientifically. This study explored the feasibility of using IR spectra of hair medulla in diagnosing lymph node metastasis of gastric cancer before operation. Peak-area ratios such as A3286/A2877, A1455/A2877, A3286/A1244, A2924/A2877 and A3286/A1455 were significantly different between non-lymph node metastasis group and lymph node metastasis group, indicating obvious changes of proteins, lipids and nucleic acids due to lymph node metastasis. Based on these ratios, we obtained 80.56% sensitivity, 82.76% specificity, 85.29% positive predictive value and 81.54% accuracy through discriminant analysis. We also carried out PCA, but its effect was not as good as discriminant analysis. Our study showed that lymph node metastasis caused biomacromolecular changes in gastric cancer patients’ hair medulla, moreover, indicating that IR spectra of hair medulla may be potentially used for diagnosing lymph node metastasis before operation.
∗ X 4 − 464.7175 Y2 = −22.7222 ∗ X1 + 25.3503 ∗ X2 + 0.0343 ∗ X3 + 826.6156 ∗ X 4 − 454.4713 Y1 signified non-lymph node metastasis while Y2 signified lymph node metastasis. X1, X2, X3 and X4 indicated A3286/A2877, A3286/ A1455, A3286/A1244 and A1455/A2877 separately. Four variable values were used as input data to calculate. The case would be categorized as non-lymph node metastasis group if Y1 > Y2 while it would be categorized as lymph node metastasis group if Y1 < Y2 [27]. Table 3 showed the comparison between SR-IR microspectroscopic diagnosis and histopathologic diagnosis (known as “the gold standard”). Then we calculated the sensitivity, the specificity, the positive predictive value and the accuracy as below [27].
The sensitivity = 29/36 ∗ 100% = 80.56% The specificity = 24/29 ∗ 100% = 82.76%
Declaration of Competing Interest
The positive predictive value = 29/(29 + 5) ∗ 100% = 85.29% The authors declared that there is no conflict of interest.
The accuracy = (24 + 29)/65 ∗ 100% = 81.54% Acknowledgements 3.4. Principle component analysis (PCA)
The authors are grateful for the supports from National Natural Science Foundation of China (Grant No. U1632148) and BL01B beamline of National Synchrotron Radiation Laboratory. The authors also thanks Dr. Zeming Qi and Dr. Chuansheng Hu for their kind help in SRIR microspectroscopy measurements.
PCA can transform multivariable into fewer variables for analysis without information loss [28]. PCA was employed to analyze IR spectra in the regions of 3560–2800 cm−1, 1700–1000 cm−1, 3560–1000 cm−1 and so on. It was not ideal for PCA method in differentiating lymph node metastasis from non-metastasis. Among these, PCA in the 3560–2800 cm−1 region achieved the best result. Fig. 2A showed the score plots of PC1 (69% of total variance) versus PC2 (19% of total variance). Fig. 2B showed PCA loading plot. For PC1, the peaks were located at 3304 cm−1 (amide A) [29] and 2898 cm−1 (CH3 symmetric
References [1] F. Bray, J. Ferly, I. Soerjomataram, R.L. Siegel, L.A. Torre, A. Jemal, Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36
