Label-free surface-enhanced Raman spectroscopy for diagnosis and analysis of serum samples with different types lung cancer
[Display omitted] •Surface-enhanced Raman spectroscopy is useful for early detection of lung cancer.•PLS-DA was superior to PCA in the classification of lung cancer and normal subjects.•PLS-DA model could not completely separate the three types of lung cancer. Screening and detection of early lung c...
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| Published in | Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 261; p. 120021 |
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| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
15.11.2021
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| Subjects | |
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| Abstract | [Display omitted]
•Surface-enhanced Raman spectroscopy is useful for early detection of lung cancer.•PLS-DA was superior to PCA in the classification of lung cancer and normal subjects.•PLS-DA model could not completely separate the three types of lung cancer.
Screening and detection of early lung cancer is important for diagnosis and prognosis. Intervention in early stage of lung cancer can significantly improve the cure and survival of patients. Surface-enhanced Raman spectroscopy (SERS) is an increasingly popular method of diagnosing cancer. We used silver nanoparticles (AgNPs) as the Raman-enhanced substrate to increase Raman signals, which contributes to the subsequent classification of lung cancer and normal serum. SERS acquired from the serum indicated the difference in biochemical components between cancerous (n = 51) lung serum and normal (n = 18) serum. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were utilized to establish the identification model, and the various indicators of PLS-DA were all superior to those of the PLS model. Our study offers a new proposal for the universal applicability of analysis and identification with SERS of serum samples in clinical diagnosis. |
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| AbstractList | [Display omitted]
•Surface-enhanced Raman spectroscopy is useful for early detection of lung cancer.•PLS-DA was superior to PCA in the classification of lung cancer and normal subjects.•PLS-DA model could not completely separate the three types of lung cancer.
Screening and detection of early lung cancer is important for diagnosis and prognosis. Intervention in early stage of lung cancer can significantly improve the cure and survival of patients. Surface-enhanced Raman spectroscopy (SERS) is an increasingly popular method of diagnosing cancer. We used silver nanoparticles (AgNPs) as the Raman-enhanced substrate to increase Raman signals, which contributes to the subsequent classification of lung cancer and normal serum. SERS acquired from the serum indicated the difference in biochemical components between cancerous (n = 51) lung serum and normal (n = 18) serum. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were utilized to establish the identification model, and the various indicators of PLS-DA were all superior to those of the PLS model. Our study offers a new proposal for the universal applicability of analysis and identification with SERS of serum samples in clinical diagnosis. |
| ArticleNumber | 120021 |
| Author | Yang, Dafu Li, Rui Dai, ZhaoXia Wu, Shifa Pang, Lu Lei, Jia Zhang, Yi Zhang, Chenlei Yu, Zhanwu Liang, Shanshan |
| Author_xml | – sequence: 1 givenname: Jia surname: Lei fullname: Lei, Jia organization: School of Physics, Dalian University of Technology, Dalian, 116023, People’s Republic of China – sequence: 2 givenname: Dafu surname: Yang fullname: Yang, Dafu organization: The Second Department of Thoracic Medical Oncology, The Second Hospital of Dalian Medical University, Dalian, People’s Republic of China – sequence: 3 givenname: Rui orcidid: 0000-0003-0104-9386 surname: Li fullname: Li, Rui email: rli@dlut.edu.cn organization: School of Physics, Dalian University of Technology, Dalian, 116023, People’s Republic of China – sequence: 4 givenname: ZhaoXia orcidid: 0000-0003-0255-4800 surname: Dai fullname: Dai, ZhaoXia email: dzx21882003@163.com organization: The Second Department of Thoracic Medical Oncology, The Second Hospital of Dalian Medical University, Dalian, People’s Republic of China – sequence: 5 givenname: Chenlei surname: Zhang fullname: Zhang, Chenlei organization: Department of Thoracic Surgery, Liaoning Cancer Hospital & Institute, Cancer Hospital of Dalian University of Technology, Shenyang 110042, People’s Republic of China – sequence: 6 givenname: Zhanwu surname: Yu fullname: Yu, Zhanwu organization: Department of Thoracic Surgery, Liaoning Cancer Hospital & Institute, Cancer Hospital of Dalian University of Technology, Shenyang 110042, People’s Republic of China – sequence: 7 givenname: Shifa surname: Wu fullname: Wu, Shifa organization: School of Physics, Dalian University of Technology, Dalian, 116023, People’s Republic of China – sequence: 8 givenname: Lu surname: Pang fullname: Pang, Lu organization: School of Materials Science and Engineering, Dalian University of Technology, Dalian 116024, People’s Republic of China – sequence: 9 givenname: Shanshan surname: Liang fullname: Liang, Shanshan organization: The Key Laboratory of Biomarker High Throughput Screening and Target Translation of Breast and Gastrointestinal Tumor, Oncology Department, Affiliated Zhongshan Hospital of Dalian University, Dalian 116023, People’s Republic of China – sequence: 10 givenname: Yi surname: Zhang fullname: Zhang, Yi organization: School of Physics, Dalian University of Technology, Dalian, 116023, People’s Republic of China |
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