基于多变量光谱数据分析方法的乳腺癌血清拉曼光谱特征研究

doi:10.3964/j.issn.1000-0593(2023)02-0426-09

Abstract
References
Related Citation (15)

Download: PDF (8410 KB)
Export: BibTeX | EndNote (RIS)

Abstract Compared to cell and sliced tissue samples, blood samples could be collected easier, and its biomedical constitution would show some relavant variations before clinical pathological symptoms. Raman spectroscopy provides molecular-related information about biomedical contents for clinical investigations in a rapid, nonlabeled, nondestructive and noninvasive way, presenting a significant application prospect for blood sample-based diagnosis. In this study, we present a reliable method for detecting breast cancer using blood serum combined with multivariate analysis methods. The blood serum samples were divided into healthy, early, and advanced cancer groups based on clinical pathological diagnosis. Using a quatz capillary tubes as sample holder, the spectral information was acquired to illustrating the biomedical constitution nature of the serum sample. The spectral classification models, which were built on the method of principal component analysis (PCA), linear discriminant analysis (LDA), supporting vector machines (SVM) and partial least squares discriminant analysis (PLS-DA), were utilized for unveiling the spectral variances among different investigated groups. And the leave-one-out cross-validation (LOOCV) method was adopted for evaluating the model classification performance. After that, we not only observed the resonance Raman spectral phenomena of carotenoid contents in serum but also identified the spectral variations of protein and lipid contents during breast cancer progression. By using the multivariate analysis methods, the representative spectral identities were recognized. Since then, the spectral classification accuracy of PCA-LDA model was found to be 99%. For three types kernel based PCA-SVM model, it was found that the linear kernel model reached 92% accuracy with parameter c=0.003, the classification accuracy of the RBF kernel model was 94% with parameter c=0.125 and γ=256, and the polynomial model presented 92% accuracy with parameter c=0.003 and d=11. Meanwhile, the spectral classification accuracy of PLS-DA was 80%. The obtained results could pave a theoretical and experimental foundation for serum Raman spectroscopy-based breast cancer early screening and diagnosis.

Key words：Raman spectroscopy technology;Multivariate data classification models;Breast cancer;Serum;Carotene

Received: 2021-11-12 Accepted: 2022-04-25

ZTFLH:

O657.3

Corresponding Authors: ZHANG Bao-ping¹, NING Tian¹, ZHANG Fu-rong¹, CHEN Yi-shen¹, ZHANG Zhan-qin², WANG Shuang^1* E-mail: swang@nwu.edu.cn

Cite this article:

ZHANG Bao-ping,NING Tian,ZHANG Fu-rong, et al. Study on Raman Spectral Characteristics of Breast Cancer Based on Multivariable Spectral Data Analysis Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 426-434.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2023)02-0426-09 OR https://www.gpxygpfx.com/EN/Y2023/V43/I02/426

[1] Daniela Lazaro-Pacheco，Abeer M Shaaban，Shazza Rehman，et al. Applied Spectroscopy Reviews，2020，55（6）: 439.
[2] Siti NorbainiSabtu，Abdul Sani S F，Bradley D A，et al. Journal of Raman Spectroscopy，2019，51（3）: 380.
[3] SHEN Zhen-zhou，SHAO Zhi-min（沈镇宙，邵志敏）. The Recent Progress in Breast Tumor（现代乳腺肿瘤学进展）. Shanghai: Shanghai Science and Technology Literature Press（上海：上海科学技术文献出版社），2002. 56.
[4] Nargis H F，Nawaz H，Ditta A，et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy，2019，222: 117210.
[5] Morais C, Lima K, Singh M, et al. Nature Protocols, 2020, 15: 2143.
[6] Cameron J M, Bruno C, Parachalil D R, et al. Vibrational Spectroscopy in Protein Research, Academic Press, 2020. 269.
[7] Lin Duo，Pan Jianji，Huang Hao，et al. Scientific Reports，2014，4: 4751.
[8] Li Xianchang，Chen Hongjun，Zhang Shiding，et al. Journal of Biophotonics，2021，14（9）: e202100010.
[9] Wang Hong，Zhang Shaohong，Wan Limei，et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy，2018, 201: 34.
[10] Maria Bilal，Muhammad Bilal，Sobia Tabassum，et al. Applied Spectroscopy，2017，71（5）: 1004.
[11] Song Dongliang，Chen Yishen，Li Jie，et al. Journal of Biophotonics，2021，14（5）: e202000456.
[12] FENG Guo-he（奉国和）. Computer Engineering and Applications（计算机工程与应用），2011，47（3）: 123.
[13] JoséLuis González-Solís，Juan Carlos Martínez-Espinosa，Luis Adolfo Torres-Gonzalez. et al. Lasers in Medical Science，2014，29（3）: 979.
[14] Cheng Hong，Xu Chunlei，Zhang Di，et al. Photodiagnosis and Photodynamic Therapy，2020，30: 101735.
[15] Maryam Bahreini，Ahmad Hosseinzadegan，Arian Rashidi，et al. Talanta，2019，204: 826.
[16] Abramczyk H，Surmacki J，Brozek-Płuska B，et al. Journal of Molecular Structure，2009，924: 175.
[17] Tanmoy Bhattacharjee，Leticia C Fontana，Leandro Raniero，et al. Journal of Raman Spectroscopy，2018，49（5）: 786.
[18] Brozek-Płuska B，Placek I，Kurczewski K，et al. Journal of Molecular Liquids，2008，141（3）：145.
[19] Piotr S Gromski，Yun Xu，Elon Correa，et al. Analytica Chimica Acta，2014，829: 1.