3
Infrared Physics and Technology 104 (2020) 103147
C. Li, et al.
2917–2921. [17] K. Jan, K. Jan, H. Vladimír, Hair elemental analysis for forensic science using nuclear and related analytical methods, Foren. Chem. 7 (2018) 65–74. [18] D.J. Lyman, J.M. Wijelath, Fourier transform infrared attenuated total reflection analysis of human hair: comparison of hair from breast cancer patients with hair from healthy subjects, Appl. Spectrosc. 59 (2005) 26–32. [19] X. Wang, Z.M. Qi, X.C. Liu, S.Y. Wang, C.X. Li, G. Liu, Y. Xiong, T.T. Li, J.Q. Tao, Y.C. Tian, The comparison of hair from gastric cancer patients and from healthy persons studied by infrared microspectroscopy and imaging using synchrotron radiation, Cancer Epidemiol. 34 (2010) 453–456. [20] S.J. Ling, Y.F. Huang, L. Huang, Z.Z. Shao, X. Chen, Application of synchrotron FTIR microspectroscopy and mapping in analytical chemistry, Prog. Chem. 25 (2013) 821–831. [21] P. Yu, Synchrotron IR microspectroscopy for protein structure analysis: potential and questions, Spectroscopy 20 (2006) 229–251. [22] L.D. Giambattista, P. Grimaldi, S. Gaudenzi, D. Pozzi, M. Grandi, S. Morrone, I. Silvestri, A.C. Castellano, UVB radiation induced effects on cells studied by FTIR spectroscopy, Eur. Biophys. J. 39 (2010) 929–934. [23] N. Elmadany, E. Khalil, L. Vaccari, G. Birarda, I. Yousef, R. Abu-Dahab, Antiproliferative activity of the combination of doxorubicin/quercetin on MCF7 breast cancer cell line: a combined study using colorimetric assay and synchrotron infrared microspectroscopy, Infrared Phys. Tech. 95 (2018) 141–147. [24] C. Aksoy, F. Severcan, Infrared spectroscopy and imaging in stem cells and aging research, Methods in Molecular Biology, Humana Press, 2018, , https://doi.org/10. 1007/7651_2018_119. [25] H. Liu, Q.L. Su, Q.P. Wu, W.J. Fang, D.C. Yang, W. Zheng, X. Wang, FTIR spectroscopic study on apoptosis of lung cancer cell line A549 induced by arsenic trioxide, Infrared Phys. Tech. 93 (2018) 340–345. [26] K. Yano, S. Ohoshima, Y. Gotou, K. Kumaido, T. Moriguchi, H. Katayama, Direct measurement of human lung cancerous and noncancerous tissues by Fourier transform infrared microscopy: can an infrared microscope be used as a clinical tool? Anal. Biochem. 287 (2000) 218–225. [27] X.Q. Zhang, Y.Z. Xu, Y.F. Zhang, L.X. Wang, C.S. Hou, X.S. Zhou, X.F. Ling, Z. Xu, Intraoperative detection of thyroid carcinoma by Fourier transform infrared spectrometry, J. Surg. Res. 171 (2011) 650–656. [28] J. Liu, Q. Huang, Screening of astaxanthin-hyperproducing haematococcus pluvialis using Fourier transform infrared (FT-IR) and Raman microspectroscopy, Appl. Spectrosc. 70 (2016) 1639–1648. [29] X. Wang, Z.M. Qi, S.Y. Wang, G. Liu, H.L. Gao, Y.C. Tian, The study of a single BGC823 cell using Fourier transform infrared microspectroscopic imaging, Spectrochim. Acta A 79 (2011) 1660–1662. [30] A.C.S. Talari, M.A.G. Martinez, Z. Movasaghi, S. Rehman, I.U. Rehman, Advances in Fourier transform infrared (FTIR) spectroscopy of biological tissues, Appl. Spectrosc. Rev. 52 (2017) 456–506. [31] L.P. Choo, J.R. Mansfield, N. Pizzi, R.L. Somorjai, M. Jackson, W.C. Halliday, H.H. Mantsch, Infrared spectra of human central nervous system tissue: diagnosis of Alzheimer’s disease by multivariate analyses, Biospectroscopy 1 (1995) 141–148. [32] L.B. Mostaco-Guidolin, L.S. Murakami, M.R. Batistuti, A. Nomizo, L. Bachmann, Molecular and chemical characterization by Fourier transform infrared spectroscopy of human breast cancer cells with estrogen receptor expressed and not expressed, Spectroscopy 24 (2010) 501–510. [33] H. Fabian, M. Jackson, L. Murphy, P.H. Watson, I. Fichtner, H.H. Mantsch, A comparative infrared spectroscopic study of human breast tumors and breast tumor cell xenografts, Biospectroscopy 1 (1995) 37–45.
cancers in 185 countries, CA-Cancer J. Clin. 68 (2018) 394–424. [2] M. Venerito, A. Link, T. Rokkas, P. Malfertheiner, Gastric cancer-clinical and epidemiological aspects, Helicobacter 21 (2016) 39–44. [3] S.H. Noh, S.R. Park, H.K. Yang, Adjuvant capecitabine plus oxaliplatin for gastric cancer after D2 gastrectomy (classic): 5-year follow-up of an open-label, randomized phase 3 trial, Lancet Oncol. 15 (2014) 1389–1396. [4] S. Ito, T. Sano, J. Mizusawa, D. Takahari, H. Katayama, H. Katai, Y. Kawashima, T. Kinoshita, M. Terashima, A. Nashimoto, M. Nakamori, H. Onaya, M. Sasako, A phase II study of preoperative chemotherapy with docetaxel, cisplatin, and S-1 followed by gastrectomy with D2 plus para-aortic lymph node dissection for gastric cancer with extensive lymph node metastasis: JCOG1002, Gastric Cancer 20 (2017) 322–331. [5] T. Heye, C. Kuntz, M. Düx, J. Encke, M. Palmowski, F. Autschbach, F. Volke, G.W. Kauffmann, L. Grenacher, CT and endoscopic ultrasound in comparison to endoluminal MRI: preliminary results in staging gastric carcinoma, Eur. J. Radiol. 70 (2009) 336–341. [6] E. Redondo-cerezo, J.G. Martínezcara, R. Jiménezrosales, F. Valverdelópez, A. Caballero-mateos, P. Jérvez-puente, J.L. Ariza-Fernández, M. Úbeda-Muñoz, M. López-de-Hierro, J. de Teresa, Endoscopic ultrasound in gastric cancer staging before and after neoadjuvant chemotherapy. a comparison with PET-CT in a clinical series, United Euro. Gastroent. 5 (2017) 641–647. [7] A.A. Bunaciu, V.D. Hong, H.Y. Aboul-Enein, Applications of FTIR spectrophoto-metry in cancer diagnostics, Crit. Rev. Anal. Chem. 45 (2015) 156–165. [8] A.A. Bunaciu, H.Y. Aboul-Enein, S. Fleschin, Vibrational spectroscopy in clinical analysis, Appl. Spectrosc. Rev. 50 (2015) 176–191. [9] Adeeba, A.J. Siddiqui, S.T.Z. Sherazi, S. Ahmed, M.I. Choudhary, S.G. Atta-UrRahmam, Musharraf, A comparative profiling of oral cancer patients and high risk niswar users using FTIR and chemometric analysis, Spectrochim. Acta A 203 (2018) 177–184. [10] A. Derenne, O. Vandersleyen, E. Goormaghtigh, Lipid quantification method using FTIR spectroscopy applied on cancer cell extracts, Biochem. Biophys. Acta 2014 (1841) 1200–1209. [11] M.J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H.J. Butler, K.M. Dorling, P.R. Fielden, S.W. Fogarty, N.J. Fullwood, K.A. Heys, C. Hughes, P. Lasch, P.L. Martin-Hirsch, B. Obinaju, G.D. Sockalingum, J. Sulé-Suso, R.J. Strong, M.J. Walsh, B.R. Wood, P. Gardiner, F.L. Martin, Using Fourier transform IR spectroscopy to analyze biological materials, Nat. Protoc. 9 (2014) 1771–1791. [12] Y.K. Bai, L.W. Yu, L. Zhang, J. Fu, H. Leng, X.J. Yang, J.Q. Ma, X.J. Li, X.J. Li, Q. Zhu, Y.F. Zhang, X.F. Ling, W.L. Cao, Research on application of Fourier transform infrared spectrometry in the diagnosis of lymph node metastasis in gastric cancer, Spectrosc. Spect. Anal. 35 (2015) 599–602. [13] Q.H. Yin, H.K. Li, B. Ye, X.Q. Zhou, C.S. Li, X.F. Zeng, X.T. Zeng, Fourier transform infrared spectroscopy study of lymph node metastasis in early gastric cancer, J. Gannan Med. Univ. 36 (2016) 367–369. [14] D. Liu, B. Song, X.J. Sun, Y.Z. Xu, Y.B. Long, J.K. Du, J.B. Zheng, B. Liu, Research of Fourier transform infrared (FTIR) spectroscopy with multivariate analysis as novel diagnostic tool for metastatic rectal cancer lymph nodes, Spectrosc. Spect. Anal. 37 (2017) 2028–2031. [15] Y. Liu, Y. Xu, Y. Liu, Y. Zhang, D. Wang, D. Xiu, Z. Xu, X. Zhou, J. Wu, X. Ling, Detection of cervical metastatic lymph nodes in papillary thyroid carcinoma by Fourier transform infrared spectroscopy, Brit. J. Surg. 98 (2011) 380–384. [16] Y.Q. Liu, M.J. Gao, Y.Z. Xu, Q.G. Sun, X.Q. Zhang, Z. Xu, Y.F. Zhang, J.G. Wu, X.S. Zhou, X.F. Ling, Investigation on Fourier transform infrared spectrum of metastatic lymph node in thyroid cancer, Spectrosc. Spect. Anal. 29 (2009)
4